The advancement of 3D-printing technology has ushered in a new era in the production of machine components, building materials, prototypes, and so on. In 3D-printing techniques, the infill reduces the amount of material used, thereby reducing the printing time and sustaining the aesthetics of the products. Infill patterns play a significant role in the property of the material. In this research, the mechanical properties of specimens are investigated for gyroid, rhombile, circular, truncated octahedron, and honeycomb infill structures (hexagonal). Additionally, the tensile properties of PLA 3D-printed objects concerning their infill pattern are demonstrated. The specimens were prepared with various infill patterns to determine the tensile properties. The fracture of the specimen was simulated and the maximum yield strengths for different infill structures and infill densities were determined. The results show the hexagonal pattern of infill holds remarkable mechanical properties compared with the other infill structures. Through the variation of infill density, the desired tensile strength of PLA can be obtained based on the applications and the optimal weight of the printed parts.

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

The growing population in urban areas generates large volumes of hospital waste which intensifies the problem of hospital waste management in developing countries. This study is designed to evaluate environmental impacts associated with hospital waste management scenarios using life cycle assessment (LCA) approach. Two scenarios were designed to describe the current practices: (scenario A) and an integrated approach (scenario B), which includes segregation and recycling of hospital waste. Data were collected from five public hospitals located in the district of Sheikhupura, Pakistan. The collected hospital waste was quantified and categorized on a daily basis for five consecutive months (October 2020 to February 2021). The functional unit was defined as one tonne of hospital waste. System boundaries for two scenarios include segregation, transportation, treatment and disposal of hospital waste. After defining functional unit and system boundaries, LCA was conducted using the IGES-GHG simulator. The scenarios were evaluated using common parameter, i.e., greenhouse gas (GHG) emissions. Scenario A and scenario B resulted in net GHG emissions of 1078.40 kg CO₂-eq. per tonne of waste and 989.31 kg CO₂-eq. per tonne of waste, respectively. Applying an integrated approach, it would be possible to mitigate GHG emissions of 37,756.44 kg CO₂-eq. per tonne of waste annually and to recover some materials such as glass, paper, plastic and metals. Therefore, implementing an integrated approach for the management of hospital waste will progress the entire system towards sustainability. The findings of this study can be used for future research and policymaking.

Utilizing electrostatic precipitators (ESPs) is an efficient particle removal method that sees a wide usage in industrial environments. This is mainly because of the low drop of the pressure flow, while retaining high collection efficiency, alongside being cost-effective. This paper reviewed previous works concerning optimizing the performance of single- and multi-stage ESPs by changing several design parameters and evaluating the effects on different performance indices, such as the corona power ratio, current-voltage characteristics, and overall collection efficiency. The review then goes through several modelling methodologies, showcasing their shortcomings and developments, as well as the relationship between the electrohydrodynamic (EHD) flow and the precipitation performance. The performance effects of using different electrode configurations and designs in terms of the number of electrodes, relative dimensions, spacings, channel lengths, and overall design were also reviewed.