Prevalence of Dental Caries among Slum Dwelling Children Aged 12-15 Years in Kolhapur, Maharashtra, India

INTRODUCTION: Dental caries, is a global health burden which hampers the holistic health of an individual and leads to complications later in life.AIM: To assess the dental caries status of children in slum-dwelling areas of Kolhapur district, Maharashtra, India.MATERIALS AND METHODS: A cross-sectional, descriptive approach was conducted among 400 slum dwelling children aged 12-15 years using DMFT index, sterile instruments and ADA type III examinations. Descriptive statistics were applied using Microsoft excel. Data was then transferred into SPSS version 21.0 and the t-test (paired), Spearman’s correlation and Odd’s Ratio were applied to find significant associations, if any.RESULTS: The prevalence of dental caries was found to be 69.0%. Mean decay values were 3.02±1.22, whereas the mean DMFT was 1.77±1.2. A significant difference was seen between caries free and children affected with caries (p=04*, r=0.78). It was also observed that males were 2.1 times more prone to have a higher DMFT as compared to females.CONCLUSION: It is recommended that further studies be carried out among slum dwelling children of Kolhapur district, Maharashtra, India and specific dental education be imparted to them to improve their oral health

A W-Shaped Convolutional Network for Robust Crop and Weed Classification in Agriculture

Agricultural image and vision computing are significantly different from other object classification-based methods because two base classes in agriculture, crops and weeds, have many common traits. Efficient crop, weeds, and soil classification are required to perform autonomous (spraying, harvesting, etc.) activities in agricultural fields. In a three-class (crop-weed-background) agricultural classification scenario, it is usually easier to accurately classify the background class than the crop and weed classes because the background class appears significantly different feature-wise than the crop and weed classes. However, robustly distinguishing between the crop and weed classes is challenging because their appearance features generally look very similar. To address this problem, we propose a framework based on a convolutional W-shaped network with two encoder-decoder structures of different sizes. The first encoder-decoder structure differentiates between background and vegetation (crop and weed), and the second encoder-decoder structure learns discriminating features to classify crop and weed classes efficiently. The proposed W network is generalizable for different crop types. The effectiveness of the proposed network is demonstrated on two crop datasets – a tobacco dataset and a sesame dataset, both collected in this study and made available publicly online for use by the community – by evaluating and comparing the performance with existing related methods. The proposed method consistently outperforms existing related methods on both datasets

Human Gait Recognition Subject to Different Covariate Factors in a Multi-View Environment

Human gait recognition system identifies individuals based on their biometric traits. A human’s biometric features can be grouped into physiologic or behavioral traits. Biometric traits, such as the face [1], ears [2], iris [3], finger prints, passwords, and tokens, require highly accurate recognition and a well-controlled human interaction to be effective. In contrast, behavioral traits such as voice, signature, and gait do not require any human interaction and can be collected in a hidden and non-invasive mode with a camera system at a low resolution. In comparison with other physiological traits, one of the main advantages of gait analysis is the collection of data from a certain distance. However, gait is less powerful than physiological traits, yet it still has widespread application in surveillance for unfavorable situations. From traditional algorithms to deep learning models, a gait survey provides a detailed history of gait recognition

Towards Sweetness Classification of Orange Cultivars Using Short‑Wave NIR Spectroscopy

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices

Analysis of Human Gait Cycle with Body Equilibrium based on leg Orientation

Gait analysis identifies the posture during movement in order to provide the correct actions for a normal gait. A person\u27s gait may differ from others and can be recognized by specific patterns. Healthy individuals exhibit normal gait patterns, while lower limb amputees exhibit abnormal gait patterns. To better understand the pitfalls of gait, it is imperative to develop systems capable of capturing the gait patterns of healthy individuals. The main objective of this research was to introduce a new concept in gait analysis by computing the static and dynamic equilibrium in a real-world environment. A relationship was also presented among the parameters stated as static \& dynamic equilibrium, speed, and body states. A sensing unit was installed on the designed metal-based leg mounting assembly on the lateral side of the leg. An algorithm was proposed based on two variables: the position of the leg in space and the angle of the knee joint measured by an IMU sensor and a rotary encoder. It was acceptable to satisfy the static conditions when the body was in a fixed position and orientation, whether lying down or standing. While walking and running, the orientation is determined by the position and knee angle variables, which fulfill the dynamic condition. High speed reveals a rapid change in orientation, while slow speed reveals a slow change in orientation. The proposed encoder-based feedback system successfully determined the flexion at 47$^\circ$, extension at 153$^\circ$, and all seven gait cycle phases were recognized within this range of motion. Body equilibrium facilitates individuals when they are at risk of falling or slipping

In vivo study of optical speckle decorrelation time across depths in the mouse brain

The strong optical scattering of biological tissue confounds our ability to focus light deeply into the brain beyond depths of a few hundred microns. This challenge can be potentially overcome by exploiting wavefront shaping techniques which allow light to be focused through or inside scattering media. However, these techniques require the scattering medium to be static, as changes in the arrangement of the scatterers between the wavefront recording and playback steps reduce the fidelity of the focus that is formed. Furthermore, as the thickness of the scattering medium increases, the influence of the dynamic nature becomes more severe due to the growing number of scattering events experienced by each photon. In this paper, by examining the scattering dynamics in the mouse brain in vivo via multispeckle diffusing wave spectroscopy (MSDWS) using a custom fiber probe that simulates a point-like source within the brain, we investigate the relationship between this decorrelation time and the depth of the point-like light source inside the living mouse brain at depths up to 3.2 mm

Eco-Friendly Management of Nausinoe Geometralis Through Botanical Extracts on Jasmine Plant

Jasmine leaf webworm, Nausinoe geometralis, is a significant pest of Jasminum spp. commonly known as Jasmine plant. This plant holds a special place in Pakistan\u27s culture; as it is declared as its national flower. N. geometralis feeds on the leaves of the jasmine plant; leaving it damaged and unattractive. Current study aimed to evaluate the efficacy of four botanical extracts (i.e. Neem, Taramira, Lemon grass, and Cactus) against N. geometralis; to explore an effective and eco-friendly method to protect the jasmine plant. Different concentrations of extracts were prepared using distilled water. Bioassays were performed on third instar larvae of N. geometralis following leaf dip method for various exposure intervals. Outcomes revealed that Neem extract was highly effective to manage the test insect pest followed by Taramira, Lemon grass, and Cactus. LC50 values of Neem after 24, 48, 72, and 96 hours were 22.25, 11.11, 11.31, and 15.82 ppm, respectively. It was concluded that botanical extracts can be utilized as promising agents in developing effective management strategies against N. geometralis. Future research should focus on optimizing the application methods and exploring the synergistic effects of these botanical extracts with other eco-friendly control measures to enhance their effectiveness against N. geometralis in field conditions