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Experimental Investigations on Static, Dynamic, and Morphological Characteristics of Bamboo Fiber-Reinforced Polyester Composites
The use of natural fiber-reinforced polymer composites has increased over a period of time, majorly due to the ecosustainability and biodegradability of the composites. Among several grades of natural fibers, bamboo fibers offer numerous environmental and cost benefits and possess excellent mechanical characteristics. The superior properties of the bamboo fibers have triggered the research interests in the domain of bamboo fiber-reinforced polymer composites. Among the polymers, polyesters are long chain molecules made up of atoms arranged in various ways with other elements to form the basic building blocks of a polymeric chain. Polyester is being increasingly employed in today's industrial products due to its inherent advantages. As a result, based on the potential properties of bamboo fibers as reinforcing materials and polyester resin as matrix material, the biocomposites are synthesized by hand lay-up technique and the specimens cut as per the standard dimensions and subjected to mechanical investigations, vibration, and morphological characterization as per the ASTM test methods. The increase in fiber weight content has enhanced flexural, tensile, and impact characteristics and improved the damping characteristics of the composite specimens. The microstructural evaluations have revealed the uniform distribution of the bamboo fibers in the resin, and the morphological studies of the fractured specimens have revealed that the fracture is majorly due to the matrix cracks rather than the fiber debonding, which is a major attribute ascertaining the strong coherent strengthening mechanism brought about by the inclusion of bamboo fiber in the polyester resin. 2022 N. Santhosh et al. -
Experimental Investigations on the Thermal Diffusion Characteristics and Photoluminescence in Multiphase Micro Fluids Containing ZnO Micro Tubes and Fluorescein Dye
Abstract: Scattering of light by disordered structures is normally detrimental to their applicability in many optoelectronic devices. However, some micro and nanostructures are useful in enhancing several optical and thermal properties like emission, heat diffusion etc. For this purpose, we have optimized the low temperature hydrothermal growth method for the ZnO micro tubes which leads to the growth of ZnO as mono dispersed micro tubes. Further, these samples were used to enhance the fluorescence efficiency of disordered media consisting of micro tubes of ZnO and fluorescein dye and to optimize the thermal diffusion of the mixture which will help us optimize the composition of these microscopic inclusions in designing a random lasing medium. Dual beam thermal lens method was used for this purpose. 2020, Pleiades Publishing, Ltd. -
Experimental Investigations on Turbine-Generator Shaft Under Subsynchronous Resonance
Energy exchange takes place between turbine and generator in the power system during subsynchronous resonance (SSR) which leads to torsional interaction between the shafts. Resonance in the power system is caused by the series capacitors connected to the transmission line. This paper aims to present an electromechanical approach to analyse and interpret subsynchronous resonance using the Finite element method. Subsynchronous resonance is introduced in two test rigs consisting of turbine, generator, shaft, and coupler with capacitors. Experiments and simulations (torque analysis and frequency response analysis) are conducted in test rigs and ANSYS workbench 16.0. Moreover, a spring damper is modelled to improve the stability of the shaft. From the results, it is clear that mechanical stress is increased when capacitors are connected to the test rig. A spring damper is installed at the point where the deformation is high. The damper reduced the stress and the vibration. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Experimental screening of a series of earth-abundant bi-metallic phospho-boride electrocatalysts for overall seawater electrolysis
Seawater electrolysis offers a promising alternative for large-scale hydrogen production, but its industrial viability is hindered by the lack of efficient electrocatalysts. Herein, a series of metals (M = Ni, Fe, W, Mo, V, Cu, and Mn) were experimentally screened to form a bi-metallic catalyst with CoPB, resulting in CoMPB catalysts. Amongst the screened metals, only the inclusion of Mo, W, V, and Fe was found to be beneficial in improving the seawater-splitting reaction rates. Notably, CoMoPB, CoWPB, and CoVPB required minimal HER overpotentials of 56, 105, and 73 mV, respectively, at 10 mA/cm2 in alkaline natural seawater conditions, while CoFePB (291 mV at 10 mA/cm2) outperformed other Co-M-P-B counterparts for OER. The addition of a second metal to CoPB enhances activity, conductivity, and surface reactivity by modulating electron density, optimizing it for seawater splitting. Further, the CoWPB/NFHER || CoFePB/NFOER combination yielded the lowest cell potential of 1.59 V at 100 mA/cm2 and sustained operation for over ?65 h in alkaline natural seawater with ?98 % OER selectivity. The same combination, when integrated into an advanced seawater electrolyzer with zero-gap assembly, required a cell voltage of ?1.94 V to achieve 0.5 A/cm2, demonstrating strong commercial potential. 2025 Hydrogen Energy Publications LLC -
