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.

Research methods for seawater intrusion in China and recommendations for novel radium-radon technologies.

Seawater intrusion has been a globally significant environmental issue. This paper comprehensively reviews and highlights the research methods of seawater intrusion in China, recommending the potential application of novel radioactive radium-radon isotopes. Geochemical and geophysical techniques have been extensively utilized in studying seawater intrusion in China, including methods such as hydrochemical analysis, groundwater level observations, geophysical survey techniques, and isotope tracing. The former three methodologies boast a lengthier historical application in seawater intrusion field, while the radium-radon tools in isotope tracing, as newcomers, can specifically indicate crucial scientific questions such as seawater intrusion rates, salt groundwater age, water-rock reactions, and preferential flow dynamics. However, it is imperative to acknowledge the limitations inherent in the utilization of radium-radon tools within the realm of seawater intrusion research, as with any other methodologies. Strategic integration of radium-radon tools with other methodologies will propel advancements in the investigation of seawater intrusion in China. While the primary focus is on research methods in China, insights gained from novel radium-radon tools could have broader value for seawater intrusion research and coastal management globally.

Social ecological system framework as a decision-making tool for risk mitigation: A superfund site case study.

Several studies have reported vapor intrusion (VI) occurring when volatile organic compound (VOC) vapors are transported through subsurface piping systems into building spaces (e.g. conduit VI). Site-specific risk assessment and risk management practices are complicated and evolving for conduit VI, especially at large hazardous waste sites, like Superfund sites, where many stakeholders are involved and have varied interests. Here, we propose a social ecological system (SES) framework as a decision-making tool to inform risk mitigation decisions. We demonstrate the SES framework using field data associated with a Superfund site near San Francisco, California. We evaluate sewer invert elevation and groundwater elevation data, as well as pre- and post- mitigation VOC concentration data within a sewer system. Unexpectedly, the sewer located above the groundwater table was determined to be a potential source of conduit VI risks. The SES framework describes how typical stakeholders associated with the site can affect and be affected by mitigation activities. It informs decisions about mitigation implementation and long-term operation efficacy by considering stakeholder roles and interests. Ultimately, gas siphons were selected as the mitigation technology for the example site. To date, approximately 6 gas siphons have been installed to mitigate conduit VI risks throughout the community. Collectively, our findings advance risk management decisions and highlight key considerations for risk mitigation approaches at hazardous waste sites, including Superfund sites, especially where VI risks are a concern.

Hydrodynamics and dissolved organic matter components shaped the fate of dissolved heavy metals in an intensely anthropogenically disturbed estuary.

Anthropogenic activities and natural erosion caused abundant influx of heavy metals (HMs) and organic matter (OM) into estuaries characterized by the dynamic environments governed by tidal action and river flow. Similarities and differences in the fate of HM and OM as well as the influences of OM on HMs remain incomplete in estuaries with seasonal human activity and hydrodynamic force. To address this gap, dissolved HMs (dHMs) and fluorescence dissolved OM (FDOM) were investigated in the Pearl River Estuary, a highly seasonally anthropogenic and dynamic estuary. It aimed to elucidate the effects of hydrodynamic conditions and DOM on the seasonal fate of dHMs via the multivariate statistical methods. Our findings indicated dHMs and FDOM exhibited consistently higher levels in the upper estuarine and coastal waters in both seasons, predominantly controlled by the terrestrial/anthropogenic discharge. In the wet season, dHMs and humic-like substances (HULIS) were positively correlated, showing that dHMs readily combined with HULIS. This association led to a synchronous decrease offshore along the axis of the estuary and the transport following the river plume in the surface affected by the salt wedge. Contrarily, dHMs were prone to complex with protein-like components impacted by the hydrodynamics during the dry season. Principal component analysis (PCA) results revealed the terrestrial/anthropogenic inputs and the fresh-seawater mixing process were the most crucial factors responsible for the fate of dHM in wet and dry seasons, respectively, with DOM identified as a secondary but significant influencing factor in both seasons. This study holds significance in providing valuable insights into the migration, transformation, the ultimate fate of dHMs in anthropogenically influenced estuaries, as well as the intricate dynamics governing coastal ecosystems.

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.

IDAC: Federated Learning-Based Intrusion Detection Using Autonomously Extracted Anomalies in IoT.

