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After tooth loss, bone resorption is irreversible, leaving the area without adequate bone volume for successful implant treatment. Bone grafting is the only solution to reverse dental bone loss and is a well-accepted procedure required in one in every four dental implants. Research and development in materials, design and fabrication technologies have expanded over the years to achieve successful and long-lasting dental implants for tooth substitution. This review will critically present the various dental bone graft and substitute materials that have been used to achieve a successful dental implant. The article also reviews the properties of dental bone grafts and various dental bone substitutes that have been studied or are currently available commercially. The various classifications of bone grafts and substitutes, including natural and synthetic materials, are critically presented, and available commercial products in each category are discussed. Different bone substitute materials, including metals, ceramics, polymers, or their combinations, and their chemical, physical, and biocompatibility properties are explored. Limitations of the available materials are presented, and areas which require further research and development are highlighted. Tissue engineering hybrid constructions with enhanced bone regeneration ability, such as cell-based or growth factor-based bone substitutes, are discussed as an emerging area of development.

Water treatment public–private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies.

Backgrounds Diagnostic prediction is a central application that spans various medical specialties and scenarios, sequential diagnosis prediction is the process of predicting future diagnoses based on patients' historical visits. Prior research has underexplored the impact of irregular intervals between patient visits on predictive models, despite its significance. Method We developed the Multi-task Fusion Visit Interval for Sequential Diagnosis Prediction (MISDP) framework to address this research gap. The MISDP framework integrated sequential diagnosis prediction with visit interval prediction within a multi-task learning paradigm. It uses positional encoding and interval encoding to handle irregular patient visit intervals. Furthermore, it incorporates historical attention residue to enhance the multi-head self-attention mechanism, focusing on extracting long-term dependencies from clinical historical visits. Results The MISDP model exhibited superior performance across real-world healthcare dataset, irrespective of the training data scarcity or abundance. With only 20% training data, MISDP achieved a 4. 2% improvement over KAME; when training data ranged from 60 to 80%, MISDP surpassed SETOR, the top baseline, by 0. 8% in accuracy, underscoring its robustness and efficacy in sequential diagnosis prediction task. Conclusions The MISDP model significantly improves the accuracy of Sequential Diagnosis Prediction. The result highlights the advantage of multi-task learning in synergistically enhancing the performance of individual sub-task. Notably, irregular visit interval factors and historical attention residue has been particularly instrumental in refining the precision of sequential diagnosis prediction, suggesting a promising avenue for advancing clinical decision-making through data-driven modeling approaches.

Offshore wind farms play a pivotal role in the global transition to clean energy but remain susceptible to diverse meteorological hazards—ranging from highly variable wind speeds and temperature anomalies to severe oceanic disturbances—that can jeopardize both turbine safety and overall power output. Although Hidden Markov Models (HMMs) have a longstanding track record in operational forecasting, this study leverages and extends their capabilities by introducing a dynamic HMM framework tailored specifically for multi-risk offshore wind applications. Building upon historical datasets and expert assessments, the proposed model begins with initial transition and observation probabilities and then refines them adaptively through periodic or event-triggered recalibrations (e.g., Baum–Welch), thus capturing evolving weather patterns in near-real-time. Compared to static Markov chains, naive Bayes classifiers, and RNN (LSTM) baselines, our approach demonstrates notable accuracy gains, with improvements of up to 10% in severe weather conditions across three industrial-scale wind farms. Additionally, the model’s minutes-level computational overhead for parameter updates and state decoding proves feasible for real-time deployment, thereby supporting proactive scheduling and maintenance decisions. While this work focuses on the core dynamic HMM method, future expansions may incorporate hierarchical structures, Bayesian uncertainty quantification, and GAN-based synthetic data to further enhance robustness under high-dimensional measurements and rare, long-tail meteorological events. In sum, the multi-risk forecasting methodology presented here—though built on an established HMM concept—offers a practical, adaptive solution that significantly bolsters safety margins and operational reliability in offshore wind power systems.

While there are many studies in which body ownership can be transferred to a virtual body, there are few experimental studies of how subjects feel about their own bodies being deformed since a real body cannot be deformed. Here, we propose such an experimental setup, in which a twisted hand is diagonally viewed from behind, which is called a “monkey's hand.” Although the subject cannot see the thumb hidden behind his or her arm, he or she feels that the monkey's hand has an ambiguous thumb that functionally never exists but structurally exists. This ambiguity is consistent with experimental results on proprioceptive drift, by which the deformation of the hand is measured. The ambiguity of the presence and absence of the thumb is finally analyzed with a specific algebraic structure called a lattice. This can help us understand disownership as being different from the absence of ownership.

Let (M1,g) and (M2,h) be two Hermitian manifolds. The twisted product Hermitian manifold (M1×M2f,G) is the product manifold M1×M2 endowed with the Hermitian metric G=g+f2h, where f is a positive smooth function on M1×M2. In this paper, the Chern curvature, Chern Ricci curvature, Chern Ricci scalar curvature and holomorphic sectional curvature of the twisted product Hermitian manifold are derived. The necessary and sufficient conditions for the compact twisted product Hermitian manifold to have constant holomorphic sectional curvature are obtained. Under the condition that the logarithm of the twisted function is pluriharmonic, it is proved that the twisted product Hermitian manifold is Chern flat or Chern Ricci-flat, if and only if M1,g and M2,h are Chern flat or Chern Ricci-flat, respectively.

