Growth of the king seerfish (Scomberomorus commerson) from the South East Coast of India

Length frequency data of Scomberomorus commerson collected from April 1984 to March 1987 from artisanal fisheries using three types of gill nets, hook and line, shore seine and shrimp trawls are analysed. Assuming that the length frequencies of combined gears will give distributions unaffected by selectivity data were pooled and analysed by the Bhattacharya method

Methods of shell cleaning and polishing

From very early time in the history of mankind molluscan shells have been used for various purposes. Primitive man who lived in the cave used shells as ornaments, vessels and weapons. Ancient tribes used conch and triton shells as trumpets to summon people

Effect of demonstration in transferring fish processing technology

A group of 28 fisherwomen who attended demonstration on three subjects, namely, preparation of fish wafers, fish pickles and fish soup powder showed significant knowledge and skill gain for all the three messages. The total knowledge and skill gain was maximum for preparation of fish wafers followed by that for preparation of fish soup powder and fish pickles

Relative retentivity of knowledge in fish processing by fisherwomen

Retention of knowledge in the preparation of fish pickle, fish wafers and fish soup powder for an experimental group of 20 fisherwomen selected from three fishing villages was studied. The knowledge retention immediately after exposure and also at intervals of 15 days and 30 days after exposure differed significantly

Analysis of Time-Based Public Transport Demand Prediction Using OPTUNA Framework

Buses are the most popular and easy mode of transportation in all over the world. The state government operates bus service in all routes with low-cost fare. Traffic congestion has risen at an alarming rate due to an increase in the number of automobiles. Travel time have increased as a result, while accessibility and mobility have worsened. The primary challenge encountered by passengers is the absence of information regarding bus numbers that are accessible on a certain route and the approximate time of bus departure.  The delay in bus operations, could have several reasons which are inclement weather, traffic jam, and breakdowns. Neither the arrival time of the bus nor the delay are known to the people waiting at the bus stop. In order to address this problem, encouraging the usage of public transportation seems to be a feasible way. Over the past ten years, prediction of bus arrival time has become a fascinating subject around the world. In the transportation sector, Machine Learning (ML) technologies have already shown great promise and have additionally shown to yield a larger return on investment than traditional methods. In this research work , authors propose and develop predictive models to predict public transport demand for passenger transit based on bus arrival time. The dataset shows the proportion of buses operated by the Rochester-Genesee Regional Transportation Authority (RGRTA) that arrive on time. Initially, lazy predict classifier is used for solving regression-based dataset for predicting the bus demand in On-time based passenger transit. Based on the examination of lazy classifiers, the Decision Tree Regressor (DTR) has been identified as the best model. It is assessed using the most advanced hyperparameter optimization framework (OPTUNA). The proposed OPTUNA based DTR which is utilized to identify On-time performance of bus services-based passenger transit. Using OPTUNA for search is an efficient and beneficial approach considering the search speed and the improvement in model accuracy. According to the experimental data, the proposed approach performs better where the R-squared score is 0.9878 with best hyperparameter to be optimized

Performance of Public Transport Appraisal using Machine Learning

Public passenger transport holds immense significance in the overall transportation system. Forecasting the movement of public transport has emerged as a crucial problem in transport planning due to its practical implications. Recently, there has been a lot of significant attention in Intelligent Transportation Systems (ITS), introducing various advancements and innovative applications to develop conditions for public transit that are safer, more effective, and fun. To fully leverage the potential of ITS applications and deal with road situations proactively, it becomes crucial to have a reliable method for predicting traffic flow. This opens up opportunities for ITS applications to anticipate and address potential challenges in advance. Enhancing the efficient functioning of Public Transport (PT) networks is a primary objective for urban area authorities, and the proliferation of location and communication devices has led to an abundance of operational data. Applying appropriate Machine Learning (ML) methods can help identify patterns in the data to improve the Schedule Plan. This research focuses on heterogeneous information that influences the prediction value, aiming to predict the required transport demand for specific routes and the arrival time of public transport. Utilizing DBSCAN clustering with SARIMA Algorithm, real-time passenger demand forecasting is extensively promoted to enhance dynamic bus scheduling and management. Furthermore, this paper compares the accuracy of the proposed Prophet Model with traditional time series models like ARIMA and SARIMA. The aim is to provide precise and robust passenger demand predictions, enabling more effective planning and management of PT services

Two Parallel Finite Queues with Simultaneous Services and Markovian Arrivals

In this paper, we consider a finite capacity single server queueing model with two buffers, A and B, of sizes K and N respectively. Messages arrive one at a time according to a Markovian arrival process. Messages that arrive at buffer A are of a different type from the messages that arrive at buffer B. Messages are processed according to the following rules: 1. When buffer A(B) has a message and buffer B(A) is empty, then one message from A(B) is processed by the server. 2. When both buffers, A and B, have messages, then two messages, one from A and one from B, are processed simultaneously by the server. The service times are assumed to be exponentially distributed with parameters that may depend on the type of service. This queueing model is studied as a Markov process with a large state space and efficient algorithmic procedures for computing various system performance measures are given. Some numerical examples are discussed

Study of the relative effectiveness of extension methods in educating fisherwomen

Three experimental groups from three different fishing villages were selected and administered with three extension treatments on two messages, namely, production of fish wafers and fish pickles. There was a significant knowledge gain in the subjects taught through different extension methods. It was observed that lecture aided with slides induced maximum knowledge followed by lecture aided with charts and lecture alone. Among all, the young and highly educated women gained maximum knowledge

Training needs of traditional marine fishermen in Kerala

Training needs of 80 fishermen in 25 subject areas revealed a mean training need score of 23.0l; 95% wanted to get trained. The training needs were fairly strong in all subject areas, with the highest demand being for fishery engineering. Training need was also high for areas related to fishery technology. Most of the fishermen preferred to have the training at their own village, and in the months of June or July for an average period of 20.85 days. Education and income were positively related to intensity of training needs whereas age, number of family members, number of employed family members and experience in fishing were negatively correlated with it. These six variables explained 27 of the variance in training need intensity

Waste to Energy

Incineration of municipal and industrial solid waste for the purpose of reducing the waste volume is not a new technology, but has not been used extensively in the United states. Landfi lls are the most common method of s olid waste disposal. Many of the existing nat ion's landfills are reaching their capacity and developing new landfills is becoming increasingly expensive. Municipalities and industries are now investigating the use of solid waste incinerators and some have constructed and started operation of these facilities. To help to stabilize or reduce the costs of these f a cilities, heat from the burning waste is used to generate steam and electricity.Industrial Engineering and Managemen