Search Results (115)

Background: Wedge-shaped implants have been proposed as a minimally invasive solution for narrow alveolar ridges, aiming to avoid bone augmentation. While the short-term results are promising, long-term clinical evidence remains limited. Methods: This multicenter prospective single-arm cohort study reports the 7-year outcomes of tissue-level wedge-shaped implants (1.8 mm thickness) placed without grafting in horizontally atrophic ridges (mean thickness 3.73 ± 0.36 mm). Clinical and radiographic evaluations were performed on 45 implants (34 patients). Results: At the 7-year post-loading follow-up, the implant survival rate was 95.5%, with two failures recorded—one early loss and one due to peri-implantitis. Peri-implant mucositis was observed in 5 implants (11.4%), while peri-implantitis was diagnosed in 3 implants (6.8%). No mechanical complications were reported. The mean marginal bone loss (MBL) was 1.45 ± 1.41 mm, measured relative to the implant shoulder. Multivariate linear regression identified older age (β = +0.040; p = 0.012) and mandibular implant placement (β = +1.39; p = 0.007) as significant predictors of greater bone loss. Conclusions: Wedge-shaped implants demonstrated high long-term survival and stable marginal bone levels in narrow ridges without the need for bone augmentation. Age and mandibular location negatively influenced long-term bone stability, while smoking, gender, and history of periodontitis were not significant predictors. Full article

Background. Orthodontic extrusion (OE), or forced eruption, is a conservative technique used to recover teeth affected by coronal fractures, traumatic intrusions, or severe caries. It involves applying light, continuous forces to induce vertical tooth movement, promoting tissue remodeling through periodontal ligament stimulation. Materials and Methods. This narrative review included studies investigating OE as a therapeutic approach for the management of deep or subgingival carious lesions, traumatic dental injuries (such as intrusion or fracture), or for alveolar ridge augmentation in implant site development. OE is typically performed using fixed appliances such as the straight-wire system or, in selected cases, clear aligners. Forces between 30 and 100 g per tooth are applied, depending on the clinical situation. In some protocols, OE is combined with fiberotomy to minimize gingival and bone migration. Results. Studies show that OE leads to significant vertical movement and increases in buccal bone height and interproximal septa. It enhances bone volume in targeted sites, making it valuable in implant site development. Compared to surgical crown lengthening, OE better preserves periodontal tissues and improves esthetics. Conclusions. In this narrative review is analized how OE is effective for managing traumatic intrusions and compromised periodontal sites, particularly when paired with early endodontic treatment. It reduces the risks of ankylosis and root resorption while avoiding invasive procedures like grafting. Although clear aligners may limit axial tooth movement, OE remains a minimally invasive, cost-effective alternative in both restorative and implant dentistry. Full article

Space–time adaptive processing (STAP) based on sparse recovery (SR-STAP) has demonstrated remarkable clutter suppression performance under insufficient sample conditions. However, the main aim of sparse recovery is to solve the norm minimization problem. To this end, this study proposes a weighted STAP algorithm based on a greedy block coordinate descent method to address the problems of slow convergence speed and insufficient estimation accuracy in the existing l_2,1-norm minimization methods. First, the weights are estimated using the multiple signal classification (MUSIC) algorithm. Then, a greedy block selection rule that favors sparsity is used, prioritizing the update of the weighted block that has the greatest impact on sparsity. Although the proposed algorithm in this paper is greedy in nature, it is globally convergent. Finally, the accuracy of clutter covariance matrix estimation and the convergence speed of the SR-STAP algorithm are enhanced by reasonably estimating the noise power and selecting appropriate regularization parameters. The results of simulation experiments indicate that the proposed algorithm can effectively suppress clutter ridge expansion, achieving excellent clutter suppression and target detection performance compared with the existing methods, as well as satisfactory convergence properties. Full article

The accurate assessment of soil fertility is critical for guiding nutrient management and promoting sustainable agriculture in semi-arid agroecosystems. In this study, a machine learning-based Soil Fertility Index (SFI) model was developed using regularized regression techniques to evaluate fertility across a dryland maize-growing region in southeastern Türkiye. A total of 64 composite soil samples were collected from the Batman Plain, characterized by alkaline and salinity-prone conditions. Five soil chemical indicators, electrical conductivity (EC), pH, organic matter (OM), zinc (Zn), and iron (Fe), were selected for SFI estimation using a standardized rating approach. The dataset was randomly split into training (80%) and test (20%) subsets to calibrate and validate the models. Ridge, Lasso, and Elastic Net regression models were employed to predict SFI and assess variable importance. Among these, the Lasso model achieved the highest predictive accuracy on test data (R² = 0.746, RMSE = 0.060), retaining only EC and Zn as significant predictors. Ridge and Elastic Net captured OM and pH, though their contributions were minimal (|β| < 0.01). Spatial predictions showed moderate alignment with observed SFI values (range: 0.48–0.76), but all models underestimated high-fertility zones (>0.69), likely due to coefficient shrinkage. Despite its simplicity, the Lasso model offered superior interpretability and spatial resolution. The results reveal the potential of interpretable machine learning for supporting sustainable, site-specific fertility assessment and informed nutrient management in data-scarce and environmentally vulnerable regions. Full article

