Effectiveness of different intrusion modes of maxillary anterior teeth with mini-implants in clear aligner treatment: a three-dimensional finite element analysis.

BACKGROUND: The intrusion of maxillary anterior teeth is often required and there are various intrusion modes with mini-implants in clear aligner treatment. The objective of this study was to evaluate the effectiveness of maxillary anterior teeth intrusion with different intrusion modes, aiming to provide references for precise and safe intrusion movements in clinical practice. METHODS: Cone-beam computed tomography and intraoral optical scanning data of a patient were collected. Finite element models of the maxilla, maxillary dentition, periodontal ligaments (PDLs), clear aligner (CA), attachments, and mini-implants were established. Different intrusion modes of the maxillary anterior teeth were simulated by changing the mini-implant site (between central incisors, between central and lateral incisor, between lateral incisor and canine), loading site (between central incisors, on central incisor, between central and lateral incisor, between lateral incisor and canine), and loading mode (labial loading and labiolingual loading). Ten conditions were generated and intrusive forces of 100 g were applied totally. Then displacement tendency of the maxillary anterior teeth and CA, and stress of the PDLs were analyzed. RESULTS: For the central incisor under condition L14 and for the canine under conditions L11, L13, L23, and L33, the intrusion amount was negative. Under other conditions, the intrusion amount was positive. The labiolingual angulation of maxillary anterior teeth exhibited positive changes under all conditions, with greater changes under linguoincisal loading. The mesiodistal angulation of canine exhibited positive changes under labial loading, while negative changes under linguoincisal loading except for condition L14. CONCLUSIONS: The intrusion amount, labiolingual and mesiodistal angulations of the maxillary anterior teeth were affected by the mini-implant site, loading site, and loading mode. Labial and linguoincisal loading may have opposite effects on the intrusion amount of maxillary anterior teeth and the mesiodistal angulation of canine. The labiolingual angulation of the maxillary incisors would increase under all intrusion modes, with greater increases under linguoincisal loading.

Effect of seasonal variations in sea level on residual saltwater removal upstream of subsurface dam in coastal layered heterogeneity aquifers.

Subsurface dams have been recognized as one of the most effective measures for preventing saltwater intrusion. However, it may result in large amounts of residual saltwater being trapped upstream of the dam and take years to decades to remove, which may limit the utilization of fresh groundwater in coastal areas. In this study, field-scale numerical simulations were used to investigate the mechanisms of residual saltwater removal from a typical stratified aquifer, where an intermediate low-permeability layer (LPL) exists between two high-permeability layers, under the effect of seasonal sea level fluctuations. The study quantifies and compares the time of residual saltwater removal (Tre) for constant sea level (CSL) and seasonally varying sea level (FSL) scenarios. The modelling results indicate that, in most cases, seasonal fluctuations in sea level facilitate the dilution of residual saltwater and thus accelerate residual saltwater removal compared to a static sea level scenario. However, accounting for seasonal sea level variations may increase the required critical dam height (the minimum dam height required to achieve complete residual saltwater removal). Sensitivity analyses show that Tre decreases with increasing height of subsurface dam (Hd) under CSL or weaker sea level fluctuation scenarios; however, when the magnitude of sea level fluctuation is large, Tre changes non-monotonically with Hd. Tre decreases with increasing distance between subsurface dam and ocean for both CSL and FSL scenarios. We also found that stratification model had a significant effect on Tre. The increase in LPL thickness for both CSL and FSL scenarios leads to a decrease in Tre and critical dam height. Tre generally shows a non-monotonically decreasing trend as LPL elevation increases. These quantitative analyses provide valuable insights into the design of subsurface dams in complex situations.

Confidence for intrusion errors during the attentional blink depends on target-defining features.

