Influence of mastication and its relationship with Body Mass Index before and after prosthetic rehabilitation in partially edentulous patients

BACKGROUND: The main purpose of prosthetic rehabilitation is to enhance the masticatory function by replacing the missing teeth with an artificial substitute, which improves nutrient-rich food intake. There are recent studies which indicate the influence of chewing behavior and energy intake, but little is known about the relationship between chewing on nutritional status. OBJECTIVE: This study intended to assess the changes in masticatory efficiency before and after prosthetic rehabilitation and its influence on nutritional status and body weight. METHODS: A total of 40 partially edentulous subjects aged between 45- 65 years were recruited. Body Mass Index was determined by measuring body weight using a medical grade weighing scale. Height was measured using wall mounted stature meter and Waist circumference was measured with an anthropometric measure tape. Masticatory efficiency was determined using the sieve method with peanuts as test food at baseline, at 3 and 6 months of prosthetic rehabilitation with a removable partial denture. RESULTS: Sieve test performed for evaluating masticatory efficiency showed an increase in the percentage of smaller particles by 28.3% in non-obese and 32.15% in the obese group. The obese/overweight group showed a decrease in BMI values and non-obese subjects showed no significant change in BMI. CONCLUSION: The study concluded that improving masticatory efficiency by prosthodontic rehabilitation can aid in normalizing the nutritional status in certain partially edentulous non-obese and obese individuals

Drain versus no drain in an uncomplicated elective laparoscopic cholecystectomy- an institutional study

Background: Laparoscopic cholecystectomy (LC) is the gold standard for symptomatic gallstones. Post surgery to keep a subhepatic drain is an issue of debate. A randomised trial was designed to assess the outcome of drain in elective lap cholecystectomy.Methods: A randomized control trial was done from January 2019 to June 2020 among 40 patients. They were randomised into group A: (n=20) in which subhepatic space was drained by an abdominal drain size 28F drain which was brought out through right anterior axillary port (even group) and group B: (n=20) in which there was no-drain at sub hepatic space (odd group). The end points of this study was to compare postoperative pain, fever, wound infection ,hospital stay between the two groups.Results: Mean hospital stay among drain group was 3.95±1.35 days as compared to 2.55±0.60 days among no drain group and the difference was statistically significant (p value =0.001). 8 (40%) patients with drain had port side infection as compared to 1 (5%) patient among no drain group and the observed difference was statistically significant (p value =0.02). Post operative pain abdomen assessed using VAS, and found significant 12 after surgery. The young female patients were unhappy with the drain scar and 3 cases requested for need of plastic surgery corrections also.Conclusions: The routine use of a drain in uncomplicated elective laparoscopic cholecystectomy has no benefit; in contrast, it is associated with longer hospital stay, so better to avoid the drain

Trends in Summer-Time Tropospheric Ozone during COVID-19 Lockdown in Indian Cities Might Forecast a Higher Future Risk

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Spatial distribution of outbreak of locust swarms: a geographical analysis of vulnerability and preventions in India

Current outbreak of locust swarms in the different parts of the world has also emerged as a big problem for the Indian agricultural sector and associated livelihood options. Being known for its transient nature, the locust swarms containing millions of locusts. These swarms are, therefore, one of the most dangerous pests in the world that may have a disastrous impact on food, food crops, fodder and food security around the world. The outbreak has been historically noticed in several regions of the world which effected the agriculture system of the many countries and major cause for the slowdown in the economy. This paper examines the origin and migration trends of locust swarms in the world in general and India in particular.  Also paper evaluates the recent outbreak of locusts in India along with assessing its devastating impact on Indian Agricultural Sector and the track routes of the swarms in India in different months. In the end, the paper highlights preventive measures that have been used in monitoring and preventions of locust swarms. The outbreaks of locust in India is not new, but have been encountered in the past too. The intensity and number of hives and migratory frequency is increasing with time. The Area, magnitude and impacts of the locust swarms is also growing with time and space. Since the outbreak results in social, economic and environmental consequences, therefore, adequate measures and planning are required to tackle the crisis.Keywords:  Locusts, Swarms, Vulnerability, Prevention Measures, Spatial distributio

Empirical model for estimation of global solar radiation at lowland region Biratnagar using satellite data

