Search Results (9)

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

The layer-by-layer deposition of molten polymer filament in fused deposition modeling (FDM) has evolved as a disruptive technology for building complex parts. This technology has drawbacks such as the anisotropic property of the printed parts resulting in lower strength for parts printed in the vertical Z direction compared with the other two planes. In this manuscript, we attempt to address these challenges as well as the lack of standardization in sample preparation and mechanical testing of the printed parts. The paper focuses on process parameters and design optimization of the ZX build orientation. Type I tensile bars in ZX orientation were printed as per the ASTM D638 standard using two (2B) and four (4B) tensile bar designs. The proposed design reduces material loss and post-processing to extract the test coupons. Printing a type I tensile bar in the ZX orientation is more challenging than type IV and type V due to the increased length of the specimen and changes in additional heat buildup during layer-by-layer deposition. Three different polymer composite systems were studied: fast-crystallizing nanofiller-based high-temperature nylon (HTN), slow-crystallizing nanofiller-based polycyclohexylene diethylene terephthalate glycol-modified (PCTG), and amorphous carbon fiber-filled polyetherimide (PEI-CF). For all the polymer composite systems, the 2B showed the highest strength properties due to the shorter layer time aiding the diffusion in the interlayers. Further, rheological studies and SEM imaging were carried out to understand the influence of the two designs on fracture mechanics and interlayer bonding, providing valuable insights for the field of additive manufacturing and material science. Full article

Hydrazine oxidation in single-atom catalysts (SACs) could exploit the efficiency of metal atom utilization, which is a substitution for noble metal-based electrolysers that results in reduced overall cost. A well-established ruthenium single atom over mesoporous carbon nitride (SRu-mC₃N₄) catalyst is explored for the electro-oxidation of hydrazine as one of the model reactions for direct fuel cell reactions. The electrochemical activity observed with linear sweep voltammetry (LSV) confirmed that SRu-mC₃N₄ shows an ultra-low onset potential of 0.88 V vs. RHE, and with a current density of 10 mA/cm² the observed potential was 1.19 V vs. RHE, compared with mesoporous carbon nitride (mC₃N₄) (1.77 V vs. RHE). Electrochemical impedance spectroscopy (EIS) and chronoamperometry (i-t) studies on SRu-mC₃N₄ show a smaller charge-transfer resistance (R_Ct) of 2950 Ω and long-term potential, as well as current stability of 50 h and 20 mA/cm², respectively. Herein, an efficient and enhanced activity toward HzOR was demonstrated on SRu-mC₃N₄ from its synergistic platform over highly porous C₃N₄, possessing large and independent active sites, and improving the subsequent large-scale reaction. Full article

Thin-layer convective drying of plantain banana was performed at four different temperatures from 50 to 80 °C, with slice thicknesses from 2 to 8 mm. The drying curves, fitted to seven different semi-empirical mathematical models, were successfully used to fit experimental data (${\mathrm{R}}^2$ 0.72–0.99). The diffusion approach had better applicability in envisaging the moisture ratio at any time during the drying process, with the maximum correlation value (${\mathrm{R}}^2$ 0.99) and minimum value of $x^2$ (2$.5\times {10}^{-5}\mathrm{to}1.5\times {10}^{-4})$ and RMSE (5.0 $\times {10}^{-3}\mathrm{to}1.2\times {10}^{-2})$. The $D_{eff}$, $h_m$, and $E_a$ values were calculated on the basis of the experimental data, and overall ranged from $1.11\times {10}^{-10}$ to $1.79\times {10}^{-9}$ m² s⁻¹, $3.17\times {10}^{-8}$ to 2.20 $\times {10}^{-7}$ m s⁻¹ and 13.70 to 18.23 kJ mol⁻¹, respectively. The process energy consumption varied from 23.3 to 121.4 kWh kg⁻¹. The correlation study showed that the drying temperature had a close correlation with $h_m$ value and sample hardness. A significant (p < 0.05) increase in hardness of dried plantain banana was observed at 80 °C compared to the other temperatures. Additionally, the sample hardness and process energy consumption were more positively correlated with the thickness of the samples. Full article

