HTW Berlin - University of Applied Sciences

Chaudhary R., Jaganathan V.M., Glivin G., Joseph Sekhar S., Assis S.M.

About 50–60% of the average daily water consumption by humans is excreted as urine. It is high time to reconsider the aversion to such wastes and change the perception to view them as wealth instead of waste. In this line, in the current study, we have explored the possibility of using human urine as one of the feedstocks for biogas plants. In the current work, human urine is partially/fully replaced with water, and the potential for generating biogas and manure rich in nitrogen is studied. The purpose of using human urine in biogas system is two folds. (1) It is generally a waste disposed of through sewage lines, which initiates aerobic reactions in lines leading to a fouling smell and becomes pathogenic, and the maintenance of sewage lines is difficult and cost ineffective and (2) Human urine consists of nitrogen, phosphorous, and potassium in the following ratio as 11 parts, 1 part and 2.5 parts, which can improvise the biogas yield and manure quality as nitrogen present in the human urine work as the micronutrient for the feedstock degrading bacteria. Urine has a pH that varies from 4 to 8 and a high nitrogen content. It is a waste, and it is currently disposed of without any treatment in water bodies or barren lands. Various combinations of cow dung, food waste, urine, and water were mixed and studied in a lab-scale batch-type reactor. Ten such combinations with three samples for each combination were taken to check the repeatability and reproducibility of the results. The biogas yield is accessed for a retention period of 35 days and the yield is measured using the gas displacement method. Out of the ten combinations, a mixing ratio of 1:1:3 (rice waste, cow dung, and human urine) is found to be optimal with respect to maximum gas yield (230 l/kg) and manure fertiliser (Nitrogen, Phosphorus, and Potash) values. The optimum sample is again checked for gas yield, and the results are consistent with earlier experiments. The gas composition was measured, and it was found to be 62.10% methane and 37.90% carbon dioxide, which is in good agreement with the well-performing state-of-the-art biogas plants at present. Slurries derived from these studied samples, with optimal N-P-K values, can also be utilised for optimal growth and productivity in plants.

Minimizing energy consumption and maximizing network longevity through efficient data transmission are open research problems in practical wireless sensor networks. The proposed work focuses on data transmission based on two methods: Compressive Sensing (CS) and Partial Canonical Identity matrix-based Compressive Sensing (PCI-CS). The performance of these two methods on different energy-efficient clustering and routing protocols, such as Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), and Threshold sensitive Energy-Efficient Sensor Network (TEEN) is compared. The combination of CS or PCI-CS, along with these energy-efficient clustering and routing protocols, can immensely reduce energy consumption. Simulation results show that the PCI-CS method outperforms other methods in terms of energy efficiency as well as network longevity.

Compressed sensing is a signal processing technique that is used for the efficient acquisition and reconstruction of signals by finding solutions to under-determined linear systems. In most cases, the compressed signal is recovered using the conventional $$l_{1}$$ norm minimization technique, which is a convex optimization procedure. However, the global minimum is not necessarily the sparsest solution. Therefore, another method called sparse Bayesian learning (SBL) is introduced for the reconstruction of compressed audio signals. Using the different SBL algorithms, a better reconstruction for the compressed audio signal is achieved. The SBL algorithm for the multiple measurement vector (MMV) model is also implemented for the audio signal. The results of different SBL techniques (SBL, T-SBL, MSBL, T-MSBL) are compared. We show via the experimental results that the time-varying model outperforms the other techniques. Further, we show that the T-MSBL algorithm reconstructed the original audio signal from the compressed signal more efficiently than the other algorithms.

Carbon nanodots (CNDs) have garnered significant attention as viable drug delivery vehicles in recent years, especially in the field of phytomedicine. Although there is much promise for therapeutic applications with phytomedicine, its effectiveness is frequently restricted by its low solubility, stability, and bioavailability. This paper offers a thorough synopsis of the developing field of phytomedicine drug delivery based on CND. It explores CND synthesis processes, surface functionalization strategies, and structural and optical characteristics. Additionally, the advantages and difficulties of phytomedicine are examined, with a focus on the contribution of drug delivery methods to the increased effectiveness of phytomedicine. The applications of CNDs in drug delivery are also included in the review, along with the mechanisms that underlie their improved drug delivery capabilities. Additionally, it looks at controlled-release methods, stability augmentation, and phytomedicine-loading tactics onto CNDs. The potential of polymeric carbon nanodots in drug delivery is also covered, along with difficulties and prospective directions going forward, such as resolving toxicity and biocompatibility issues. In summary, the present review highlights the encouraging contribution of CNDs to the field of drug delivery, specifically in enhancing the potential of phytomedicine for therapeutic purposes.

The optimization of critical parameters to enhance solar cell efficiency has been made possible by the use of SCAPS-1D modeling software, which has facilitated the exhaustive analysis of device performance under a variety of operating conditions. The SCAPS-1D software is utilized in this investigation to simulate and optimize heterojunction perovskite solar cells (PSCs) with a proposed configuration of FTO/ZnOS/CsSnBr3/Cs2SnI6. The bilayer absorption scenario is expected to facilitate the efficient absorption of the solar spectrum and the enhancement of the stability and efficiency of PSCs. The performance of absorbers is assessed using a variety of factors, including absorption thickness, work function, working temperature, defect density, series, and shunt resistance (Rs, RSH). The optimization of the physical factors substantially enhanced the overall performance capacity for the CsSnBr3/Cs2SnI6-based devices. The optimized device exhibited outstanding performance, achieving a fill factor (FF) of 81.98%, an open-circuit voltage (VOC) of 1.24 V, a short-circuit current density (JSC) of 19.09 mA/cm2, and an impressive power conversion efficiency (PCE) value of 19.44%. These simulation models illustrate the exceptional potential of the novel lead-free heterojunction structure for highly stable and efficient PSCs.

