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Operation map of a traveling-wave thermoacoustic electric generator with variable resistive-capacitive electric loads
No full textA toroidal-topology traveling-wave thermoacoustic electric generator (TWTEG) is developed. It consists of a traveling-wave thermoacoustic engine, two linear alternators connected in parallel, and sets of variable resistive-capacitive (R-C) external electric loads, in conjunction with accessories and instrumentation required for experimental investigations. The working medium is helium with a static absolute pressure that varies from 25 bars to 30 bars. A detailed description of the thermal design of the heat exchangers is presented. Sustainable operation of the TWTEG is achieved over a range of external R-C loads at different imposed hot-side temperatures and mean gas pressures. The performance parameters are measured for different experimental conditions and compared with a developed lumped-element model. The comparison between the experimental results and predictions reveals a good agreement. The impedance matching between the thermoacoustic engine and the linear alternators is investigated experimentally over a wide range of external R-C loads. The external R-C loads play a crucial role in the operation of the TWTEG. The mean gas pressure changes the operating frequency; however, it has no significant influence on the operating range of the TWTEG on the R-C load map. Increasing the hot-side temperature improves the thermal-to-acoustic efficiency and extends the operating region into larger regions
A Parametric Study for Eco-friendly Cost-effective Two-Story Structures
No full textModernity has introduced construction alternatives that could carry high loads such as reinforced concrete; however, such material is proven to be non-eco-friendly and expensive. However, these alternatives are not necessarily needed when constructing low-rise buildings. Hence, there is a need to study using different construction materials that are eco-friendlier and much more cost-effective compared to reinforced concrete. Within this study, a parametric study is performed on a two-story residential building. The study contains 25 different variations of the same building that feature five different dimensions. These 25 variations are divided into five conventional reinforced concrete structures representing the control group, and 20 eco-friendly structures made from combinations of wood, limestone, and compressed earth blocks. Following the modeling and analysis phase, the results obtained are compared to highlight the most economically-sound option for each range of dimensions. In addition, the total costs and carbon emissions of the eco-friendly models are compared with that of the commonly used reinforced concrete control group, all while delivering cost per area values for the most economical models, and the carbon emissions per area
Behavior effect of Semiconductor 2D dopants on time response of TMDC-MoS2 based Schottky-photodiode
No full textSiC and GaN as 2D materials as well as MoS2 as a TMDC semiconducting material was chosen, a Shottkyphotodiode based SiC/MoS2 composite as well as GaN/MoS2 composite was fabricated, the resulted photodiode external quantum efficiency as well as internal quantum efficiency was compared to that of MoS2 based photodiode and consequently the time response was also compared
Numerical Simulation Study of Water Salinity Optical Sensors Using Nano-Slot and Slab Waveguides
No full textThis paper aims to address the critical necessity for efficient methods of measuring salinity, considering its substantial impact on water quality across diverse applications. Conventional techniques, often cumbersome and time-consuming, underscore the urgent need for innovative and economically viable alternatives. This study proposes an optical approach to salinity detection utilizing both nano-slot and slab waveguide-based sensing platforms, offering advantages in terms of sensitivity, real-time monitoring, and cost-effectiveness. Advanced numerical simulations are employed to evaluate the efficacy of these platforms in detecting salinity levels. Theoretical analysis and computational modelling are conducted to delve into the intricate interplay between the two different waveguides and various salinity concentrations. The outcomes of this research endeavor aim to furnish valuable insights into the potential of these platforms for efficient salinity detection
The Problem of Many Vehicles: An Explainable System for Autonomous Multi-agent Accidents
No full textGiven the need for trustworthy autonomous vehicles, this research paper presents an explanatory framework for multi-agent autonomous vehicle crashes, leveraging the FCI causal algorithm to generate a Full Time Causal Graph (FTCG). The framework’s performance is evaluated using simulated scenarios within the MetaDrive environment, employing key metrics such as accuracy, time, scalability, and stability. Results demonstrate promising accuracy in crash explanation, showcasing the effectiveness of the FCI causal algorithm. The scalability analysis reveals that the framework operates within reasonable time constraints as the dataset size increases. However, the introduction of uniform noise poses a challenge to stability, adversely affecting the system’s reliability in identifying crash sequences under random variations. While the framework exhibits strengths in accuracy and scalability, addressing stability concerns, especially in the presence of uniform noise, presents an avenue for future research and improvement
A meta-analysis on the influence of dietary betaine on the growth performance and feed utilization in aquatic animals