The development of compact, CMOS-compatible gas sensors is critical for advancing real-time environmental monitoring and industrial diagnostics. In this study, we present a detailed numerical investigation of integrated photonic waveguide designs—such as ridge and slot—optimized for overtone-based gas spectroscopy in the near-infrared range. By evaluating both the evanescent-field confinement and curvature-induced losses across multiple silicon-on-insulator platforms, we identify optimal geometries that maximize light–analyte interactions while minimizing bending attenuation. Our findings provide essential design guidelines for high-performance, low-footprint gas sensors. Full article

Predictive regression models often face a common challenge known as multicollinearity. This phenomenon can distort the results, causing models to overfit and produce unreliable coefficient estimates. Ridge regression is a widely used approach that incorporates a regularization term to stabilize parameter estimates and improve the prediction accuracy. In this study, we introduce four newly modified ridge estimators, referred to as RIRE1, RIRE2, RIRE3, and RIRE4, that are aimed at tackling severe multicollinearity more effectively than ordinary least squares (OLS) and other existing estimators under both normal and non-normal error distributions. The ridge estimators are biased, so their efficiency cannot be judged by variance alone; instead, we use the mean squared error (MSE) to compare their performance. Each new estimator depends on two shrinkage parameters, $k$ and $d$, making the theoretical analysis complex. To address this, we employ Monte Carlo simulations to rigorously evaluate and compare these new estimators with OLS and other existing ridge estimators. Our simulations show that the proposed estimators consistently minimize the MSE better than OLS and other ridge estimators, particularly in datasets with strong multicollinearity and large error variances. We further validate their practical value through applications using two real-world datasets, demonstrating both their robustness and theoretical alignment. Full article

The reconstruction of a severely resorbed alveolar bone is a significant challenge in dental implantology and maxillofacial surgery. Traditional bone grafting materials, including autogenous, allogeneic, xenogeneic, and alloplastic materials, have limitations such as donor site morbidity, limited availability, and prolonged maturation periods. To address these challenges, recombinant human bone morphogenetic protein-2 (rhBMP-2) has emerged as a potent osteoinductive factor that facilitates bone regeneration without the need for additional donor site surgery. This study introduces a box technique which combines rhBMP-2 (CowellBMP^®, Cowellmedi, Busan, Republic of Korea) with a 3D-preformed titanium mesh (3D-PFTM), utilizing a mixture of allografts and xenografts for horizontal and vertical alveolar ridge augmentation. The technique leverages the structural stability provided by the OssBuilder^® (Osstem, Seoul, Republic of Korea), a preformed titanium mesh, that allows for simultaneous implant placement and vertical ridge augmentation. This technique not only reduces the treatment time compared to traditional methods but also minimizes post-operative discomfort by eliminating the need for autogenous bone harvesting. Clinical outcomes from this technique demonstrate successful bone regeneration within a shorter period than previously reported techniques, with excellent bone quality and implant stability being observed just four months after vertical augmentation. In conclusion, the so called BOXAM (BMP-2, Oss-builder, Xenograft, Allograft, Maintenance) technique presents a promising therapeutic strategy for alveolar bone reconstruction, particularly in cases of severe bone resorption. Further studies are needed to evaluate the long-term outcomes and potential limitations of this approach, especially in scenarios where the inferior alveolar nerve proximity poses challenges for fixture placement. Full article

As a promising nuclear technology, molten salt reactors (MSRs) have a bright future in the energy sector due to their unique advantages such as high efficiency, safety, and fuel flexibility. However, the accurate analysis of fission products in MSRs requires reliable fission yield data. Current reactor burnup analysis often uses the ORIGEN2 code, whose fission yield libraries mainly originate from the outdated 1970s ENDF/B-VI nuclear database, thus risking data obsolescence. This study evaluates ORIGEN2’s fission yield libraries (THERMAL, PWRU, PWRU50) against the modern ENDF/B-VIII.0 library. Through a comprehensive comparative analysis of Oak Ridge National Laboratory’s Molten Salt Reactor Experiment (MSRE) model, numerical simulations reveal library-dependent differences in MSR burnup characteristics. The PWRU library best matches ENDF/B-VIII.0 for U-235-fueled cases in keff results, while the PWRU50 library has minimal keff deviation in U-233-fueled setups. Moreover, in both fuel cases, the fission yield library was found to significantly affect the activity of key radionuclides, including Kr-85, Kr-85m, I-133m, Cs-136, Sn-123, Sn-125, Sn-127, Sb-124, Sb-125, Cd-115m, Te-125m, Te-129m, etc. Additionally, the fission gas decay heat power calculated via the ORIGEN2 library is over 20% lower than that from the ENDF/B-VIII.0 library tens of days after shutdown, mainly due to differences in long-lived Kr-85 production. These findings highlight the need to update traditional fission yield libraries in burnup codes. For next-generation MSR designs, this is crucial to ensure accurate safety assessments and the effective development of this promising energy technology. Full article