AbstractThe growing environmental consciousness and research into green lubricant technology in various industries has drawn a lot of attention to vegetable oils. The major negative aspect of vegetable oil is its poor oxidation stability. Recent research in ionic liquids (ILs) showed its potential as a base fluid and additive for lubricant applications. These are organic salts that dissolve at temperatures below 100°C. Among the potential characteristics of ILs are their non‐volatility, high oxidation stability, non‐flammability, and high conductivity to heat and electricity. The present study synthesized a protic ionic liquid named dioctyl ammonium oleate (DAO). The optimum DAO percentage in rice bran oil (RBO) was then optimized based on the tribological properties. The electrical conductivity and basic lubricant properties, such as physicochemical, oxidation stability, corrosion stability, thermal properties, and extreme pressure capability of the optimized RBO‐DAO blend were evaluated and compared with RBO. The developed RBO‐DAO blend shows the potential to be used as a base fluid for electrical conductive lubricants due to its significantly enhanced electrical conductivity, better oxidation stability, and superior tribological properties. The optimum DAO weight percentage in RBO is 2 wt%. The results show the potential of electrical conductive lubricant developed to enhance the performance of electrical equipment, reduce static electricity in industrial machinery, and prevent lubricant degradation in the presence of electric current. The components of electric vehicles, such as bearings, pads, seals, and gear, also require electrical conductive lubricants to avoid premature failure and electromagnetic interference problems. The developed lubricant can be considered a potential alternative in various industries such as automotive, renewable systems, medical devices, robotics, high‐speed machining, and so forth.

The vulnerability of Reinforced Concrete (RC) structures against seismic events has prompted extensive research into retrofitting techniques aimed at enhancing their seismic performance. Among these, Fabric-Reinforced Cementitious Matrix/Mortar (FRCM) systems have gained prominence as promising solutions for strengthening RC-columns. This study presents a comprehensive investigation into the seismic strengthening of RC columns using FRCM, combining experimental and numerical approaches to assess their effectiveness. The experimental phase of this research involved the fabrication of scaled RC-column specimens representing real-world conditions. These columns were subjected to a series of cyclic loading tests to simulate seismic forces. Multiple FRCM configurations, including different fiber types and dosages, were applied to these specimens. The experimental results revealed a substantial increase in the ductility, stiffness, and ultimate strength of the strengthened RC-columns, indicating the potential of FRCM systems as effective seismic retrofit solutions. In parallel, a numerical analysis was conducted using Finite Element Modeling (FEM) to simulate the behavior of the strengthened RC-columns under seismic loading conditions. The FEM simulations were validated against the experimental data, demonstrating good agreement. This numerical investigation allowed for a more in-depth understanding of the stress distribution and deformation patterns within the strengthened columns, aiding in the optimization of FRCM reinforcement strategies. The integrated experimental and numerical investigation presented in this study contributes valuable insights into the seismic strengthening of RC-columns using FRCM systems. It provides a holistic understanding of their performance, including their enhanced load-carrying capacity, as well as improved ductility guiding the adoption of FRCM systems as a viable solution for mitigating seismic risk in existing RC-structures.

This study presents a feasibility assessment of solar photovoltaic and wind energy systems for irrigation at three locations (a coastal lowland location named A, a midland location B, and a highland location C) in Thiruvananthapuram district, Kerala, in India’s southwest region. The solar and wind potentials are determined using the National Solar Radiation Database and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis data sets, respectively. The technical analyses suggest that site B has the most solar energy potential, while site C has the highest wind energy potential. However, the economic feasibility study reveals that the only suitable location is site B, which has a 10-year simple payback time (SPT). The site has an annual solar energy potential of 1633.85 kWh/kWp, which is enough to pump 3921.24 kL of water to irrigate the majority of crops in one acre.

Pottekkat R., S. V.R., Resalayyan R., Gopakumar K., Umanand L., Reddy B.S.

The performance of safety-critical systems implemented on multi-core platforms depends heavily on the scheduling mechanism used. This paper addresses the problem of multi-core scheduling of a real-time application modelled as a Directed Acyclic Graph (DAG) with multiple service levels (where, a higher service level implies higher Quality-of-Service (QoS)), by proposing a novel two-phase offline–online scheduling mechanism called HYBRID-SCHED. The offline phase constructs a static schedule assuming worst-case execution behaviour, in order to ensure desired predictability with a minimum guaranteed QoS under all possible execution scenarios. Two alternative offline solution strategies have been designed. While the first strategy is a fast but reasonably good heuristic solution called Service-level Aware Scheduler (SAS), the second is a branch-and-bound based optimal solution-space search technique. However, online execution based on strict adherence to the static schedule may result in poor resource utilization as actual execution time of tasks at run time may be significantly less than worst-case estimates. In order to improve the situation, an online scheduler called Actual Execution-time Aware Scheduler (AEAS) has been developed. The basic goal of AEAS is to strategically reclaim resources that were provided for tasks at design time but are in fact being used inactively at run time. By gradually raising the service levels of the remaining (yet-to-be-completed) jobs, AEAS can then use the recovered resources to improve system-level QoS. Using real-world benchmark applications, we assessed the performance of the suggested framework. Results obtained demonstrate the usefulness of our plan.