An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks.

Smart cities have developed advanced technology that improves people's lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

A robust decision-making approach for designing coastal groundwater quality monitoring networks.

This paper presents a new approach to the spatiotemporal design of groundwater quality monitoring networks for coastal aquifers. A fusion model combines the outputs of several developed simulation models to make estimates more accurate. A modified GALDIT method is used to incorporate the aquifer vulnerability to saltwater intrusion. The value of information (VOI) theory is applied to determine sufficient monitoring wells. The groundwater quality monitoring network is designed by employing a robust decision-making (RDM) approach under different management strategies and economic considerations. This approach incorporates the deep uncertainties of some critical variables, including water level and total dissolved solids (TDS) concentration at the coastline and pumping flow rates of agricultural wells. The new methodology is implemented in the coastal Qom-Kahak aquifer, Iran. The results illustrate that the combination model has significantly improved evaluation criteria compared to individual prediction models. The fusion model results indicate that thirty monitoring wells would be ideal. The RDM-based analyses in the Qom-Kahak aquifer showed that an optimal network with 30 monitoring wells outperforms the current network regarding various criteria, such as VOI and variance of estimation error. The new well configuration also demonstrates a suitable spatial distribution. Given that the current sampling frequencies are unsuitable for areas with varying vulnerabilities, we recommend sampling every 3 months in areas with moderate vulnerabilities and once every three seasons in areas with low vulnerabilities, based on the information transfer index. Finally, a management strategy in which the pumping rate should be less than 60% of the current rate is suggested to prevent saltwater intrusion into the aquifer.

High salinity restrains microplastic transport and increases the risk of pollution in coastal wetlands.

Microplastics (MPs) pollution in coastal wetlands has attracted global attention. However, few studies have focused on the effect of soil properties and structure on MP transport in coastal wetlands. Salinity is one of the most pivotal environmental factors and varies in coastal wetlands. Here, we conducted column experiments and employed fluorescent labeling combined with Derjaguin-Landau-Verwey-Overbeek (DLVO) theoretical calculations to reveal the vertical transport behavior of MPs. Specifically, we investigated the influence of five salinity levels (0, 0.035, 0.35, 3.5, and 35 PSU) on MP transport in different coastal wetlands soils and a sand through the X-ray photoelectron spectroscopy and nondestructive computed tomography technique. The results indicated that the migration capability of MPs in soils is significantly lower than in quartz sand, and that the migration capability varies depending on the soil type. This variability may be due to soil minerals and microporous structures providing numerous attachment sites for MPs and may be explained by the DLVO energy barrier of MP-Soil (6568-7767 KT) and MP-sand (5250 KT). Salinity plays a crucial role in modifying the chemical properties of pore water (i.e., zeta potential) as well as altering the soil elemental composition and pore structure. At 0 PSU, the maximum C/C0 of MPs through the sand, Soil 1, and Soil 2 transport columns were 37.86 ± 2.36 %, 23.96 ± 1.71 %, and 3.94 ± 0.68 %, respectively. When salinity increased to 3.5 PSU, MP mobility decreased by over 20 %. Additionally, a salinity of 35 PSU may alter the soil pore distribution, thereby changing water flow paths and velocities to constrain the migration of MPs in soils. These findings could provide valuable insights into understanding the environmental behavior and transport mechanisms of MPs, and lay a solid scientific basis for accurately simulating and predicting the fate of MPs in coastal wetland water-soil systems. We highlight the effect of salinity on the fate of MPs and the corresponding priority management of MPs risks under the background of global climate change.

An efficient intrusion detection system for IoT security using CNN decision forest.

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

Enhancing intrusion detection performance using explainable ensemble deep learning.

Given the exponential growth of available data in large networks, the need for an accurate and explainable intrusion detection system has become of high necessity to effectively discover attacks in such networks. To deal with this challenge, we propose a two-phase Explainable Ensemble deep learning-based method (EED) for intrusion detection. In the first phase, a new ensemble intrusion detection model using three one-dimensional long short-term memory networks (LSTM) is designed for an accurate attack identification. The outputs of three classifiers are aggregated using a meta-learner algorithm resulting in refined and improved results. In the second phase, interpretability and explainability of EED outputs are enhanced by leveraging the capabilities of SHape Additive exPplanations (SHAP). Factors contributing to the identification and classification of attacks are highlighted which allows security experts to understand and interpret the attack behavior and then implement effective response strategies to improve the network security. Experiments conducted on real datasets have shown the effectiveness of EED compared to conventional intrusion detection methods in terms of both accuracy and explainability. The EED method exhibits high accuracy in accurately identifying and classifying attacks while providing transparency and interpretability.