Background: Stereolithography is a rapid prototyping and 3D printing technique that creates solid three-dimensional models. An accurate and functional 3D model using stereolithography is invaluable in scientific research, particularly in studies involving edentulous patients. Additive manufacture and CAD systems help achieve accurate measurements and procedures and be easily replicated by lowering human error mistakes. The main objective of this study was to develop an in vitro simulation model with a reduced alveolar ridge with the same characteristics as mandibular edentulous patients using stereolithography. Methods: A mandibular model with a resorbed mandibular crest was scanned, and the STL model was aligned to the XYZ reference system. A reduction in the alveolar ridge corresponding to the mandibular mucosa of an edentulous patient was achieved. A negative model also derived from the original model was made to ensure the space for oral simulation material. A dimensional stability test was performed to validate the model. Results: The maximal mean displacement of the model was 0.015 mm, and the minimal mean displacement was 0.004 mm. The oral mucosa had a displacement of approximately 1.6 mm. Conclusions: An in vitro 3D simulation model of a complete edentulous patient mucosa was achieved. Full article

The Earth’s degassing is an important factor in evaluating global carbon budget estimates and understanding the carbon cycle. As a result, numerous studies have focused on this topic. However, current estimates predominantly focus on subaerial CO₂ emissions and CO₂ deep submarine emissions, particularly along mid-ocean ridges (MORs), whereas very few and only spatially limited estimates of shallow submarine CO₂ emissions have been reported, despite being widespread features of the seafloor. This study reports the results of measuring the dissolved CO₂ concentrations in shallow submarine environments along the coast of Vulcano Island (Aeolian Islands, Italy). For the areas exhibiting the highest concentrations, we calculated the amount of diffuse degassing by computing the sea–air CO₂ flux. The results revealed extremely high dissolved CO₂ concentrations, reaching up to 24 vol.% in areas with visible hydrothermal activity, including one location far from the island’s main crater. Notably, elevated CO₂ levels were also detected in areas with minimal or no apparent hydrothermal discharge, indicating the occurrence of diffuse degassing processes in these areas. In addition, the calculated diffuse degassing flux was comparable in magnitude to the CO₂ flux directly emitted into the atmosphere from the island’s main bubbling pools. Full article

Microbial functional genes serve as the core genetic foundation driving microbial ecological functions; however, its microbial functional gene composition across varied habitats and its ecological adaptation interplay with plants remain understudied. In this study, we investigated the P. tabuliformis rhizosphere microbial functional genes which are related to N and P cycles across ridge and slope habitats between different elevational gradients, analyzed their composition and abundance, and analyzed their responses to environmental factors. Results showed that slope habitats had a significantly greater abundance of N and P cycling functional genes compared to those of ridge counterparts (p < 0.05). Specifically, slope environments showed an enhanced gene abundance associated with denitrification, nitrogen fixation, nitrification, assimilatory/dissimilatory nitrate reduction, and nitrogen transport processes, along with the superior expression of genes related to inorganic/organic phosphorus metabolism, phosphorus transport, and regulatory gene expression. These nutrient cycling gene levels were positively correlated with soil nutrient availability. Our findings revealed distinct ecological strategies: Ridge communities employ resource-conservative tactics, minimizing microbial investments to endure nutrient scarcity, whereas slope populations adopt competitive strategies through enriched high-efficiency metabolic genes and symbiotic microbial recruitment to withstand resource competition. Full article