Climate change has increased the frequency of various types of meteorological disasters in recent years. Finding the primary factors that limit the emergency response capability of meteorological disasters through the evaluation of that capability and proposing corresponding improvement measures in order to increase that capability is of great practical importance. The evaluation of meteorological disaster emergency response capability still has some issues. The majority of research methods use qualitative analysis, which makes it challenging to deal with fuzzy factors, leading to conclusions that are subjective and insufficiently rigorous. The evaluation models themselves are also complex and challenging to simulate and analyze, making it challenging to promote and use them in practice. Deep learning techniques have made it easier to collect and process large amounts of data, which has opened new avenues for advancement in the emergency management of weather-related disasters. In this paper, we suggest a Recurrent Neural Network (RNN)-based dynamic capability feature extraction method. The process of evaluation content determination and index selection is used to build a meteorological disaster emergency response capability evaluation index system before an encoder, based on the encoder–decoder architecture, is built for dynamic feature extraction. The RNN autoencoder deep learning ability dynamic rating method used in this paper has been shown through a series of experiments to be able to not only efficiently extract ability features from time series data and reduce the dimensionality of ability features, but also to reduce the focus of the ability evaluation model on simple and abnormal samples, concentrate the model learning on difficult samples, and have a higher accuracy. As a result, it is more suitable for the problem situation at evaluation of the disaster capability.

Heavy metals and metalloids are common cooccurrence in contaminated soils, making their behaviors more complex than their individual presences. Adsorption to soil minerals and organic components determines the solubility and mobility of heavy metals. However, little information is available regarding coadsorbing metals (e.g., Cd) and metalloids (e.g., Sb) to soil components, and whether there is a universal coadsorption rule needs to be illuminated. This study investigated the coadsorption behaviors of Cd(II) and Sb(V) to goethite, kaolinite, and bacteria (Bacillus cereus) at both acidic (pH 4.5) and alkaline pH (pH 8.5). Equilibrium adsorption experiments, coupled with scanning electron microscopy- (SEM-) energy-dispersive X-ray spectrum (EDS) and X-ray photoelectron spectroscopy (XPS), were applied to determine the batch adsorption phenomena and possible mechanisms. Batch results showed that Cd(II) adsorption was greater at pH 8.5 whereas Sb(V) adsorption was greater at pH 4.5. The presence of Cd or Sb promoted each other’s adsorption to goethite, kaolinite, and bacteria, but slight differences were that Sb(V) preferred to enhance Cd(II) adsorption at acidic pH, whereas Cd(II) was more able to increase Sb(V) adsorption at alkaline pH. SEM-EDS analyses further showed that the distribution of Cd and Sb was colocalized. The surface FeOH, AlOH, and COOH groups participated in the binding of Cd(II) and Sb(V), probably through the formation of inner-sphere complexes. Two possible ternary complexes, i.e., sorbent-Cd2+-Sb(OH)6– and sorbent-Sb(OH)6–-Cd2+, were possibly formed. Both the charge effect and the formation of ternary complexes were responsible for the collaborative coadsorbing of Cd-Sb. The universal synergistic rule obtained suggests that current models for predicting Cd(II) or Sb(V) sequestration based on single systems may underestimate their solid-to-liquid distribution ratio in a coexistence situation. The results obtained have important implications for understanding the chemical behavior of Sb and Cd in contaminated soils.

Osteoclast (OC) abnormalities lead to many osteolytic diseases, such as osteoporosis, inflammatory bone erosion, and tumor-induced osteolysis. Exploring effective strategies to remediate OCs dysregulation is essential. FTY720, also known as fingolimod, has been approved for the treatment of multiple sclerosis and has anti-inflammatory and immunosuppressive effects. Here, we found that FTY720 inhibited osteoclastogenesis and OC function by inhibiting nuclear factor kappa-B (NF-κB) signaling. Interestingly, we also found that FTY720 inhibited osteoclastogenesis by upregulating histone deacetylase 4 (HDAC4) expression levels and downregulating activating transcription factor 4 (ATF4) expression levels. In vivo, FTY720 treatment prevented lipopolysaccharide- (LPS-) induced calvarial osteolysis and significantly reduced the number of tartrate-resistant acid phosphatase- (TRAP-) positive OCs. Taken together, these results demonstrate that FTY720 can inhibit osteoclastogenesis and ameliorate inflammation-induced bone loss. Which may provide evidence of a new therapeutic target for skeletal diseases caused by OC abnormalities.

Soft tissue generation, especially in large tissue, is a major challenge in reconstructive surgery to treat congenital deformities, posttraumatic repair, and cancer rehabilitation. The concern is along with the donor site morbidity, donor tissue shortage, and flap necrosis. Here, we report a dissection-free adipose tissue chamber–based novel guided adipose tissue regeneration strategy in a bioreactor of elastic gelatin cryogel and polydopamine-assisted platelet immobilization intended to improve angiogenesis and generate large adipose tissue in situ. In order to have matched tissue mechanics, we used 5% gelatin cryogel as growth substrate of bioreactor. Platelets from the platelet-rich plasma were then immobilized onto the gelatin cryogel with the aid of polydopamine to form a biomimetic bioreactor (polydopamine/gelatin cryogel/platelet). Platelets on the substrate led to a sustained high release in both platelet-derived growth factor and vascular endothelial growth factor compared with non-polydopamine-assisted group. The formed bioreactor was then transferred to a tissue engineering chamber and then inserted above inguinal fat pad of rats without flap dissection. This integrate strategy significantly boomed the vessel density, stimulated cellular proliferation, and upregulated macrophage infiltration. There was a noticeable rise in the expression of dual-angiogenic growth factors (platelet-derived growth factor and vascular endothelial growth factor) in chamber fluid; host cell migration and host fibrous protein secretion coordinated with gelatin cryogel degradation. The regenerated adipose tissue volume gained threefold larger than control group (p < 0.05) with less fibrosis tissue. These results indicate that a big well-vascularized three-dimensional mature adipose tissue can be regenerated using elastic gel, polydopamine, platelets, and small fat tissue.