Neural processing of audiovisual and painful analogue trauma and its relationship with subsequent audiovisual and pain intrusions.

Background: Posttraumatic stress disorder and medically unexplained pain frequently co-occur. While pain is common during traumatic events, the processing of pain during trauma and its relation to audiovisual and pain intrusions is poorly understood.Objective: Here we investigate neural activations during painful analogue trauma, focusing on areas that have been related to threat and pain processing, and how they predict intrusion formation. We also examine the moderating role of cumulative lifetime adversity.Methods: Sixty-five healthy women were assessed using functional magnetic resonance imaging. An analogue trauma was induced by an adaptation of the trauma-film paradigm extended by painful electrical stimulation in a 2 (film: aversive, neutral) x 2 (pain: pain, no-pain) design, followed by 7-day audiovisual and pain intrusion assessment using event-based ecological momentary assessment. Intrusions were fitted with Bayesian multilevel regression and a hurdle lognormal distribution.Results: Conjunction analysis confirmed a wide network including anterior insula (AI) and dorsal anterior cingulate cortex (dACC) being active both, during aversive films and pain. Pain resulted in activation in areas amongst posterior insula and deactivation in a network around ventromedial prefrontal cortex (VMPFC). Higher AI and dACC activity during aversive>neutral film predicted greater audiovisual intrusion probability over time and predicted greater audiovisual intrusion frequency particularly for participants with high lifetime adversity. Lower AI, dACC, hippocampus, and VMPFC activity during pain>no-pain predicted greater pain intrusion probability particularly for participants with high lifetime adversity. Weak regulatory VMPFC activation was associated with both increased audiovisual and pain intrusion frequency.Conclusions: Enhanced AI and dACC processing during aversive films, poor pain vs. no-pain discrimination in AI and dACC, as well as weak regulatory VMPFC processing may be driving factors for intrusion formation, particularly in combination with high lifetime adversity. Results shed light on a potential path for the etiology of PTSD and medically unexplained pain.

AI and dACC play a common role for both trauma- and pain-processing.In combination with high lifetime adversity, higher AI and dACC aversive film processing was associated with higher audiovisual intrusion frequency, whereas weaker AI and dACC pain discrimination enhanced the chance for pain intrusions.Weak regulatory VMPFC activity in aversive situations increased both audiovisual and pain intrusion formation.

Quality and health impact of groundwater in a coastal region: a case study from west coast of southern India.

Seawater intrusion seriously threatens the quality of coastal groundwater, affecting nearly 40% of the world's population in coastal areas. A study was conducted in the Kamini watershed situated in the Udupi district of Karnataka to assess the groundwater quality and extent of seawater intrusion. During the pre-monsoon period, 57 groundwater and 3 surface water samples were analyzed to understand the impact of seawater on the groundwater and surface water. The analysis revealed that the groundwater in the study area is slightly alkaline. The weighted overlay analysis map indicated that 11% of the study area is unsuitable for drinking water due to the influence of seawater. The Piper plot analysis revealed that the groundwater is predominantly CaMgCl facies. The hydrogeochemical facies evolution diagram (HFED) showed that 62% of the groundwater is affected by seawater. The HFED and Piper plots also indicate that the surface water is also affected by seawater. These results are also supported by various molar ratios such as Cl- vs. Cl⁻/HCO3⁻, Cl⁻ vs. Na⁺/Cl⁻, Cl- vs. SO42-/Cl-, and Cl⁻/HCO3- vs. Mg2+/Ca2+, suggesting that the majority of the water sample has been affected by seawater. The saturation indices indicated that mineral dissolution has significantly contributed to groundwater salinization. The correlation between sulfate concentration and calcite and dolomite dissolution suggested the influence of seawater intrusion in the coastal aquifer. The process of reverse ion exchange mainly influences the groundwater chemistry according to chloroalkali indices. The total hazard index (THI) values of nitrate and fluoride exceeded limits, posing health risks to adults and children. Studies suggest that with time and space, seawater intrusion is increasing in some pockets of the study area, especially along the west coast.