This study proposes to find the regression coefficient of the modified Angstrom type model for the estimation of global solar radiation (GSR) in lowland Biratnagar (Lat. 26.5º N, Long. 87.3º E and Alt. 72m) using relative sunshine duration and satellite data of GSR. Using the regression technique, the empirical constants 0.29 and 0.56 are found in the modified Angstrom model. Furthermore, the Modified Angstrom model along with other linear models such as Glover and McCulloch model, Page model, Rietveld model, and Turton's model are statistically assessed to evaluate the significance of models. Statistical tests like MPE, MBE, RMSE, and CC reveal that all these models are statistically significant. These findings can be utilized for other locations with a high confidence level at the similar climatic locations of Nepal. BIBECHANA 18 (1) (2021) 193-20

Empirical model for estimation of global solar radiation at lowland region Biratnagar using satellite data

This study proposes to find the regression coefficient of the modified Angstrom type model for the estimation of global solar radiation (GSR) in lowland Biratnagar (Lat. 26.5º N, Long. 87.3º E and Alt. 72m) using relative sunshine duration and satellite data of GSR. Using the regression technique, the empirical constants 0.29 and 0.56 are found in the modified Angstrom model. Furthermore, the Modified Angstrom model along with other linear models such as Glover and McCulloch model, Page model, Rietveld model, and Turton's model are statistically assessed to evaluate the significance of models. Statistical tests like MPE, MBE, RMSE, and CC reveal that all these models are statistically significant. These findings can be utilized for other locations with a high confidence level at the similar climatic locations of Nepal. BIBECHANA 18 (1) (2021) 193-20

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system

Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis

Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning model

Spatial distribution of outbreak of locust swarms: a geographical analysis of vulnerability and preventions in India

Current outbreak of locust swarms in the different parts of the world has also emerged as a big problem for the Indian agricultural sector and associated livelihood options. Being known for their transient nature, locust swarms contain millions of locusts.These swarms are; therefore, pose danger to the world because of their disastrous impact on food crops, fodder and food security around the world. The outbreak has been historically noticed in several regions of the world where it has affected the agriculture system of the many countries and has caused a major cause of the slowdown of the economy. This paper examines the origin and migration trends of locust swarms in the world in general and India in particular. Also, the paper evaluates the recent outbreak of locusts in India by assessing its devastating impact on the Indian Agricultural Sector and the track routes of the swarms in India in different months. The paper highlights preventive measures that have been used in monitoring and control of locust swarms. The outbreaks of locust in India is not new, but has been encountered in the past. The intensity and migratory frequency is increasing with time. The area, magnitude and impacts of the locust swarms is also growing with time and space. Since the outbreak results in social, economic and environmental consequences, therefore, adequate measures and planning are required to tackle the crisis.El brote actual deenjambres de langostas en las diferentes partes del mundo también se ha convertido en un gran problema para el sector agrícola indio y las opciones de sustento asociadas. Siendo conocido por su naturaleza transitoria, la langosta enjambres que contienen millones de langostas. Estos enjambres son, por lo tanto, una de las plagas más peligrosas del mundo que pueden tener un impacto desastroso en los alimentos, los cultivos alimentarios, el forraje y la seguridad alimentaria en todo el mundo. El brote se ha observado históricamente en varias regiones del mundo que afectaron el sistema agrícola de muchos países y la principal causa de la desaceleración de la economía. Este documento examina las tendencias de origen y migración de los enjambres de langostas en el mundo en general y en la India en particular. También el papel evalúa el reciente brote de langostas en la India junto con la evaluación de su impacto devastador en el sector agrícola indio y las rutas de seguimiento de los enjambres en la India en diferentes meses. Al final, el documento destaca las medidas preventivas que se han utilizado en el monitoreo y prevención de enjambres de langostas. Los brotes de langosta en la India no son nuevos, pero también se han encontrado en el pasado. La intensidad y el número de colmenas y la frecuencia migratoria aumentan con el tiempo. El área, la magnitud y los impactos de los enjambres de langostas también está creciendo con el tiempo y el espacio. Dado que el brote tiene consecuencias sociales, económicas y ambientales, por lo tanto, se requieren medidas y planificación adecuadas para hacer frente a la crisis