Onosma bracteata Wall. is an important medicinal and immunity-enhancing herbs. This plant is commonly used in the preparation of traditional Ayurvedic drugs to treat numerous diseases. Inspired by the medicinal properties of this plant, the present study aimed to investigate the antiproliferative potential and the primary molecular mechanisms of the apoptotic induction against human osteosarcoma (MG-63) cells. Among all the fractions isolated from O. bracteata, ethyl acetate fraction (Obea) showed good antioxidant activity in superoxide radical scavenging assay and lipid peroxidation assay with an EC₅₀ value of 95.12 and 80.67 µg/mL, respectively. Silica gel column chromatography of ethyl acetate (Obea) fraction of O. bracteata yielded a pure compound, which was characterized by NMR, FTIR, and HR-MS analysis and was identified as 1,2-benzene dicarboxylic acid, bis (2-methyl propyl) ester (BDCe fraction). BDCe fraction was evaluated for the antiproliferative potential against human osteosarcoma MG-63, human neuroblastoma IMR-32, and human lung carcinoma A549 cell lines by MTT assay and exhibited GI₅₀ values of 37.53 μM, 56.05 μM, and 47.12 μM, respectively. In MG-63 cells, the BDCe fraction increased the level of ROS and simultaneously decreased the mitochondria membrane potential (MMP) potential by arresting cells at the G₀/G₁ phase, suggesting the initiation of apoptosis. Western blotting analysis revealed the upregulation of p53, caspase3, and caspase9 while the expressions of p-NF-κB, p-Akt and Bcl-xl were decreased. RT-qPCR studies also showed upregulation in the expression of p53 and caspase3 and downregulation in the expression of CDK2, Bcl-2 and Cyclin E genes. Molecular docking analysis displayed the interaction between BDCe fraction with p53 (−151.13 kcal/mol) and CDK1 (−133.96 kcal/mol). The results of the present work suggest that the BDCe fraction has chemopreventive properties against osteosarcoma (MG-63) cells through the induction of cell cycle arrest and apoptosis via Akt/NF-κB/p53 pathways. This study contributes to the understanding of the utilization of BDCe fraction in osteosarcoma treatment. Full article

Due to limited availability of diagnostics and capacity, people with tuberculosis do not always undergo systematic assessment for severe illness (requiring inpatient care). In Karnataka (south India), para-medical programme staff used a screening tool to identify people at ‘high risk of severe illness’, defined using indicators of very severe undernutrition, abnormal vital signs and poor performance status (any one): (i) body mass index (BMI) ≤ 14.0 kg/m² (ii) BMI ≤ 16.0 kg/m² with bilateral leg swelling (iii) respiratory rate > 24/min (iv) oxygen saturation < 94% (v) inability to stand without support. Of 3020 adults notified from public facilities (15 October to 30 November 2020) in 16 districts, 1531 (51%) were screened (district-wise range: 13–90%) and of them, 538 (35%) were classified as ‘high risk of severe illness’. Short median delays in screening from notification (five days), and all five indicators being collected for 88% of patients, suggests the feasibility of using this tool in programme settings. However, districts with poor screening coverage require further attention. To end tuberculosis deaths, screening should be followed by referral to higher facilities for comprehensive clinical evaluation, to assess the need for inpatient care. Future studies should assess the validity (especially sensitivity in picking severely ill patients) of this screening tool. Full article

The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation, and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the detection of objects at short and long distances. As both the sensors are capable of capturing the different attributes of the environment simultaneously, the integration of those attributes with an efficient fusion approach greatly benefits the reliable and consistent perception of the environment. This paper presents a method to estimate the distance (depth) between a self-driving car and other vehicles, objects, and signboards on its path using the accurate fusion approach. Based on the geometrical transformation and projection, low-level sensor fusion was performed between a camera and LiDAR using a 3D marker. Further, the fusion information is utilized to estimate the distance of objects detected by the RefineDet detector. Finally, the accuracy and performance of the sensor fusion and distance estimation approach were evaluated in terms of quantitative and qualitative analysis by considering real road and simulation environment scenarios. Thus the proposed low-level sensor fusion, based on the computational geometric transformation and projection for object distance estimation proves to be a promising solution for enabling reliable and consistent environment perception ability for autonomous vehicles. Full article

Augmented reality (AR) systems are becoming next-generation technologies to intelligently visualize the real world in 3D. This research proposes a sensor fusion based pipeline inspection and retrofitting for the AR system, which can be used in pipeline inspection and retrofitting processes in industrial plants. The proposed methodology utilizes a prebuilt 3D point cloud data of the environment, real-time Light Detection and Ranging (LiDAR) scan and image sequence from the camera. First, we estimate the current pose of the sensors platform by matching the LiDAR scan and the prebuilt point cloud data from the current pose prebuilt point cloud data augmented on to the camera image by utilizing the LiDAR and camera calibration parameters. Next, based on the user selection in the augmented view, geometric parameters of a pipe are estimated. In addition to pipe parameter estimation, retrofitting in the existing plant using augmented scene are illustrated. Finally, step-by-step procedure of the proposed method was experimentally verified at a water treatment plant. Result shows that the integration of AR with building information modelling (BIM) greatly benefits the post-occupancy evaluation process or pre-retrofitting and renovation process for identifying, evaluating, and updating the geometric specifications of a construction environment. Full article

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. Full article