A Survey on Multi-Sensor Fusion Perimeter Intrusion Detection in High-Speed Railways.

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring.

Green Intrusion Detection Systems: A Comprehensive Review and Directions.

Intrusion detection systems have proliferated with varying capabilities for data generation and learning towards detecting abnormal behavior. The goal of green intrusion detection systems is to design intrusion detection systems for energy efficiency, taking into account the resource constraints of embedded devices and analyzing energy-performance-security trade-offs. Towards this goal, we provide a comprehensive survey of existing green intrusion detection systems and analyze their effectiveness in terms of performance, overhead, and energy consumption for a wide variety of low-power embedded systems such as the Internet of Things (IoT) and cyber physical systems. Finally, we provide future directions that can be leveraged by existing systems towards building a secure and greener environment.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Personnel Monitoring in Shipboard Surveillance Using Improved Multi-Object Detection and Tracking Algorithm.

Detecting and tracking personnel onboard is an important measure to prevent ships from being invaded by outsiders and ensure ship security. Ships are characterized by more cabins, numerous equipment, and dense personnel, so there are problems such as unpredictable personnel trajectories, frequent occlusions, and many small targets, which lead to the poor performance of existing multi-target-tracking algorithms on shipboard surveillance videos. This study conducts research in the context of onboard surveillance and proposes a multi-object detection and tracking algorithm for anti-intrusion on ships. First, this study designs the BR-YOLO network to provide high-quality object-detection results for the tracking algorithm. The shallow layers of its backbone network use the BiFormer module to capture dependencies between distant objects and reduce information loss. Second, the improved C2f module is used in the deep layer of BR-YOLO to introduce the RepGhost structure to achieve model lightweighting through reparameterization. Then, the Part OSNet network is proposed, which uses different pooling branches to focus on multi-scale features, including part-level features, thereby obtaining strong Re-ID feature representations and providing richer appearance information for personnel tracking. Finally, by integrating the appearance information for association matching, the tracking trajectory is generated in Tracking-By-Detection mode and validated on the self-constructed shipboard surveillance dataset. The experimental results show that the algorithm in this paper is effective in shipboard surveillance. Compared with the present mainstream algorithms, the MOTA, HOTP, and IDF1 are enhanced by about 10 percentage points, the MOTP is enhanced by about 7 percentage points, and IDs are also significantly reduced, which is of great practical significance for the prevention of intrusion by ship personnel.

Beach nourishment for coastal aquifersimpacted by climate change and population growth using machine learning approaches.

Groundwater in coastal regions is threatened by saltwater intrusion (SWI). Beach nourishment is used in this study to manage SWI in the Biscayne aquifer, Florida, USA, using a 3D SEAWAT model nourishment considering the future sea level rise and freshwater over-pumping. The present study focused on the development and comparative evaluation of seven machine learning (ML) models, i.e., additive regression (AR), support vector machine (SVM), reduced error pruning tree (REPTree), Bagging, random subspace (RSS), random forest (RF), artificial neural network (ANN) to predict the SWI using beach nourishment. The performance of ML models was assessed using statistical indicators such as coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), means absolute error (MAE), root mean square error (RMSE), and root relative squared error (RRSE) along with the graphical inspection (i.e., Radar and Taylor diagram). The findings indicate that applying SVM, Bagging, RSS, and RF models has great potential in predicting the SWI values with limited data in the study area. The RF model emerged as the best fit and closely matched observed values; it obtained R2 (0.999), NSE (0.999), MAE (0.324), RRSE (0.209), and RMSE (0.416) during the testing process. The present study concludes that the RF model could be a valuable tool for accurate predictions of SWI and effective water management in coastal areas.

Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems.

The explosive growth of the Internet of Things (IoT) has highlighted the urgent need for strong network security measures. The distinctive difficulties presented by Internet of Things (IoT) environments, such as the wide variety of devices, the intricacy of network traffic, and the requirement for real-time detection capabilities, are difficult for conventional intrusion detection systems (IDS) to adjust to. To address these issues, we propose DCGR_IoT, an innovative intrusion detection system (IDS) based on deep neural learning that is intended to protect bidirectional communication networks in the IoT environment. DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. Furthermore, complex gated recurrent networks (CGRNs) are used for the temporal feature extraction module, which is utilized by DCGR_IoT. Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. The effectiveness of the DCGR_IoT was proven through extensive evaluations of the UNSW-NB15, KDDCup99, and IoT-23 datasets, which resulted in a high detection accuracy of 99.2%. These results demonstrate the DCG potential of DCGR-IoT as an effective solution for defending IoT networks against sophisticated cyber-attacks.