Research surrounding the attentional blink phenomenon - a deficit in responding to the second of two temporally proximal stimuli when presented 150-500 ms after the first - has used a wide variety of target-defining and response features of stimuli. The typical U-shape curve for absolute performance is robust, surviving across most stimulus features, and therefore changes in performance are discussed as dynamics in an attentional system that are nonspecific a stimulus type. However, the patterns of errors participants make might not show the same robustness, and participants' confidences in these errors might differ - potentially suggesting the involvement of different attentional or perceptual mechanisms. The present research is a comparison of error patterns and confidence in those errors when letter target stimuli are defined by either the color of the letter, the presence of a surrounding annulus, or the color of the annulus. Across three experiments, we show that participants erroneously report stimuli that are further away from T2 and they are similarly confident in specifically their post-target errors as their correct responses when annuli define targets, but not when color of the letter defines targets. Experiment 3 provides some evidence to suggest that this error pattern and associated confidence is time-dependent when the color of the annulus defines the target, but not when the color of the letter defines the target. These results raise questions concerning the nature of the errors and possibly the mechanisms of the attentional blink phenomenon itself.

Evolving pathways towards water security in the Vietnamese Mekong Delta: An adaptive management perspective.

The interplay of climate change, upstream hydropower development, and local water engineering interventions for agricultural production contributes substantially to the transformation of waterscapes and water scarcity in the Vietnamese Mekong Delta. This paper aims to examine how these dynamics are linked to the paradigm shift in water management in An Giang and Ben Tre, the two ecologically distinct provinces that face serious water scarcity in the delta. We used the adaptive management concept to examine how state-led policy directions from food security towards water security enable change in water management that gives priority to water retention. While policy learning is evident, questions remain about how this ad-hoc solution could help address the presently acute water scarcity and water security over the long term. The paper advocates achieving water security should focus not only on diplomatic interventions into upstream climate-development complexities but also local water-livelihood politics.

An intrusion detection system based on convolution neural network.

With the rapid extensive development of the Internet, users not only enjoy great convenience but also face numerous serious security problems. The increasing frequency of data breaches has made it clear that the network security situation is becoming increasingly urgent. In the realm of cybersecurity, intrusion detection plays a pivotal role in monitoring network attacks. However, the efficacy of existing solutions in detecting such intrusions remains suboptimal, perpetuating the security crisis. To address this challenge, we propose a sparse autoencoder-Bayesian optimization-convolutional neural network (SA-BO-CNN) system based on convolutional neural network (CNN). Firstly, to tackle the issue of data imbalance, we employ the SMOTE resampling function during system construction. Secondly, we enhance the system's feature extraction capabilities by incorporating SA. Finally, we leverage BO in conjunction with CNN to enhance system accuracy. Additionally, a multi-round iteration approach is adopted to further refine detection accuracy. Experimental findings demonstrate an impressive system accuracy of 98.36%. Comparative analyses underscore the superior detection rate of the SA-BO-CNN system.

Marine microplastics enhance release of arsenic in coastal aquifer during seawater intrusion process.

Microplastics (MPs), omnipresent contaminants in the ocean, could be carried by seawater intrusion into coastal aquifers, which might affect the fate of heavy metals existing in aquifers. Herein, we investigated the release behavior of arsenic (As) in coastal aquifers during MPs-containing seawater intrusion by applying laboratory experiment and numerical simulation. We found that seawater with marine MPs enhanced the release of As in aquifers, especially for dissolved As(V) and colloidal As. Negatively charged MPs competed with As(V) for the adsorption sites on iron (hydr)oxides in aquifers, resulting in the desorption of As(V). In addition, MPs could promote the release of Fe-rich colloids by imparting negative charge to its surface and providing it with sufficient repulsive force to detach from the matrix, thereby leading to the release of As associated with Fe-rich colloid. We also developed a modeling approach that well described the transport of As in coastal aquifer under the impact of MPs, which coupled variable density flow and kinetically controlled colloids transport with multicomponent reactive transport model. Our findings elucidated the enhancement of MPs on the release of As in aquifers during seawater intrusion, which provides new insights into the risk assessment of MPs in coastal zones.

Drivers of barrier island water-table fluctuations and groundwater salinization.

