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Aim The present study was aimed to clinically evaluate the effectiveness of coenzyme Q10 (CoQ10) in the management of chronic periodontitis. Materials and Methods A total of 60 patients aged between 30 and 60 years with bleeding on probing and probing pocket depth (PPD) of 3 to 5 mm were selected and divided into three groups, with group I receiving scaling and root planing, group II CoQ10 formulation for 6 weeks, and group III receiving both scaling and root planning, followed by coenzyme Q10 administration for 6 weeks. PPD, relative attachment level (RAL), and gingival index were recorded in all the groups at baseline, 6 weeks, and 3 months, respectively. The data was statistically analyzed using Kruskal–Wallis, Mann–Whitney, and Wilcoxon signed rank tests. Result Intragroup comparison showed statistically significant difference (p ≤ 0.05) between the clinical parameters of all the groups at all time intervals, whereas intergroup comparison of all the parameters showed high statistically significant difference (p ≤ 0.001) in group III at various time intervals followed by group I and group II. Conclusion It was concluded from the study that CoQ10 is a useful adjunct in treating chronic periodontitis by boosting the host resistance to periodontal disease.

The term radar was coined in 1940 by the United States Navy and is derived from three terms, radio detection and ranging. Radar transmits electromagnetic (EM) waves of radio frequency (RF). Radar is also helpful in classifying targets as known or unknown; a known target may be monitored to check the progress of the respective target. An unknown target may be a theft unit; it may or may not be with the intentions of causing harm. Radar also faces interferences while its operation, namely clutter or some intentional/unintentional jammers (Han C et al, Multimed Tools Appl 1–16, 2018 [Han C, Guangyu G, Yu Z (2018) Real-time small traffic sign detection with revised faster-RCNN. Multimed Tools Appl 1–16]). Moreover, radar also faces obstruction by its own thermal noise. Nowadays, radar is advanced enough to implement the two-dimensional and three-dimensional mapping. Radar is also widely used for collision avoidance and earth resource monitoring (Hu et al, IEEE Sens J 18:3152–3162, 2018 [Hu et al. in IEEE Sensors J 18:3152–3162, 2018]).

This paper presents an Optical Character Recognition (OCR) system for documents with English text and mathematical expressions. Neural network architectures using CNN layers and/or dense layers achieve high level accuracy in character recognition task. However, these models require large amount of data to train the network, with balanced number of samples for each class. Recognition of mathematical symbols poses challenges of the imbalance and paucity of training data available. To address this issue, we pose the character recognition problem as a Distance Metric Learning problem. We propose a Siamese-CNN Network that learns discriminative features to identify if the two images in a pair contain similar or dissimilar characters. The network is then used to recognize different characters by character matching where test images are compared to sample images of any target class which may or may not be included during training. Thus our model can scale to new symbols easily. The proposed approach is invariant to author’s handwriting. Our model has been tested over images extracted from a dataset of scanned answer scripts collected by us. It is seen that our approach achieves comparable performance to other architectures using convolutional layers or dense layers while using lesser training data.

Gingival recession is defined as “the displacement of marginal tissue apical to the cementoenamel junction (CEJ)”. Various classifications have been proposed to classify gingival recession. Miller's classification of gingival recession is most widely followed. With a wide array of cases in daily clinical practice, it is often difficult to classify numerous gingival recession cases according to defined criteria of the present classification systems. This article outlines the limitations of present classification systems and also the new classifications that have been proposed to classify gingival recession.

Structural analysis helps in parsing the mathematical expressions. Various approaches for structural analysis have been reported in literature, but they mainly deal with online and printed expressions. In this work, two-dimensional, stochastic context-free grammar is used for the structural analysis of offline handwritten mathematical expressions in a document image. The spatial relation between characters in an expression has been incorporated so that the structural variability in handwritten expressions can be tackled.

Recognition of touching characters in mathematical expressions is a challenging problem in the field of document image analysis. Various approaches for recognizing touching maths symbols have been reported in literature, but they mainly dealt with printed expressions and handwritten numeral strings. In this work, a new segmentation-free approach is proposed which matches convex shape portions of symbols occurring in various layout such as subscript, superscript, fraction etc. and is able to perform spotting of symbols present in a handwritten expression. Our contribution lies in the design of a novel feature which can handle touching symbols effectively in the presence of handwriting variations. This recognition-based approach helps in spotting symbols in an expression even in the presence of clutter created by the presence of other symbols.

Cryotherapy is the clinican use of cold, applied locally or generally through various methods, to lower the temperature of the skin and subcutaneous tissues. Clinicians often emphasis that patients apply ice for therapeutic purposes after physical injuries and various surgical procedures. This case report describes a case of Oral Lichen Planus being treated with cryosurgery in 52 years old female. Keywords: Cryosurgery, Cryoprobe, Re-epithelization.

Aims and Objectives: Curing units in dental offices across Punjab are analyzed for a practical purpose. Materials and Methods: One thousand light-curing units regularly used by the dentists in dental offices across the state of Punjab were examined for their output intensity. Various factors include as follows: type, power of light-curing unit, material molecule formation on tip (YES/NO), diameter of guide tip, and frequency of bulb replacement. A radiometer, a magnifying glass, and a Vernier caliper were used. Data were collected and analyzed. Results: About 75.80% of dentists use light-emitting diode (LED) lights, while 24.20% utilize halogen lamps to cure dental composites. 36.60% of light-curing units of the state had light intensity below 300 mW/cm2, out of which 61.60% were quartz–tungsten–halogen (QTH) and 28.60% LED light-curing units. 17.40% of light-curing units recorded light intensity between 301 and 400 mW/cm2. 46% of light-curing units registered an output intensity of >400 mW/cm2. 79.60% of light units showed material particle buildup on light-curing tips. 62.40% of dentists never replaced the bulbs of their light-curing units. Two-third of dental the practitioners avoided infection control barriers on the tips of curing units. Conclusions: There is a deficit of knowledge among dental practitioners in Punjab regarding care of light-curing units. Dental light-curing units should be regularly checked and infection control remedies should be opted.

the presented work, a robotic arm is controlled using the EMG signal acquired from the electrodes attached to the human arm. The EMG signals are acquired from three different muscle groups of the upper forearm. The EMG signal is very noisy and of the order of micro volts. Faithful extraction of the EMG signal is the prime objective in the presented work. The acquired signal is then fed to signal conditioning unit consisting of signal acquisition, amplification, rectification and final filtration. The output of the signal conditioning unit is then converted to digital signal using a 16-bit serial ADC. The digitized signal is used as feedback and control signal for final control of the robotic arm. The robotic arm is attached to the shaft of the stepper motor for motion. Stepper motor is interfaced using microcontroller. There may be more than one stepper motor to give multi-dimensional motion to robotic arm. It can be assumed that with one single stepper motor, ideally we can have 360/θ no. of positions (θ being the step angle). Presently we have used only two stepper motors for up-down motion at two joints thereby enabling to give (360/θ)2 no. of positions.