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection.

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

Laboratory-scale study of mixed barriers in stratified layers with an inclined ocean aquifer boundary.

The over-pumping of freshwater makes shoreline aquifers susceptible to seawater intrusion. Most studies on aquifer homogeneity that are used to form management guidelines focus on salinization sensitivity. However, under certain extraction conditions, the geographic structure can be quite diverse, with low-permeability obstacles and preferred flow routes that affect circulation and saline transport mechanisms. Here, we used a laboratory-scale glass box apparatus of dimension 100 × 50 × 10 cm3 to study intrusion in stratified layers under the influence of an inclined ocean-aquifer boundary with a mixed barrier as a remediation technique. The TL\H ratio ranged from 0.2 to 12.84 for all stratification conditions and remediation installed. There was a 40-48 % decrease in the extent of toe length after installation. With a mixed barrier installed, the height of the intrusion was reduced, resulting in an increase in the TL\H and a decrease in the potential for toe length. The intrusion was delayed by 86.67 % in parallel stratification and 28.22 % in perpendicular stratification after comparing the time frame for base case and the mixed barrier installed condition. A parabolic profile of intrusion was observed in the low-permeability layer, while a convex-outward profile was observed in the higher-permeability layers. Similar results are obtained after conducting the sensitivity analysis. The intrusion follows an increasing pattern of ratio with increasing interaction gap opening in parallel stratification, while for perpendicular stratification, with gap opening from 10 cm to 30 cm, there was a decreasing trend followed by an increasing trend, indicating an increase in magnitude with a similar pattern of intrusion. The results of this investigation shed light on the mixed barrier's suitability for use in realistically diverse coastal aquifers. Future research could explore the utilization of different combinations of new barriers, such as under-surface barriers, which work well for stratified layers, and already established barrier systems, to further improve the efficiency of mixed barriers.

Hydraulic recharge and element dynamics during salinization in an overexploited coastal aquifer of the world's driest zone: Atacama Desert.

The management of water resources in hyper-arid coastal regions is a challenging task because proper information regarding groundwater recharge and water budget is needed for maintaining the hydraulic balance in optimal conditions, avoiding salinization and seawater intrusion. Thus, this article deals with the estimation of the hydraulic recharge and the study of the effects of salinization on the dynamics of major and trace elements in an alluvial aquifer located in the world's driest zone, the northern Atacama Desert. The result of stable water isotopes (δD and δ18O) and tritium (3H) indicated that groundwater in the area is not recent, whereas 14C results estimated a groundwater residence time ranging between 11,628 and 16,067 yBP. The estimation of the artificial recharge coming from the urban water-supply-system leaks and wastewater/river-water/groundwater infiltration during irrigation was about 19.84 hm3/year, which represents an annual negative water balance of 177 hm3/year for the aquifer. The groundwater salinization triggered by seawater intrusion (up to 32.6 %) has caused the enrichment of Li, Rb, Ca, Ba, and Sr in groundwater by cationic exchange, where the excess of aqueous Na is exchanged by these elements in the aquifer sediments. Other elements such as B, Se, Si, and Sb are enriched in groundwater by ionic strength and/or anionic exchange during salinization. The heightened B concentrations derived from the B-rich alluvial sediments were higher than the limit suggested by international guidelines, representing a risk to consumers. Vanadium seems to be unaffected by salinization, whereas Pb, Mo, As, U, and Zr did not show a clear behavior during saline intrusion. Finally, this article highlights the consequences of conducting improper water management in coastal hyper-arid regions with exacerbated agriculture.

Effects of transpalatal arch wire dimension and temporary skeletal anchorage device position on maxillary molar intrusion.