The recent rapid growth in Internet of Things (IoT) technologies is enriching our daily lives but significant information security risks in IoT fields have become apparent. In fact, there have been large-scale botnet attacks that exploit undiscovered vulnerabilities, known as zero-day attacks. Several intrusion detection methods based on network traffic monitoring have been proposed to address this issue. These methods employ federated learning to share learned attack information among multiple IoT networks, aiming to improve collective detection capabilities against attacks including zero-day attacks. Although their ability to detect zero-day attacks with high precision has been confirmed, challenges such as autonomous labeling of attacks from traffic information and attack information sharing between different device types still remain. To resolve the issues, this paper proposes IDAC, a novel intrusion detection method with autonomous attack candidate labeling and federated learning-based attack candidate sharing. The labeling of attack candidates in IDAC is executed using information autonomously extracted from traffic information, and the labeling can also be applied to zero-day attacks. The federated learning-based attack candidate sharing enables candidate aggregation from multiple networks, and it executes attack determination based on the aggregated similar candidates. Performance evaluations demonstrated that IDS with IDAC within networks based on attack candidates is feasible and achieved comparable detection performance against multiple attacks including zero-day attacks compared to the existing methods while suppressing false positives in the extraction of attack candidates. In addition, the sharing of autonomously extracted attack candidates from multiple networks improves both detection performance and the required time for attack detection.

[The importance of the first home visit to a patient in need of psychiatric care]. / L'importance de la première visite à domicile chez une patiente en rupture de soins psychiatriques.

The first home visit (VAD) by a nurse from the medical-psychological center (CMP) for a patient who has been out of psychiatric care for a very long time, or who has never benefited from it, is decisive for the follow-up and continuity of care. The attitude and posture of the caregiver are decisive. VAD is an intrusion into a place of life, intimacy and suffering. It upsets a person who has walled himself into a shell, a cocoon, from which the fear of coming out is often massive. Anguish prevents any mobilization towards care. It takes a great deal of skill to create a bond of trust to initiate the idea of care.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

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.

Municipal pipeline networks as preferential vapor intrusion pathways: A review.

Recent reports show a rise in instances where municipal networks, such as sewer lines, serve as pathways for vapor intrusion (VI), enabling volatile organic compounds (VOCs) vapors to travel along these networks. These VOCs pose potential health risks to occupants of buildings connected to these networks. Currently, there's a lack of specific technical or regulatory guidance on identifying and assessing the VI risk associated with sewer as preferential VI pathways. This critical review summarizes key findings from studies and site investigations related to sewer VI pathways. These findings cover background VOCs concentration levels in sewers, updates to site conceptual models, advances in sewer sampling techniques, innovative tools for identifying and characterizing sewer VI, and practices for assessing and mitigating sewer VI risk. While significant improvements have been made towards understanding how municipal pipeline networks act as VI pathways, more research is still needed to develop strategies for investigating sites and assessing risks associated with "pipeline VI pathways". Future research could focus on the development of "pipeline VI pathways" data set, the improvement and validation of investigation tools, and improving the understanding of VOCs transportation mechanisms within these "pipeline VI pathways".

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.

Sleep and dream disturbances associated with dissociative experiences.

Some dissociative experiences may be related, in part, to REM intrusion into waking consciousness. If so, some aspects of dream content may be associated with daytime dissociative experiences. We tested the hypothesis that some types of dream content would predict daytime dissociative symptomology. As part of a longitudinal study of the impact of dreams on everyday behavior we administered a battery of survey instruments to 219 volunteers. Assessments included the Dissociative Experiences Scale (DES), along with other measures known to be related to either REM intrusion effects or dissociative experiences. We also collected dream reports and sleep measures across a two-week period from a subgroup of the individuals in the baseline group. Of this subgroup we analyzed two different subsamples; 24 individuals with dream recall for at least half the nights in the two-week period; and 30 individuals who wore the DREEM Headband which captured measures of sleep architecture. In addition to using multiple regression analyses to quantify associations between DES and REM intrusion and dream content variables we used a split half procedure to create high vs low DES groups and then compared groups across all measures. Participants in the high DES group evidenced significantly greater nightmare distress scores, REM Behavior Disorder scores, paranormal beliefs, lucid dreams, and sleep onset times. Validated measures of dreamed first person perspective and overall dream coherence in a time series significantly predicted overall DES score accounting for 26% of the variance in dissociation. Dream phenomenology and coherence of the dreamed self significantly predicts dissociative symptomology as an individual trait. REM intrusion may be one source of dissociative experiences. Attempts to ameliorate dissociative symptoms or to treat nightmare distress should consider the stability of dream content as a viable indicator of dissociative tendencies.

Climate change impact on groundwater resources in sandbar aquifers in southern Baltic coast.

Shallow coastal aquifers are vulnerable hydrosystems controlled by many factors, related to climate, seawater-freshwater interactions and human activity. Given on-going climate change, sea level rise and increasing human impact, it is especially true for groundwater resources situated in sandbars. We developed numerical models of unsaturated zone water flow for two sandbars in northern Poland: the Vistula Spit and the Hel Spit using HYDRUS-1D. The simulations were performed for three types of land use: pine forest, grass cover and bare soil, for 2024-2100 based on weather data and sea level rise forecasts for two emissions scenarios (RCP 4.5 and RCP 8.5). The results present prognosis of groundwater recharge, water table level and water content changeability in near-term (2023-2040), mid-term (2041-2060), and long-term period (2081-2100). Expected sea level rise and decreasing hydraulic gradient of the sandbar aquifers will probably cause in-land movement of the freshwater-saltwater interface, leading to significant decrease or complete salinization of groundwater resources. The study shows that holistic monitoring including groundwater level and salinization, sea level rise, and metheorological data (precipitation amount and variability, temperature) is crucial for sustainable management of vulnerable aquifers located in sandbars.

