Staged esthetic crown lengthening: Classification and guidelines for periodontal-restorative therapy.

OBJECTIVE: This article presents technical guidelines for perio-restorative esthetic crown lengthening, along with a discussion of the biologic rationale. A classification system is proposed to assist in treatment planning and sequencing the surgical and restorative phases. CLINICAL CONSIDERATIONS: When esthetic crown lengthening is performed as an adjunct to restorative therapy, the surgical approach must be determined by the anticipated position of the restorative margins. The removal of sufficient bone to achieve the desired clinical crown length and preserve the supracrestal gingival tissue dimensions is facilitated by the use of a surgical guide fabricated according to the design of the restorations. A staged approach allows sequencing the provisional restoration to minimize unesthetic sequelae during the healing period. Inadequate bone resection and/or alteration of the soft tissue dimensions results in delayed healing, leading to coronal gingival rebound and biologic width impingement. CONCLUSION: The identification and preservation of appropriate restorative and biologic landmarks is essential for success in pre-prosthetic esthetic crown lengthening treatment. A staged approach improves the esthetic management during the postsurgical healing and maturation period. CLINICAL SIGNIFICANCE: A restorative driven classification system for sequencing and staging adjunctive esthetic crown lengthening procedures is presented. Technical guidelines to enhance gingival margin predictability are suggested, accompanied by relevant evidence. In addition, wound healing timelines following gingival and osseous resection are provided.

Sequential Human Histology Results of the Subperiosteal Minimally Invasive Aesthetic Ridge Augmentation Technique (SMART): A Chronologic Wound Healing Proof-of-Principle Study.

Traditional GBR procedures have been associated with frequent complications and compromised peri-implant esthetics. Tunneling techniques have been proposed as a promising alternative in this regard. More recently, a subperiosteal minimally invasive aesthetic ridge augmentation technique (SMART) was reported to have been clinically successful in a prospective case series. This technique includes the use of a bone graft/recombinant human platelet-derived growth factor-BB combination delivered to the site by a tunneling method. However, published histologic information regarding the nature of the regenerated tissue has been limited. The current study evaluated the histologic and histomorphometric findings of four human specimens harvested at 2, 5, 9, and 14 months after ridge augmentation using the SMART method. Evaluations of the wound healing and bone regeneration sequence over time found that the ridge augmentation was the result of extensive new bone formation that progressed through the woven bone to lamellar bone stages, with remodeling of the xenogeneic graft material and replacement by patient bone. This is the first study utilizing sequential human specimens to histologically examine the chronology of wound healing following alveolar ridge augmentation.

Analyzing preventive precautions to limit spread of COVID-19.

With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples' sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

Comparative analysis of machine learning methods to detect fake news in an Urdu language corpus.

Wide availability and large use of social media enable easy and rapid dissemination of news. The extensive spread of engineered news with intentionally false information has been observed over the past few years. Consequently, fake news detection has emerged as an important research area. Fake news detection in the Urdu language spoken by more than 230 million people has not been investigated very well. This study analyzes the use and efficacy of various machine learning classifiers along with a deep learning model to detect fake news in the Urdu language. Logistic regression, support vector machine, random forest (RF), naive Bayes, gradient boosting, and passive aggression have been utilized to this end. The influence of term frequency-inverse document frequency and BoW features has also been investigated. For experiments, a manually collected dataset that contains 900 news articles was used. Results suggest that RF performs better and achieves the highest accuracy of 0.92 for Urdu fake news with BoW features. In comparison with machine learning models, neural networks models long short term memory, and multi-layer perceptron are used. Machine learning models tend to show better performance than deep learning models.

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach.

The periodic inspection of railroad tracks is very important to find structural and geometrical problems that lead to railway accidents. Currently, in Pakistan, rail tracks are inspected by an acoustic-based manual system that requires a railway engineer as a domain expert to differentiate between different rail tracks' faults, which is cumbersome, laborious, and error-prone. This study proposes the use of traditional acoustic-based systems with deep learning models to increase performance and reduce train accidents. Two convolutional neural networks (CNN) models, convolutional 1D and convolutional 2D, and one recurrent neural network (RNN) model, a long short-term memory (LSTM) model, are used in this regard. Initially, three types of faults are considered, including superelevation, wheel burnt, and normal tracks. Contrary to traditional acoustic-based systems where the spectrogram dataset is generated before the model training, the proposed approach uses on-the-fly feature extraction by generating spectrograms as a deep learning model's layer. Different lengths of audio samples are used to analyze their performance with each model. Each audio sample of 17 s is split into 3 variations of 1.7, 3.4, and 8.5 s, and all 3 deep learning models are trained and tested against each split time. Various combinations of audio data augmentation are analyzed extensively to investigate models' performance. The results suggest that the LSTM with 8.5 split time gives the best results with the accuracy of 99.7%, the precision of 99.5%, recall of 99.5%, and F1 score of 99.5%.

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset.

COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis.

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.

Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for its accurate prediction is of significant importance. Diagnosis of blood cancer using leukemia microarray gene data and machine learning approach has become an important medical research today. Despite research efforts, desired accuracy and efficiency necessitate further enhancements. This study proposes an approach for blood cancer disease prediction using the supervised machine learning approach. For the current study, the leukemia microarray gene dataset containing 22,283 genes, is used. ADASYN resampling and Chi-squared (Chi2) features selection techniques are used to resolve imbalanced and high-dimensional dataset problems. ADASYN generates artificial data to make the dataset balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. For classification, a hybrid logistics vector trees classifier (LVTrees) is proposed which utilizes logistic regression, support vector classifier, and extra tree classifier. Besides extensive experiments on the datasets, performance comparison with the state-of-the-art methods has been made for determining the significance of the proposed approach. LVTrees outperform all other models with ADASYN and Chi2 techniques with a significant 100% accuracy. Further, a statistical significance T-test is also performed to show the efficacy of the proposed approach. Results using k-fold cross-validation prove the supremacy of the proposed model.

Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology.

Every year about one million people die due to diseases transmitted by mosquitoes. The infection is transmitted to a person when an infected mosquito stings, injecting the saliva into the human body. The best possible way to prevent a mosquito-borne infection till date is to save the humans from exposure to mosquito bites. This study proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes such as the Aedes and Culex. The proposed system will effectively aid in epidemiology to design evidence-based policies and decisions by analyzing the risks and transmission. The study proposes an effective methodology for the classification of mosquitoes using ML and CNN models. The novel RIFS has been introduced which integrates two types of feature selection techniques - the ROI-based image filtering and the wrappers-based FFS technique. Comparative analysis of various ML and deep learning models has been performed to determine the most appropriate model applicable based on their performance metrics as well as computational needs. Results prove that ETC outperformed among the all applied ML model by providing 0.992 accuracy while VVG16 has outperformed other CNN models by giving 0.986 of accuracy.

Self voting classification model for online meeting app review sentiment analysis and topic modeling.

Online meeting applications (apps) have emerged as a potential solution for conferencing, education and meetings, etc. during the COVID-19 outbreak and are used by private companies and governments alike. A large number of such apps compete with each other by providing a different set of functions towards users' satisfaction. These apps take users' feedback in the form of opinions and reviews which are later used to improve the quality of services. Sentiment analysis serves as the key function to obtain and analyze users' sentiments from the posted feedback indicating the importance of efficient and accurate sentiment analysis. This study proposes the novel idea of self voting classification (SVC) where multiple variants of the same model are trained using different feature extraction approaches and the final prediction is based on the ensemble of these variants. For experiments, the data collected from the Google Play store for online meeting apps were used. Primarily, the focus of this study is to use a support vector machine (SVM) with the proposed SVC approach using both soft voting (SV) and hard voting (HV) criteria, however, decision tree, logistic regression, and k nearest neighbor have also been investigated for performance appraisal. Three variants of models are trained on a bag of words, term frequency-inverse document frequency, and hashing features to make the ensemble. Experimental results indicate that the proposed SVC approach can elevate the performance of traditional machine learning models substantially. The SVM obtains 1.00 and 0.98 accuracy scores, using HV and SV criteria, respectively when used with the proposed SVC approach. Topic-wise sentiment analysis using the latent Dirichlet allocation technique is performed as well for topic modeling.

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach.

Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer's, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning.

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human's voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors' knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

Deepfake tweets classification using stacked Bi-LSTM and words embedding.

The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques make it easier to generate fake content and post it on social media. In addition, tweets with deep fake content make their way to social platforms. The polarity of such tweets is significant to determine the sentiment of people about deep fakes. This paper presents a deep learning model to predict the polarity of deep fake tweets. For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets. Several well-known machine learning classifiers are investigated as well such as support vector machine, logistic regression, Gaussian Naive Bayes, extra tree classifier, and AdaBoost classifier. These classifiers are utilized with term frequency-inverse document frequency and a bag of words feature extraction approaches. Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM. Experimental results indicate that the proposed SBi-LSTM outperforms both machine and deep learning models and achieves an accuracy of 0.92.

Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors.

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

Non-Invasive Driver Drowsiness Detection System.

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

Contour Management of Implant Restorations for Optimal Emergence Profiles: Guidelines for Immediate and Delayed Provisional Restorations.

Adequate management of the implant-supported restoration has become an important task when trying to obtain optimal esthetic outcomes. The transgingival area must be developed to maintain or influence the final appearance of the peri-implant soft tissues. Two distinct zones within the implant abutment/crown can be identified: the critical contour and the subcritical contour. Their design and subsequent alteration may impact the peri-implant soft tissue architecture, including the gingival margin level and zenith, labial alveolar profile, and gingival color. Defining these two areas helps clarify how to process soft tissue contours and may additionally improve the necessary communication with the laboratory. Since there are many protocols for placing implants, it is worthwhile to determine similarities in the contouring and macrodesign of their corresponding provisional restorations. Therefore, the purpose of this paper is to discern the general characteristics of the critical and subcritical contours for provisional restorations made for immediate and delayed implants in order to obtain guidelines for daily clinical practice.