OBJECTIVES: To investigate the effects of transpalatal (TPA) wire dimension and temporary skeletal anchorage device (TSAD) position on maxillary molar intrusion. MATERIALS AND METHODS: The maxillary molar intrusion measurement system included a maxillary acrylic model, TPA, TSADs, and a three-dimensional Force/Moment (F/M) sensor. The intrusion patterns were categorized into six groups: buccal-mesial, buccal-distal, buccal-mesiodistal, palatal-mesial, palatal-distal, and palatal-mesiodistal. TPA wire dimensions were designed to be 0.7 mm, 0.9 mm, and 1.2 mm. The force and moment loads of the maxillary first molar were measured by the F/M sensor. RESULTS: Single buccal or palatal TSADs induced torquing movement, and single mesial or distal TSADs tended to promote tipping movement. Mesiodistal TSADs would have eliminated tipping, but accentuated torquing movement. The TPA significantly reduced the force and moment experienced by the maxillary first molar along three-dimensional axes. The thicker the TPA wire, the smaller the force and moment to which the maxillary first molar was subjected. CONCLUSIONS: Precise placement of TSADs might have a substantial influence on tooth movement and should be determined in accordance with specific clinical requirements. Increasing the TPA wire dimension could diminish the tipping, torquing, and rotation during TSAD-assisted maxillary molar intrusion, but these tendencies could not be completely eliminated.

Evaluation of dentoalveolar changes following maxillary incisor intrusion with one vs two anterior miniscrews in subjects with gummy smile: a randomized clinical trial.

OBJECTIVES: To examine dentoalveolar changes following intrusion of maxillary incisors with one or two anterior miniscrews in subjects with gummy smile and deep bite. MATERIALS AND METHODS: Forty-three subjects were selected and divided into two groups: group I (22 subjects: 15 women, 7 men; mean age 30 ± 10 years) received one miniscrew between the upper central incisors, and group II (21 subjects: 16 women, 5 men; mean age 30 ± 10 years) received two miniscrews between the canines and lateral incisors. Dentoalveolar parameters, including amount of intrusion, root resorption, incisor inclination, alveolar bone thickness, and buccal alveolar crest height (cementoenamel junction to labial alveolar crest), were evaluated using cone-beam computed tomography scans obtained before and after intrusion. The intergroup comparison was analyzed using a paired t-test and unpaired t-test to determine significant changes within and between groups. RESULTS: The amount of intrusion was significantly greater in group II than in group I (P < .05). No statistically significant differences were found between groups I and II for changes in incisor inclination, labial bone thickness, and buccal alveolar crest height (P > .05). CONCLUSIONS: Maxillary central and lateral incisor intrusion was significantly greater in subjects treated with two miniscrews. Root resorption of the maxillary central incisors was notably greater in subjects with one miniscrew, while maxillary lateral incisor resorption was greater in subjects treated with two miniscrews.

Predictors of Curve of Spee Leveling in Class II Division 1 Malocclusion Treated With Reverse Curve of Spee Archwires: An Observational Study.

Introduction Aligning and leveling is the initial stage of comprehensive fixed orthodontic treatment aimed at minimizing the depth of the curve of Spee (COS). Various techniques exist to decrease the magnitude of the curve. This study investigates the skeletal and dental factors that reduce COS in individuals with minor class II malocclusions receiving nitinol (NiTi) wires with a reverse curve of Spee (RCS). Materials and methods The data for this observational study was collected from a sample of 84 patients who had class II molar relations and were sequentially treated with RCS NiTi wires throughout the initial leveling and aligning phase. All patients with class II molar relationships underwent non-extraction procedures during the leveling phase. The COS was determined using digitalized dental models. Skeletal and dental characteristics that could impact COS were identified and quantified using digital lateral cephalograms and orthopantomograms recorded during the pre-treatment (T1) and post-leveling (T2) stages. After calibrating the radiographs and models, we acquired angular and linear data. The data was categorized based on gender, growth pattern, and initial alignment of the teeth. We analyzed the differences between the groups using an independent t-test and an ANOVA. A paired t-test was used to compare the difference in the dimensional values between (T1) and (T2) points. Following the correlation coefficient tests, the study used stepwise multiple linear regression analysis to assess the predictive value of independent factors on the COS. The results were considered statistically significant at p < 0.05. Results The COS decreased by -1.43 ± 0.68 mm, which is statistically significant (<0.001*). There is no significant difference in COS reduction between the categorical variables. Despite statistically significant differences in the parameters between pre- and post-treatment, the linear correlation between most of the variables and COS reduction ranged from very weak (<0.20) to weak (0.20-0.39). Conclusions The vertical extrusion of lower premolars and molars combined with the intrusion of lower incisors contributed to the reduction of the COS by RCS wires. There is a change in the orientation of the occlusal plane with the flattening of the COS.