Barrier islands are threatened by climate change as sea-level rise and higher frequency storm surge lead to more flooding and saltwater intrusion. Vegetation plays a vital role in preventing erosion of barrier islands due to aeolian and hydrological forces. However, vegetation on barrier islands is threatened by rising water tables causing hypoxic conditions and storm-surge overwash introducing saline water to the root zone. To better protect barrier island ecosystems, it is critical to identify the relative influence of different hydrological drivers on water table elevation and salinity, and understand how this influence varies spatially and temporally. In this study, three barrier island sites were instrumented with groundwater wells monitoring water level and specific conductance. Using these data, a set of transfer function noise models were calibrated and used to determine the relative influence of hydrologic drivers including precipitation, evapotranspiration, bay and ocean water levels, and wave height on groundwater levels and specific conductance. We found that drivers of water-level change and specific conductance vary strongly among sites, depending primarily on the surface water connectivity and the geology of the island. Sites with close connection to inlets showed more salinization and responded to a larger number of drivers, while sites that were poorly connected to the ocean responded to fewer drivers.

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach.

Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost's hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively.

Security Analysis for Smart Healthcare Systems.

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems.

This article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini's effectiveness within the domain of intrusion detection systems (IDS). Recognizing the need for improved feature interaction analysis methodologies in IDS, this research aims to investigate the performance of BukaGini in this context. BukaGini's performance is evaluated across four diverse datasets commonly used in IDS research: NSLKDD (22,544 samples), WUSTL EHMS (16,318 samples), WSN-DS (374,661 samples), and UNSWNB15 (175,341 samples), amounting to a total of 588,864 data samples. The evaluation encompasses key metrics such as stability score, accuracy, F1-score, recall, precision, and ROC AUC. Results indicate significant advancements in IDS performance, with BukaGini achieving remarkable accuracy rates of up to 99% and stability scores consistently surpassing 99% across all datasets. Additionally, BukaGini demonstrates an average reduction in dimensionality of 25%, selecting 10 features for each dataset using the Gini index. Through rigorous comparative analysis with existing methodologies, BukaGini emerges as a promising solution for feature interaction analysis within cybersecurity applications, particularly in the context of IDS. These findings highlight the potential of BukaGini to contribute to robust model performance and propel intrusion detection capabilities to new heights in real-world scenarios.

Sorption and attenuation of petroleum VOCs in five unsaturated soils: Microcosms and column experiments.

The fate of volatile organic compounds (VOC) vapors in the unsaturated zone is the basis for evaluating the natural attenuation potential and vapor intrusion risk. Microcosm and column experiments were conducted to study the effects chemical speciation and soil types/properties on the fate of petroleum VOCs in unsaturated zone. The biodegradation and total attenuation rates of the seven VOCs obtained by microcosm experiments in black soil and yellow earth were also generally higher than those in floodplain soil, lateritic red earth, and quartz sand. The VOC vapors in floodplain soil, lateritic red earth, and quartz sand showed slow total attenuation rates (<0.3 d-1). N-pentane, methylcyclopentane, and methylcyclohexane showed lower biodegradation rates than octane and three monoaromatic hydrocarbons. Volatilization into the atmosphere and biodegradation are two important natural attenuation paths for VOCs in unsaturated soil columns. The volatilization loss fractions of different volatile hydrocarbons in all five unsaturated soils were generally in the order: n-pentane (93.5%-97.8%) > methylcyclopentane (77.2%-85.5%) > methylcyclohexane (53.5%-69.2%) > benzene (17.1%-73.3%) > toluene (0-45.7%) > octane (1.9%-34.2%) > m-xylene (0-5.7%). The fractions by volatilization into the atmosphere of all seven hydrocarbons in quartz sand, lateritic red earth, and floodplain soil were close and higher compared to the yellow earth and black soil. Overall, this study illustrated the important roles chemical speciation and soil properties in determining the vapor-phase transport and natural attenuation of VOCs in the unsaturated zone.

Periodontal considerations during orthodontic intrusion and extrusion in healthy and reduced periodontium.