No full textBetaine is one of the most widely used attractants in feed formulations. Its dietary inclusion in aquafeeds positively influences several growth performance metrics and feed utilization of aquatic animals. In this study, a meta-analysis was conducted to quantify the effects of dietary supplementation of betaine on the specific growth rate (SGR), feed conversion ratio (FCR), protein efficiency ratio (PER), and survival (SUR) of several aquaculture species reared under different environmental conditions. Standardized mean differences (Hedge\u27s g) were computed to quantify the primary outcomes. Likewise, the influence of several moderators such as aquaculture species, feeding behavior, experimental duration, and percentage of betaine supplementation in aquafeeds on Hedge\u27s g effect sizes for SGR, FCR, PER, and SUR was determined by a mixed-effects model. The results indicated improved SGR, FCR, and PER in both carnivorous and omnivorous/herbivorous species fed on betaine-supplemented diets relative to the control. Although SUR was higher in control groups relative to the betaine-supplemented groups, no significant differences were noted. Higher values for betaine inclusion level tended to lower SUR whereas lower values improved the SUR. Furthermore, longer experimental durations exhibited higher values for SUR compared to shorter experimental durations. The dietary betaine requirement of aquaculture animals was estimated to be 0.99 % based on broken-line regression between the betaine inclusion level in aquafeed and SGR effect sizes
Hyper CLS-Data-Based Robotic Interface and Its Application to Intelligent Peg-in-Hole Task Robot Incorporating a CNN Model for Defect Detection
No full textVarious types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB
From metabolomics to proteomics: understanding the role of dopa decarboxylase in Parkinson’s disease. Scientific commentary on: “Comprehensive proteomics of CSF, plasma, and urine identify DDC and other biomarkers of early Parkinson’s disease”
No full textOptimization of Pervious Concrete Mechanical Properties Through Incorporation of Fiber Reinforcement Schemes
No full textPortland Cement Pervious Concrete (PCPC) is a special high porosity concrete containing zero or minimal amount of fine aggregates. Such concrete endows the concrete to have significant voids allowing water to percolate; however, such an open void structure reduces the mechanical strength of the concrete considerably. It is recommended that chemical admixtures be added to the concrete to enhance its workability and other properties. The study aims to potentially enhance the properties of PCPC through the incorporation of various fiber reinforcement schemes. The fibers used in the scope of this study are hooked-end steel fiber, macro-polypropylene fiber, and glass fiber. To meet that objective, concrete mixes were prepared using varying fiber dosage rates and aggregate gradations. An experimental program was developed to test fresh concrete properties, hardened concrete properties, and durability. In order to gauge only the effectiveness of the aforementioned fiber, the PCPC mix design was standardized across the spectrum to eliminate such variables. It was evident that the glass fiber with Vf 0.17% (GF 2) enhanced mechanical properties with the most significant compressive strength increase, compared to the control sample, reaching 34 MPa. Moreover, (GF 2) has enhanced, compared to the control sample, the flexural and the splitting tensile strength with an increase of 93.7% and 161.9%, respectively. The outcome of the study is that the use of different fiber reinforcement schemes has managed to enhance the mechanical strength of PCPC while maintaining an adequate rate of infiltration that complies with ASTM standards. The research opens the door for further application of the proposed model in different contexts both regionally and internationally, thus playing a vital role in the concrete nexus
Performance Optimization of Double-Absorber Perovskite Solar Cell: Numerical Calculations
No full textIn this study, we designed and simulated a new double absorber perovskite solar cell device by performing numerical analysis using SCAPS-1D. The architecture of the solar cell device consists of Glass/FTO/C2N/CsSnGeI3/CsSnI3. Optimizing the core parameters of CsSnGeI3 implied to an encouraging solar cell performance with a remarkable power conversion efficiency of 28.61%. The results show that increasing the thickness of CsSnGeI3 to 1000 nm enhances the solar cell performance. The simulations show that increasing the absorber layer thickness is desirable to promote the spectrum absorption process, thus accelerating the generation rate of the charge carriers. Increasing the doping density in CsSnGeI3 implied to inconsistent impact on the solar cell performance. The solar cell displayed the optimal performance at low and high doping level, while at intermediate doping level, the solar cell efficiency depreciates to 23.18%. This result is ascribed to creation unstable depletion regions at this doping level, which prevent the charge carriers from reaching the metal back contact. Additionally, increasing the defect density in CsSnGeI3 yielded a sharp continuous degradation in the device performance. The power conversion efficiency decreased to 15.22%. Finally, increasing the operating temperature showed a harsh impact on the solar cell performance, and the optimal performance occurs at 300 K. It is believed that the simulated model represents an added value and a great potential in the field of solar cells research and fabrication