Statistical optimization methods serve as fundamental tools for studying sea-ice-related phenomena in the polar regions. To comprehensively analyze the spatial distributions of ice ridge keels, including the draft and spacing distributions, a statistical optimization model was developed with the aim of determining the optimal keel cutoff draft, which differentiates ice ridge keels from sea ice bottom roughness. By treating the keel cutoff draft as the identified variable and minimizing the relative errors between the theoretical and measured keel spatial distributions, the developed model aimed to seek the optimal keel cutoff draft and provide a precise method for this differentiation and to explore the impact of the ridging intensity, defined as the ratio of the mean ridge sail height to spacing, on the spatial distributions of the ice ridge keels. The utilized data were obtained from observations of sea ice bottom undulations in the Northwestern Weddell Sea during the winter of 2006; these observations were conducted using helicopter-borne electromagnetic induction (EM-bird). Through rigorous analysis, the optimal keel cutoff draft was determined to be 3.8 m, and this value was subsequently employed to effectively differentiate ridge keels from other roughness features on the sea ice bottom. Then, building upon our previous research that clustered measured profiles into three distinct regimes (Region 1, Region 2, and Region 3, respectively), a detailed statistical analysis was carried out to evaluate the influence of the ridging intensity on the spatial distributions of the ice ridge keels for all three regimes. Notably, the results closely matched the predictions of the statistical optimization model: Wadhams’80 function (a negative exponential function) exhibited an excellent fit with the measured distributions of the keel draft, and a lognormal function proved to effectively describe the keel spacing distributions in all three regimes. Furthermore, it was discovered that the relationship between the mean ridge keel draft and frequency (number of keels per kilometer) could be accurately modeled by a logarithmic function with a correlation coefficient of 0.698, despite considerable data scatter. This study yields several significant results with far-reaching implications. The determination of the optimal keel cutoff draft and the successful modeling of the relationship between the keel draft and frequency represent key achievements. These findings provide a solid theoretical foundation for analyzing the correlations between the morphologies of the sea ice surface and bottom. Such theoretical insights are crucial for improving remote sensing algorithms for ice thickness inversion from satellite elevation data, enhancing the accuracy of sea ice thickness estimations. Full article

This work presents a novel dual-polarized antenna array tailored for Internet of Things (IoT) applications, specifically designed to operate in the millimeter-wave (mm-wave) spectrum within the frequency range of 30–60 GHz. Leveraging printed ridge gap waveguide (PRGW) technology, the antenna ensures robust performance by eliminating parasitic radiation from the feed network, thus significantly enhancing the reliability and efficiency required by IoT communication systems, particularly for smart cities, autonomous vehicles, and high-speed sensor networks. The proposed antenna achieves superior radiation characteristics through a cross-shaped magneto-electric (ME) dipole backed by an artificial magnetic conductor (AMC) cavity and electromagnetic bandgap (EBG) structures. These features suppress surface waves, reduce edge diffraction, and minimize back-lobe emissions, enabling stable, high-quality IoT connectivity. The antenna demonstrates a wide impedance bandwidth of 24% centered at 30 GHz and exceptional isolation exceeding 40 dB, ensuring interference-free dual-polarized operation crucial for densely populated IoT environments. Fabrication and testing validate the design, consistently achieving a gain of approximately 13.88 dBi across the operational bandwidth. The antenna’s performance effectively addresses the critical requirements of emerging IoT systems, including ultra-high data throughput, reduced latency, and robust wireless connectivity, essential for real-time applications such as healthcare monitoring, vehicular communication, and smart infrastructure. Full article

Background: The autogenous bone core block (BCB) is a viable, biologically advantageous, and minimally invasive alternative to other augmentation procedures for small bone defects around dental implants. This study focused specifically on horizontal vestibular defects in the mandible, a frequently encountered yet underrepresented clinical situation, to evaluate the effectiveness and predictability of bone core grafting. Methods: Cylindrical autogenous bone cores, harvested from the implant-site osteotomy using trephine drills with a 2.5 internal diameter, were stabilized with osteosynthesis screws, and implants were placed simultaneously. Initial preoperative measurements of the edentulous ridge width were performed based on cone beam computer tomography (CBCT). At 4 months postoperatively, a subsequent CBCT measurement was performed for each implant site. Results: A total of 38 augmentation procedures were analyzed with a mean horizontal bone gain of 1.8 mm (p = 0.000). Improved outcomes were observed in V-shaped defects with remaining vertical bony walls, which contributed to better graft stability and volume preservation. While Khoury et al. previously validated the general applicability of this technique across various defect types, our study refines its indication by offering a clear protocol tailored to a common clinical niche. Conclusions: The proposed BCB method proved to be a safe, efficient, and with reduced morbidity procedure, providing clinicians with a practical and evidence-based tool for predictable horizontal bone augmentation. Full article

This study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material’s density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with $R^2$ values that exceeded 0.95 and minimal root-mean-square errors. The results reveal that the temperature and pressure significantly influenced the thermodynamic properties of the a-Si, while the cooling rate had a minor effect. The models generated isobaric and isothermal curves, which offered deeper insights into the thermodynamic behavior of the a-Si and complemented traditional MD simulations by providing a more efficient means to explore thermodynamic states. This work highlights the potential of machine learning to accelerate the study of materials by enabling faster exploration of thermodynamic behavior and the generation of additional data. This approach enhances the understanding of the equation of state of a-Si and opens new avenues for applying this hybrid modeling technique to other materials. Full article