In patients with advanced periodontal disease, pathological tooth migration may occur, which may require subsequent orthodontic treatment for both aesthetic and functional purposes. When planning orthodontic treatment mechanics, intrusive or extrusive forces are frequently indicated. Understanding tissue reactions during these movements is essential for clinicians when devising a comprehensive orthodontic-periodontal treatment plan. This knowledge enables clinicians to be fully aware of and account for the potential effects on the surrounding tissues. The majority of our understanding regarding the behavior of periodontal tissues in both healthy and compromised periodontal conditions is derived from animal studies. These studies offer the advantage of conducting histological and other assessments that would not be feasible in human research. Human studies are nevertheless invaluable in being able to understand the clinically relevant response elicited by the periodontal tissues following orthodontic tooth movement. Animal and human data show that in dentitions with reduced periodontal support, orthodontic intrusion of the teeth does not induce periodontal damage, provided the periodontal tissues do not have inflammation and plaque control with excellent oral hygiene is maintained. On the contrary, when inflammation is not fully controlled, orthodontic intrusion may accelerate the progression of periodontal destruction, with bacterial plaque remnants being displaced subgingivally, leading to further loss of attachment. Orthodontic extrusion, on the other hand, does not seem to cause further periodontal breakdown in dentitions with reduced periodontal support, even in cases with deficient plaque control. This is attributed to the nature of the tooth movement, which directs any plaque remnants coronally (supragingivally), reducing the risk of adverse effects on the periodontal tissues. This specific type of tooth movement can be leveraged to benefit periodontal conditions by facilitating the regeneration of lost hard and soft periodontal tissues in a coronal direction. As a result, orthodontic extrusion can be employed in implant site development, offering an advantageous alternative to more invasive surgical procedures like bone grafting. Regardless of the tooth movement prescribed, when periodontal involvement is present, it is essential to prioritize periodontal therapy before commencing orthodontic treatment. Adequate plaque control is also imperative for successful outcomes. Additionally, utilizing light orthodontic forces is advisable to achieve efficient tooth movement while minimizing the risk of adverse effects, notably root resorption. By adhering to these principles, a more favorable and effective combined orthodontic-periodontal approach can be ensured. The present article describes indications, mechanisms, side effects, and histological and clinical evidence supporting orthodontic extrusion and intrusion in intact and reduced periodontal conditions.

Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

Characterization of groundwater salinity by hydrogeochemical and multivariate statistical analysis in the coastal aquifer of Nagapattinam district, Southern India.

The impact of seawater intrusion from coast to inland terrain in the Cauvery River Basin (CRB) and Uppanar River Basin (URB) was evaluated based on major ion groundwater chemistry. TDS ranges from 229 to 2260 mg/l, and 408 to 3732 mg/l; Na+ range from 67 to 560 mg/l, and 74 to 1600 mg/l, and Cl- range from 120 to 906 mg/l, and 110 to 3260 mg/l for CRB and URB respectively. Piper Diagram, Hydrochemical Facies Evolution Diagram (HFE-D), rock-water interaction (Gibbs Plots), various bivariate plots viz., TDS vs. Cl-; Na+ vs. Cl-; Ca2+ vs. Cl-; Ca2+ vs. SO4 2-; TH vs. TDS and Principal Component Analysis (PCA) (Cluster and Factor analysis) were used to identify the seawater intrusion from coast to inland aquifers and to understand hydrogeochemical characterization and salinization processes. Piper diagram shows that most of the samples are Na+-Cl- type, HFE-D diagram also shows that most of the samples were saline intrusion type and mixing behavior, while TH vs. TDS plot shows hard fresh to hard brackish type from both the basins. PCA results clearly show the three factors, explaining 84.02 % and 76.67 % variance in URB and CRB. Factor-1 records 53.03 % alteration, with a strong confidence loading of TDS, Na+, Cl-, Ca2+, K+, SO4 2, Total Alkalinity (TA), and Total Hardness (TH) in URB indicating saline nature. A total variance of 46.23 % in CBR is more positively loaded with TH, Mg2+, Ca2+, and SO4 2- indicating rock-water interaction. Cluster analyses of these parameters illustrate the cluster distribution in CRB and URB. In URB, TDS, Na+, and Cl- ions make a cluster with a linkage distance of 5000 m, whereas in CRB, the TDS, Na+, Cl-, and TA ions make a cluster with a linkage distance of 2800 m. The factor and cluster analysis fetched out an effect of intensive use of fertilizers, aquaculture activities, and excessive groundwater exploitation. This technique gave the relationship between various chemical parameters in groundwater. Factor and cluster analysis have proven highly effective in groundwater quality studies. The study concluded that the study area has the threat of saline water intrusion in shallow aquifers with continuous influences of seawater mixing.

