Efficient Body-Transfer Wheelchair for Assisting Functionally ImpairedPeople

Functionally impaired people always have difficulty accomplishing activities of daily living. In this regard, tasks including toileting and bathing have a higher prevalence rate of injuries and greater risk of falling. In this study, a body-transfer wheelchair was developed to assist people in transferring from bed to wheelchair for bathing, and toileting. The body-transfer wheelchair is a semi-automatic wheelchair that has features such as a controlled leg and backrest, linkage commode slot, and height adjustment. The wheelchair consists of a seat and a main frame that can be detached to enable bathtub transfer. This mechanism lets the user stay on the seat while being transferred into the bathtub without any risk of falling. A linkage mechanism was developed as a part of the seat for ease of toileting. Kinematic and force analysis was conducted to calculate the force required for each actuator. It has been proved by the experimental results that the wheelchair can securely and comfortably transfer a patient from the bed to the toilet or bathtub. A survey has been conducted to evaluate the wheelchair prototype design idea. Two focus groups were chosen: one comprised of functionally impaired people, and the other comprised of caregivers. The results of the survey show that 60% of both functionally impaired people and caregivers would like to use the body-transfer wheelchair for toileting and bathing purpose. Additionally, on average 65% of both focus groups find it convenient to operate the body-transfer wheelchair independently.     

Lake Level Prediction using Feed Forward and Recurrent Neural Networks

The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes time-series forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .

     

Immobilized titanium dioxide/powdered activated carbon system for the photocatalytic adsorptive removal of phenol

Titanium dioxide (TiO₂) and powdered activated carbon (PAC) were fabricated via a layer by layer arrangement on a glass plate using a dip-coating technique for the photocatalytic-adsorptive removal of phenol. Thinner TiO₂ layer coated on PAC sub-layer has larger surface area and better phenol removal than the thicker TiO₂ layer. The system obeyed the Langmuir isotherm model, which exhibited a homogeneous and monolayer adsorption with a maximum capacity of 27.8 mg g^-1. The intra-particle diffusion was the rate-limiting step as the linear plot crossed the origin, while the adsorption was unfavorable at elevated temperature. Under light irradiation, the TiO₂/PAC system removed phenol two-times more effectively than the TiO₂ monolayer due to the synergistic effect of photocatalysis by TiO₂ top layer and adsorption by PAC sub-layer. The COD removal of phenol was rapid for 10mg L^-1 of concentration and under solar light irradiation. It was shown that the PAC sub-layer plays a significant role in the total removal of phenol by providing the adsorption sites and slowing down the recombination rate of charge carriers to improve the TiO₂ photocatalytic oxidation performance.     

Physical and adsorptive characterizations of immobilized polyaniline for the removal of methyl orange dye

Synthesized polyaniline (PANI) powder mixed with ENR-PVC polymer blend adhesive was immobilized on glass plates for the adsorption of methyl orange (MO) dye. The immobilized PANI composite was made up of plasticized PANI aggregates and was in a doped state. The incorporation of ENR-PVC blend in PANI slightly reduced the surface area from 9.2 to 8.5 m² g^-1, and its presence was confirmed through FTIR. The adsorption process was highly dependent on the aeration rate, and the pH of MO solution in which 40 mL min^-1 and ambient pH (6.5) was selected as the working conditions. The process of MO uptake onto the immobilized PANI obeyed the pseudo-second-order kinetic model, while intra-particle diffusion was found to dominate the adsorption process. The q_m of the immobilized PANI was 77.3 mg g^-1 for MO uptake and obeyed the Freundlich isotherm model. The thermodynamic study revealed that the adsorption process of immobilized PANI was spontaneous and unfavorable at high temperature. The immobilized PANI was found to be comparable with other PANI based adsorbents in term of cost, recyclability and adsorption efficiency.