Restorative Emergence Profile for Single-Tooth Implants in Healthy Periodontal Patients: Clinical Guidelines and Decision-Making Strategies.

The peri-implant soft tissue seal consists of a connective tissue cuff and a junctional epithelium that is different from the arrangement of periodontium around a natural tooth. However, the peri-implant soft tissue complex lacks Sharpey's fibers, thus offering less resistance to clinical probing and biofilm penetration compared to the natural dentition. Therefore, the proper restorative emergence profile design is essential to facilitate favorable esthetic outcomes and maintain peri-implant health. The aim of this article is to review the currently available evidence related to the design of subgingival (critical and subcritical) and supragingival contours of the implant restorative emergence profile (IREP) as well as provide a flowchart for decision-making in clinical practice. Theoretically, the subgingival contours of the crown/abutment complex should mimic the morphology of the root and the cervical third of the anatomic crown as much and as often as possible. However, this is highly dependent upon the three-dimensional spatial position of the implant relative to the hard and soft tissue complex, in addition to the location of the definitive restoration. Frequently, a convex critical contour is required on the facial aspect of a palatally or incisally positioned implant to support an adequate gingival-margin architecture. Conversely, if the implant is placed too far facially, then a flat or concave contour is recommended. In instances where soft tissue support is not needed, the subcritical area may be undercontoured to increase the thickness, height, and stability of the soft tissue cuff.

Tridimensional Reconstruction of a Complex Iatrogenic Defect Using Orthodontic Forced Eruption and Minimally Invasive Bone Grafting.

As the use of dental implants has become more common, so has the frequency of complications and unforeseen outcomes. This article describes the treatment of a complex iatrogenic defect secondary to a failed implant (No. 7) and multiple bone-grafting attempts in the maxillary anterior region. The patient's revealing smile line and high-risk circumstances demanded the use of an interdisciplinary treatment approach with high potential for predictable esthetic results. Forced eruption was performed to restore the alveolar height deficit and develop the compromised hard and soft tissues around teeth Nos. 6 and 8. The subperiosteal minimally invasive (a)esthetic ridge-augmentation technique (SMART) was subsequently used to provide horizontal bone augmentation while preserving the soft-tissue architecture. After bone-graft integration, immediate postextraction implants were placed at Nos. 6 and 8 using a flapless approach, and a screwretained long-term polymethylmethacrylate provisional prosthesis was delivered during the same appointment. The synergy of these combined therapies resulted in a complete tridimensional reconstruction of the defect. Gingival and alveolar volumes and gingival margin levels were successfully restored.

Subperiosteal Minimally Invasive Aesthetic Ridge Augmentation Technique (SMART): A New Standard for Bone Reconstruction of the Jaws.

Traditional guided bone regeneration techniques include flap mobilization and placement of a bone graft, often with the use of space-maintaining devices and cell-occlusive membranes. This approach is associated with frequent complications that negatively affect the outcome of the augmentation and the peri-implant soft tissue esthetics. Although current tunneling techniques have focused on periodontal soft tissue applications, earlier publications described their use for horizontal augmentation of mandibular posterior edentulous ridges in full-denture patients. More recently, the use of recombinant human platelet-derived growth factor (rhPDGF-BB) was tested with different bone matrices to treat maxillary anterior edentulous spans. The present case series reports the use of a subperiosteal minimally invasive aesthetic ridge augmentation technique (SMART) to treat 60 single and multiple edentulous, dentate, and implant sites on 21 patients and five treatment categories with a follow-up period ranging from 4 to 30 months. The technique includes the use of a laparoscopic approach to deliver a growth factor/xenograft combination into a subperiosteal pouch. No flap elevation, cell-occlusive membranes, space-maintaining devices, or decortication procedures were used. The results from this case series demonstrated predictable and consistent bone regeneration. The average gain in ridge width for all treatment categories was 5.11 mm (SD 0.76 mm), which compares favorably with previously published reports. Morbidity and complication rates were consistently reduced as well. Human histology results show xenograft particles surrounded by newly formed bone. The role of the periosteum as a source of pluripotent cells in growth factor­mediated bone regeneration is discussed.

Lingualized flapless implant placement into fresh extraction sockets preserves buccal alveolar bone: a cone beam computed tomography study.

The use of immediate placement and loading protocols in implant dentistry has increased during the past several years. However, limited information related to the response of the osseous architecture has been reported. The purpose of this study was to evaluate the fate of the buccal alveolar plate with cone beam computed tomography (CBCT) following lingualized placement of implants into fresh extraction sockets using a flapless surgical approach and immediate nonocclusal loading. A total of 14 patients who required extraction of a single maxillary incisor were selected for this study. CBCT was performed preextraction, at the time of implant placement, and 6 months following implant surgery. The results of this study indicate that resorption of the buccal alveolar plate was not significant. It was therefore concluded that with strict patient selection and appropriate technique, predictable healing can be achieved with lingualized implant placement into fresh extraction sockets and immediate loading.