Unravelling the signatures of submarine groundwater discharge and seawater intrusion along the coastal plains of Odisha, India: a multi-proxy approach.

Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) are two contrary hydrological processes that occur across the land-sea continuum and understanding their nature is essential for management and development of coastal groundwater resource. Present study has attempted to demarcate probable zones of SGD and SWI along highly populated Odisha coastal plains which is water stressed due to indiscriminate-exploitation of groundwater leading to salinization and fresh groundwater loss from the alluvial aquifers. A multi-proxy investigation approach including decadal groundwater level dynamics, LANDSAT derived sea surface temperature (SST) anomalies and in-situ physicochemical analysis (pH, EC, TDS, salinity and temperature) of porewater, groundwater and seawater were used to locate the SGD and SWI sites. A total of 340 samples for four seasons (85 samples i.e., 30 porewater, 30 seawater and 25 groundwater in each season) were collected and their in-situ parameters were measured at every 1-2 km gap along ~ 145 km coastline of central Odisha (excluding the estuarine region). Considering high groundwater EC values (> 3000 µS/cm), three probable SWI and low porewater salinities (< 32 ppt in pre- and < 25 ppt in post-monsoons), four probable SGD zones were identified. The identified zones were validated with observed high positive hydraulic gradient (> 10 m) at SGD and negative hydraulic gradient (< 0 m) at SWI sites along with anomalous SST (colder in pre- and warmer in post-monsoon) near probable SGD locations. This study is first of its kind along the Odisha coast and may act as initial basis for subsequent investigations on fresh-saline interaction along the coastal plains where environmental integrity supports the livelihood of coastal communities and the ecosystem.

Intrusion detection in machine learning based E-shaped structure with algorithms, strategies and applications in wireless sensor networks.

In the everyday world of computer applications, from the cloud to the Internet of Things, distributed sensor networks are essential (IoT). These computer application devices are often connected to Arduino network connection and microcontrollers such sensors and actuators. Thus, a defensive network with an IDS serves as the need for contemporary networks. The intrusion detection system has unavoidably evolved throughout the years, but despite this, it remains a difficult study topic since the current intrusion detection system uses signature-based approaches rather than anomaly detection. Therefore, improving the current intrusion detection system is challenging since it is difficult to find zero-day attacks in IoT networks when dealing with varied data sources. Filtered Deep Learning Model for Intrusion Detection with a Data Communication Approach is presented in this study. The five steps that make up the suggested model are Initialization of Sensor Networks, Cluster Formation and Head Selection, Connectivity, Attack Detection, and Data Broker. It was discovered that the suggested model for intrusion detection outperformed both the current Deep Learning Neural Net and Artificial Neural Network. In comparison to the most popular algorithms, experimental findings revealed a superior result of 96.12 % accuracy. The E-shaped patch antenna is a brand-new single-patch wide-band microstrip antenna that is presented in this research. A microstrip antenna's patch has two parallel slots built into it to increase its bandwidth. Investigating the behaviour of the currents on the patch allows for the exploration of the wide-band mechanism. A broad bandwidth is achieved by optimising the slot's length, breadth, and location. Finally, a 40.3 % E-shaped patch antenna is developed, made, and tested to resonate at 7.5 and 8.5 GHz for wireless communications. Additionally displayed are the reflection coefficient, VSWR, radiation pattern and directivity.

Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset.

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks-blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.

Three-Dimensional Evaluation of Treatment Effects and Post-Treatment Stability of Maxillary Molar Intrusion Using Temporary Anchorage Devices in Open Bite Malocclusion.

Background: We investigated treatment outcomes and post-treatment stability in 10 patients with an anterior open bite and nonsurgical orthodontics. Methods: The patients underwent maxillary molar intrusion using temporary anchorage devices (TADs) to deepen the overbite due to mandibular autorotation. Lateral cephalograms and dental cast models were obtained before treatment (T0), immediately after it (T1), and >1 year after it (T2). Skeletal and dental cephalometric changes and three-dimensional movements of the maxillary dentitions were evaluated. Results: At T0, cephalometric analysis indicated that patients had skeletal class I with tendencies for a class II jaw relationship and a skeletal open bite. During active treatment (T0 to T1), the maxillary first molar intruded by 1.6 mm, the mandibular first molar extruded by 0.3 mm, the Frankfort-mandibular plane angle decreased by 1.1°, and the overbite increased by 4.1 mm. Statistically significant changes were observed in the amount of vertical movement of the maxillary first molar, Frankfort-mandibular plane angle, and overbite. Three-dimensional (3D) dental cast analysis revealed that the maxillary first and second molars intruded, whereas the anterior teeth extruded, with the second premolar as an infection point. In addition, the maxillary molar was tipped distally by 2.9° and rotated distally by 0.91°. Statistically significant changes were observed in the amount of vertical movement of the central incisor, lateral incisor, canine and first molar, and molar angulation. From T1 to T2, no significant changes in cephalometric measurements or the 3D position of the maxillary dentition were observed. The maxillary and mandibular dentitions did not significantly change during post-treatment follow-up. Conclusions: Maxillary molar intrusion using mini-screws is an effective treatment for open bite correction, with the achieved occlusion demonstrating 3D stability at least 1 year after treatment.

Vertical Control in Molar Distalization by Clear Aligners: A Systematic Review and Meta-Analysis.

Background: Molar distalization is used to correct molar relationships or to create space for mild anterior crowding. However, whether clear aligners can provide proper vertical control with the sequential distalization strategy has been highly debated. Thus, the current study aimed to systematically review the amount of dentoskeletal changes in the vertical dimension that results from sequential molar distalization in clear aligner therapy without temporary anchorage devices (TADs). Methods: Registered with PROSPERO (CRD42023447211), relevant original studies were screened from seven databases and supplemented by a manual search by two investigators independently. Articles were screened against inclusion and exclusion criteria, and a risk of bias assessment was conducted for each included article. Relevant data were extracted from the included articles and meta-analysis was performed using RStudio. Results: Eleven articles (nine for maxillary distalization and two for mandibular distalization) were selected for the final review. All studies have a high or medium risk of bias. For maxillary molar distalization, the meta-analysis revealed 0.26 mm [0.23 mm, 0.29 mm] of maxillary first molar intrusion based on post-distalization dental model analysis, as well as 0.50 mm [-0.78 mm, 1.78 mm] of maxillary first molar intrusion and 0.60 mm [-0.42 mm, 1.62 mm] of maxillary second molar intrusion based on post-treatment lateral cephalometric analysis. Skeletally, there was a -0.33° [-0.67°, 0.02°] change in the SN-GoGn angle, -0.23° [-0.30°, 0.75°] change in the SN-MP angle, and 0.09° [-0.83°, 1.01°] change in the PP-GoGn angle based on post-treatment lateral cephalometric analysis. There was insufficient data for meta-analysis for mandibular molar distalization. Conclusions: No significant changes in vertical dimension were observed, both dentally and skeletally, after maxillary molar distalization with a sequential distalization strategy. However, further studies on this topic are needed due to the high risk of bias in the currently available studies.

Tides of Blockchain in IoT Cybersecurity.

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.