Self-assessed Oral Health Awareness and Attitude of the First and Final Year Undergraduate Medical and Dental Students in India

Aims and Objective: To compare the attitude and awareness of dental and medical students in the first and the final academic years and to trace the change in their attitude and awareness towards oral health; to also bring out differences/similarities in response to the questionnaire based on gender. Materials and methods: A modified version of HU-DBI questionnaire was used to conduct the survey in medical and dental colleges of India. A total of 279 students were recruited in the study. The data collected concerned the oral hygiene awareness and attitude, dental complaints and previous visits to dentists. Results: Significant differences were found for 9 of 17 items, reflecting an increased awareness and improved attitude toward oral health in the final year dental students as compared to the first year dental students. The dental students exhibited better awareness than the medical students irrespective of gender. Conclusion: Significant improvement was found in the awareness and attitude of the final year dental students as compared to the first year dental students. This change was marginal in the medical students

Emerging role of transesophageal echocardiography in severe chronic obstructive pulmonary disease

Background: Pulmonary hypertension (PH) secondary to chronic obstructive pulmonary disease (COPD) has a prevalence from 20 to 91% depending on the definition of PH (mPAP >20 versus >25 mmHg). Pulmonary vasoconstriction, pulmonary vascular remodeling, endothelial dysfunction, inflammation and destruction of the pulmonary vascular bed being the common mechanisms behind. Transthoracic echocardiograms (TTE) though the most important non-invasive tool to measure degree of PH, may give false negative results in severe COPD cases due to poor echo window. This could be overcome by doing transesophageal echocardiograms (TEE) in those cases, which is, though invasive but gives good results. The aim of the study was to evaluate the role of transesophageal echocardiography in COPD patients.Methods: Total 100 patients of COPD were evaluated for PH via TTE and TEE was performed in all those 33 patients whose TTE were non-confirmatory due to poor echo window.Results: There were 0% patient with poor echo window in COPD grade 1, 18.18% in grade 2, 42.2% and 39.39% in grade 3 and grade 4 respectively. P-value obtained was statistically significant P <0.001. Out of 33 COPD patients with poor echo window, In grade 3 and grade 4, 64.2% and 76.9% patients had TEE findings respectively while in grade1 and grade 2 0% and 33.33% patient had TEE finding.Conclusions: TTE though is an excellent tool for diagnosing  pulmonary artery hypertension in COPD patients, has its limitation especially in severe COPD cases due to poor echo window which may give false negative results. So TEE should be recommended in all those severe COPD cases that have poor echo window

Precision Agriculture and Financial Management: A Profitable Synergy

This comprehensive study explores the dynamic interplay between Precision Agriculture and Financial Management, shedding light on their pivotal roles in the contemporary agricultural landscape. Precision Agriculture, underpinned by cutting-edge technologies and tools, delivers a host of compelling benefits, including amplified crop yields, judicious resource allocation, and robust environmental stewardship. Financial Management in agriculture anchors its foundations on two critical pillars: the pursuit of financial sustainability and the proficient utilization of financial metrics and analysis. These multifaceted domains are instrumental in ensuring the enduring viability of farming operations, underscoring their intrinsic value in agricultural practices. The symphony between Precision Agriculture and Financial Management significantly amplifies the financial dynamics of farming. Their convergence cultivates an environment that nurtures profitability, upholds cost-efficiency, and champions ecological responsibility. This confluence is poised to address the burgeoning global food demand, charting a course toward optimal resource utilization, sustainable agricultural practices, and the safeguarding of the environment. This chapterembarks on a comprehensive exploration of this synergistic relationship, elucidating the profound impact of data-driven decision-making, advanced technology integration, and meticulous resource management on the farm's bottom line. The outcome of this partnership holds the potential to redefine the agricultural landscape, ensuring not only the prosperity of farming operations but also the responsible stewardship of the earth's resources

Deep Learning- Based Surveillance System using Face Recognition

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises

Anesthetic management of superior vena cava syndrome due to anterior mediastinal mass

Anesthetic management of superior vena cava syndrome carries a possible risk of life-threatening complications such as cardiovascular collapse and complete airway obstruction during anesthesia. Superior vena cava syndrome results from the enlargement of a mediastinal mass and consequent compression of mediastinal structures resulting in impaired blood flow from superior vena cava to the right atrium and venous congestion of face and upper extremity. We report the successful anesthetic management of a 42-year-old man with superior vena cava syndrome posted for cervical lymph node biopsy

Fracture resistance of endodontically treated permanent anterior teeth restored with three different esthetic post systems: An in vitro study