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.

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection.

Our increasingly connected world continues to face an ever-growing number of network-based attacks. An Intrusion Detection System (IDS) is an essential security technology used for detecting these attacks. Although numerous Machine Learning-based IDSs have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained on the NSL-KDD dataset, a publicly available collection of labeled network traffic data specifically designed to support the evaluation and benchmarking of IDSs. Ultimately, our findings demonstrate that training the DRL model on synthetic datasets generated by specific GAN models can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.

A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.

Ensemble technique of intrusion detection for IoT-edge platform.

Internet of Things (IoT) technology has revolutionized modern industrial sectors. Moreover, IoT technology has been incorporated within several vital domains of applicability. However, security is overlooked due to the limited resources of IoT devices. Intrusion detection methods are crucial for detecting attacks and responding adequately to every IoT attack. Conspicuously, the current study outlines a two-stage procedure for the determination and identification of intrusions. In the first stage, a binary classifier termed an Extra Tree (E-Tree) is used to analyze the flow of IoT data traffic within the network. In the second stage, an Ensemble Technique (ET) comprising of E-Tree, Deep Neural Network (DNN), and Random Forest (RF) examines the invasive events that have been identified. The proposed approach is validated for performance analysis. Specifically, Bot-IoT, CICIDS2018, NSL-KDD, and IoTID20 dataset were used for an in-depth performance assessment. Experimental results showed that the suggested strategy was more effective than existing machine learning methods. Specifically, the proposed technique registered enhanced statistical measures of accuracy, normalized accuracy, recall measure, and stability.

Comparative evaluation of effectiveness of three versus four mini-implants for simultaneous intrusion and retraction of maxillary anterior teeth: A 3D FEM study.

OBJECTIVE: To evaluate and compare the displacement pattern of maxillary anterior teeth in the sagittal and vertical planes and evaluate the stress distribution in pdl, bone, teeth of the maxillary anterior region, and around the mini-implants during simultaneous en-masse retraction and intrusion using two, three, and four mini-implants combinations. METHODS: A three-dimensional FEM model of maxillary teeth and periodontal ligament housed in the alveolar bone with extracted first premolarswasgenerated. The models were broadly divided into three groups according to the number of mini-implants. Mini-implants were placed bilaterally between the second premolar and molar in group I, and along with bilateral implants, an additional mid-implant was placed between the central incisors as in group II, whereas in group III, anterior mini-implants were placed in between lateral incisors and canine bilaterally. RESULTS: The two mini-implant model showed the maximum amount of retraction in the sagittal plane followed by three and four mini-implant models. In the vertical plane, all six anterior teeth showed intrusion only in the four mini-implant model. The stress in cortical bone, cancellous bone, PDL, around the mini-implants, and in lateral incisor was maximum in the three mini-implant model, followed by four mini-implants with the least stress in the two mini-implant model. CONCLUSION: The four mini-implant model is better than the three and two mini-implants model as there is a more even distribution of force in the four mini-implants model as compared to the three mini-implants model.

Impacts of groundwater dynamics around a macro-tidal river on agricultural soil salinity.

Estuaries are vulnerable to oceanic and atmospheric climate change. Much of the research investigating climate change impacts on estuaries is focused on saltwater intrusion within surface water due to drought and rising sea levels, with implications for ecosystems and humans. Groundwater and soil near estuaries may also be influenced, as estuary salinity and hydraulic head changes can impact soils and aquifers not previously at risk of salinization. This study was conducted to address knowledge gaps related to present and future groundwater salinity distribution in a groundwater system connected to a macro-tidal estuary. The studied estuary experiences a tidal bore due to its hydraulic connection to the Bay of Fundy in Nova Scotia, Canada. A parcel of agricultural land adjacent to the estuary was selected to assess the groundwater response to episodic fluctuations in estuary water levels and salinity. Groundwater monitoring and electromagnetic surveys were conducted to map soil and groundwater salinity patterns. A numerical model of groundwater flow and solute transport informed by field data was used to investigate how varying estuary salinity due to droughts and sea-level rise could impact groundwater salinity. Results showed that, in contrast to salt wedges observed along marine coasts, the saline groundwater existed as a plume immediately around the estuary. Model simulations showed that short-term droughts had an insignificant impact on the adjacent groundwater salinity. However, permanent increases in salinity caused by sea-level rise increased the plume volume by 86 %, or an additional ∼11 m horizontally and âˆ¼ 4.5 m vertically. Our results suggest that increased river salinity in this setting would not result in widespread salinization of porewater and agricultural soils, but more extensive salinization may be experienced in permeable aquifers or along more saline estuarine zones. Findings may inform land management decisions in regions exposed to increased salinity in the future.