Exploration has shifted from traditional materials and alloys to composite materials in recent years to develop lightweight, high-effective materials for specific purposes. Natural fibres are less costly, biodegradable, and nonflammable than glass fibres. This study explores how titanium oxide affects woven polyester reinforced composite’s mechanical and physical characteristics. Nanocomposites were created by hand utilizing the following terms: (i) TiO2 nanoparticle filler weight ratio, (ii) fibre content, and (iii) fibre diameter, all at three unique levels. Using the L9 (33) orthogonal design, nine composite samples are generated and tested according to the ASTM standard. According to the research, hybrid composites containing 4% titanium oxide powder and 15 mm length of bamboo fibre with 0.24 mm of bamboo fibre diameter have high mechanical strength. Adding fibre to pristine polyester increased its mechanical properties. As the fibre and filler percentages grew, more effort was required to break the fragments between the matrix and its resin. The verification test, which uses the optimal processing value and grey relational analysis, outperforms the real test results by a wide margin. Tensile strength increased by 14.76%, flexural strength increased by 14.07%, and hardness increased by 25.55%.

Millions of people are dying and billions of properties are damaged by road tra c accidents each year worldwide. In the case of our country Ethiopia, the effect of tra c accidents is even more by causing injuries, death, and property damage. Forecasting Road Tra c Accident and Predicting the severity of Road Tra c accident contributes a role indirectly in reducing road tra c accidents. This Study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using Deep Arti cial Neural Network models. Around 6170 Road Tra c accidents data are collected from Oromia Police Commission Excel data and Oromia Special zone Tra c Police Department, the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model and 1482 or (20%) of the dataset was used for the testing model. This study proposed Six different Neural Network architectures such as Backpropagation Neural Network (BPNN), Feed Forward Neural Network (FFNN), Multilayer Perceptron Neural Network (MLPNN), Recurrent Neural Networks (RNN), Radial Basis Function Neural Network (RBFNN) and Long Short-Term Memory (LSTM) models for accident severity predictionand The LSTM model for a time serious forecasting of number accidents within speci ed years. The models will take input data, classify accidents, predicts the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy Accident Severity prediction. According to the model performance results RNN model showed the best prediction accuracy of 97.18% whereas MLP , LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, 77.26%, respectively. LTSM model forecasted accident for Three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road tra c accidents. accident accounted 2.1% of all deaths, this makes them the 11 th leading cause of global deaths.Road tra c injuries place a heavy burden on household nances, not only on global and national economies. Many families are driven deeper into poverty by the loss of breadwinners and the added burden of caring for members disabled by road tra c injuries.The Global status report on road safety, launched by the World Health Organization (WHO) in December 2018, highlights that the number of road tra c deaths has reached 1.35 million annually [2]. And Road tra c crashes cost most countries 3% of their gross domestic product. It is supposed that 30 to 50 million people are exposed to physical disability annually by tra c accidents in the world according to the World Health Organization (WHO) reported in 2012. This report included that more than 600-billiondollar property can be damaged by tra c accidents annually.Road tra c injuries are currently estimated to be the 9 th leading cause of death across all age gro...

Millions of people are dying and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. Forecasting Road Traffic Accident and Predicting the severity of Road Traffic accident contributes a role indirectly in reducing road traffic accidents. This Study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using Deep Artificial Neural Network models. Around 6170 Road Traffic accidents data are collected from Oromia Police Commission Excel data and Oromia Special zone Traffic Police Department, the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model and 1482 or (20%) of the dataset was used for the testing model. This study proposed Six different Neural Network architectures such as Backpropagation Neural Network (BPNN), Feed Forward Neural Network (FFNN), Multilayer Perceptron Neural Network (MLPNN), Recurrent Neural Networks (RNN), Radial Basis Function Neural Network (RBFNN) and Long Short-Term Memory (LSTM) models for accident severity prediction and The LSTM model for a time serious forecasting of number accidents within specified years. The models will take input data, classify accidents, predicts the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy Accident Severity prediction. According to the model performance results RNN model showed the best prediction accuracy of 97.18% whereas MLP , LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, 77.26%, respectively. LTSM model forecasted accident for Three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road traffic accidents.

India experiences severe weather events around the year. Severe thunderstorms occur during the premonsoon season (March-May), cyclonic storms occur over the Bay of Bengal/Arabian Sea during the premonsoon and postmonsoon seasons (October-December), and heavy rainfall occurs during the monsoon season (June-September). It causes a lot of damage to property and the lives of humans. Smart urban areas aim to improve residents’ personal satisfaction by utilizing data about urban scale procedures separated from heterogeneous information sources gathered on city-wide arrangements using sensors. The Internet of Things (IoT) is an empowering concept for forecasting weather situations on an urban scale. A multisource detecting framework influences IoT innovation to accomplish city-scale detection of climatic changes and forecasts them to citizens in a smart city. The existing models gather the data in an unstructured process that need to be structured. This is a complex task that needs to be reduced. The proposed model gathers data from various regions in a city and uses it to identify the weather regions. A novel approach is introduced to detect this climate information gathered by utilizing various sensors arranged in a city. The information gathered from the sensors is thoroughly examined to see if there is any inconsistency in weather reports in any of its key hubs, and cautions are activated to the city for prompt actions. In this proposed work, an efficient method for weather casting using an IoT mechanism is introduced, and the results state that the proposed method is effective in terms of accuracy and speed when contrasted with the traditional methodologies.

No construction activity can be conceived in the current context without concrete. A popular method is to manufacture concrete from a mixture of three ingredients: aggregates, cement, and water. Because of poor construction materials, many structures deflect prematurely and excessively. Another major worry in the building business is the cost of materials required to make concrete. As a result, adding other suitable components (known as additives) in a specific proportion to boost concrete strength is a regular requirement. Teff agriculture is more prevalent in the study region (Ambo Town), as Enjera is a common Ethiopian delicacy made from Teff. Nanofiber-based Teff Straw production from Teff agricultural fields is in excess, and it was not being used for anything other than feeding cattle, donkeys, and other animals. As a result, farmers use the unfavorable habit of burning surplus Nanofiber-based Teff Straw, resulting in environmental pollution issues such as carbon footprint. Furthermore, the natural Nanofiber-based Teff Straw is extremely strong, used to blend nanoparticles, and it may be useful in overcoming general structural problems while also being cost-effective for local building businesses. In light of this, the current research focuses on an experimental assessment of the applicability of Nanofiber-based Teff Straw as an extra concrete material in concrete mixes. The typical mix for C25 concrete has been designed to achieve a target average strength of 28 MPa with a liquid (water)-cement ratio (l-c ratio) of 0.50 and a slump range of 20-50 mm. All Nanofiber-based Teff Straw reinforced concrete beam samples failed due to pure flexural failure, whereas plane concrete beams failed due to beam crushing. With the addition of Nanofiber-based Teff Straw to concrete, the mean flexural strength increased by 19.38 percent, 4.19 percent, and 0.66 percent, respectively, with M1, M2, and M3 adding up this particular ingredient by the weight of concrete. As a result, adding Nanofiber-based Teff Straw to concrete increased its bending strength when compared to ordinary concrete. Slump reduction effects of 20.00 percent, 40.00 percent, and 50.00 percent were seen for mix designs M1, M2, and M3 when Nanofiber-based Teff Straw was added to the concrete weight. Finally, due to volume addition of fresh concrete with Nanofiber-based Teff Straw, fresh concrete densities were reduced by 2.00 percent, 2.32 percent, and 2.84 percent, respectively.