Background: Esthetic coronal reconstruction of fractured anterior teeth is often performed using intra radicular posts. Most of the commonly used commercially esthetic post systems do not exhibit similar physical properties as dentin resulting in failures. Aim: To evaluate and compare the fracture resistance and mode of failure of simulated traumatized permanent central incisors restored with three different post systems including biologic dentin posts. Materials and Methods: A total of 40 recently extracted human maxillary central incisors with similar dimensions were decoronated 2 mm above the cemento-enamel junction and endodontically treated. Ten specimens were randomly selected as the Group I - Control group (core built teeth without intraradicular posts). The remaining 30 teeth were equally divided and restored with zirconia (Group II, n = 10), fiber re-inforced composite (FRC) (Group III, n = 10) and biologic dentin posts (Group IV, n = 10) using resin bonded cement and their cores built-up. These samples were embedded in acrylic resin and then secured in a Universal Testing Machine and subjected to fracture resistance testing. The location of failure in the specimens was evaluated using a stereomicroscope. Results: Intergroup comparison revealed that the control group and zirconia post group (522 ± 110 N) demonstrated the least fracture resistance, while dentin post group (721 ± 127 N) the highest. There was no statistically significant difference between fiber post and dentin post groups. Fractures that were repairable were observed in fiber post and dentin post groups, whereas mostly unrestorable, catastrophic fractures were observed in the zirconia post group. Conclusion: Teeth restored with the biologic dentin post system demonstrated the highest fracture resistance and repairable fractures, closely followed by FRC post system. The least fracture resistance and most catastrophic fractures were demonstrated by the zirconia post system

Patron–Prophet Artificial Bee Colony Approach for Solving Numerical Continuous Optimization Problems

The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC’s performance in higher-dimension situations towards global optima is not on par with other models due to its deficiency in balancing intensification and diversification. In this research, two different strategies are applied for the improvement of the search capability of the ABC in a multimodal search space. In the ABC, the first strategy, Patron–Prophet, is assessed in the scout bee phase to incorporate a cooperative nature. This strategy works based on the donor–acceptor concept. In addition, a self-adaptability approach is included to balance intensification and diversification. This balancing helps the ABC to search for optimal solutions without premature convergence. The first strategy explores unexplored regions with better insight, and more profound intensification occurs in the discovered areas. The second strategy controls the trap of being in local optima and diversification without the pulse of intensification. The proposed model, named the PP-ABC, was evaluated with mathematical benchmark functions to prove its efficiency in comparison with other existing models. Additionally, the standard and statistical analyses show a better outcome of the proposed algorithm over the compared techniques. The proposed model was applied to a three-bar truss engineering design problem to validate the model’s efficacy, and the results were recorded

Patron&ndash;Prophet Artificial Bee Colony Approach for Solving Numerical Continuous Optimization Problems

The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC&rsquo;s performance in higher-dimension situations towards global optima is not on par with other models due to its deficiency in balancing intensification and diversification. In this research, two different strategies are applied for the improvement of the search capability of the ABC in a multimodal search space. In the ABC, the first strategy, Patron&ndash;Prophet, is assessed in the scout bee phase to incorporate a cooperative nature. This strategy works based on the donor&ndash;acceptor concept. In addition, a self-adaptability approach is included to balance intensification and diversification. This balancing helps the ABC to search for optimal solutions without premature convergence. The first strategy explores unexplored regions with better insight, and more profound intensification occurs in the discovered areas. The second strategy controls the trap of being in local optima and diversification without the pulse of intensification. The proposed model, named the PP-ABC, was evaluated with mathematical benchmark functions to prove its efficiency in comparison with other existing models. Additionally, the standard and statistical analyses show a better outcome of the proposed algorithm over the compared techniques. The proposed model was applied to a three-bar truss engineering design problem to validate the model&rsquo;s efficacy, and the results were recorded

A critical survey of live virtual machine migration techniques

Abstract Virtualization techniques effectively handle the growing demand for computing, storage, and communication resources in large-scale Cloud Data Centers (CDC). It helps to achieve different resource management objectives like load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing through Virtual Machine (VM) migration. VM migration is a resource-intensive procedure as VM’s continuously demand appropriate CPU cycles, cache memory, memory capacity, and communication bandwidth. Therefore, this process degrades the performance of running applications and adversely affects efficiency of the data centers, particularly when Service Level Agreements (SLA) and critical business objectives are to be met. Live VM migration is frequently used because it allows the availability of application service, while migration is performed. In this paper, we make an exhaustive survey of the literature on live VM migration and analyze the various proposed mechanisms. We first classify the types of Live VM migration (single, multiple and hybrid). Next, we categorize VM migration techniques based on duplication mechanisms (replication, de-duplication, redundancy, and compression) and awareness of context (dependency, soft page, dirty page, and page fault) and evaluate the various Live VM migration techniques. We discuss various performance metrics like application service downtime, total migration time and amount of data transferred. CPU, memory and storage data is transferred during the process of VM migration and we identify the category of data that needs to be transferred in each case. We present a brief discussion on security threats in live VM migration and categories them in three different classes (control plane, data plane, and migration module). We also explain the security requirements and existing solutions to mitigate possible attacks. Specific gaps are identified and the research challenges in improving the performance of live VM migration are highlighted. The significance of this work is that it presents the background of live VM migration techniques and an in depth review which will be helpful for cloud professionals and researchers to further explore the challenges and provide optimal solutions

An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices

In this research, a neural network (NN) model for metal price forecasting based on an evolutionary approach is proposed. Both the neural network model’s network parameters and network architecture are selected automatically. The time series metal price data set is used to construct a novel fitness function that takes into account both error minimizations and the reproduction of the auto-correlation function. Calculating the average entropy values allowed the selection of the input parameter count for the neural network model. Gold price forecasting was performed using the proposed methodology. The optimal hidden node number, learning rate, and momentum are 9, 0.026, and 0.76, respectively, according to the evolutionary-based NN model. The proposed strategy is shown to reduce estimation error while also reproducing the auto-correlation function of the time series data set by the validation results with gold price data. The performance of the proposed method is better than other current methods, according to a comparison study