Experimental scrutinization on production of biogas from vegetable and animal waste
Anaerobic fermentation is a highly promising technology for converting biomass waste into methane, which may directly be used as an energy source. The objective of this research was to investigate production rate of biogas from camel dung, chicken dropping and vegetable waste. Attempts have been made in this study to optimize various parameters in order to determine the most favorable conditions for maximum biogas production from three different types of wastes such as camel dung (CAD), chicken droppings (CHD) and vegetable waste (VW). The amount of biogas produced from the wastes is compared as: VW >CHD>CAD. The results showed that biogas produced from VW is 720 ml in 32 days as compared to CHD and CAD which are 600 ml in 36 days and 80 ml in 40 days respectively. The effect of the pH and temperature on the amount of biogas produced was also studied. The experiments were conducted in temperatures ranging from 36 C to 44 C. 2023 Author(s). -
Experimental study of response parameters during machining of Inconel 718 with cryogenically treated ceramic round tool using cutting fluid
Highly advanced superalloys are being rapidly spreading throughout the globe. It's in need of the hour to produce similar materials which are being used in several industries similar as petrochemical, biomechanical, aerospace and marine industries. Inconel 718 is one similar superalloy which is being used due to its better characteristic features like high attrition resistance, high temperature burden conditions, thermal fatigue resistance, and cryogenic temperatures. Owing to the hardness conditions, tools indicate the low tool life and high wear characteristics. Ceramic insert is one such tool that is being used to machine Inconel 718 which is cryogenically treated to improve tool life. The use of emulsified cutting fluid reduces tool wear and improve durability of the tool, thereby improving the efficiency of the machining of Inconel 718. In this paper, experimental investigation has been carried to find the use of emulsified cutting fluid that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the effect of cutting parameters which are cutting speed, feed rate and depth of cut. 2021 Elsevier Ltd. All rights reserved. -
Experimental study of solar dryer used for drying chilly and ginger
Open air solar drying is one of the most popular methods for drying food products holds many drawbacks resulting in contamination of food products. This project is to transform the traditional method to an innovative, clean and cost-effective method to dry chilly and ginger, two being the top export commodity of India. Here a solar dryer is made which comprised of flat plate air heater, a chamber for drying and an air blower which induces forced convection. This system enhances the drying process even at low-intensity sunlight by assimilating heat storage materials. The equipment was tested in the meteorologicalcondition of the faculty of engineering, Christ (Deemed to be University) (latitude of 12.86N, a longitude of 77.43E) Bangalore, Karnataka. The process has reduced the moisture content from around 72.69% to 28.24% in the case of chilly and from 68.88% to 14.31% in the case of ginger within a period of 10 hours for a mass flow rate of 0.051kg/s. Average drier efficiency was estimated to be about 22%. The specific moisture extraction rate was estimated to be about 0.76 kg/kWh. This process resulted in a better moisture extraction rate, eliminating the defects caused by open sun drying. This process resulted in a better moisture extraction rate, eliminating the defects caused by open sun drying. 2019 Author(s). -
Experimental Study on Warm Mix Asphalt Binders with a Focus on Rheological Performance
Warm mix asphalt (WMA) mixtures have been increasingly used in road construction due to their energy saving and environmental protect benefits. However, unsuitable additives were often adopted due to their variability and it often led to poorer quality of asphalt pavements. In this study, nine asphalt samples from two categories of warm mix additives, including six organic and three chemical additives, were prepared. The rotational viscosity test, temperature sweep test, linear amplitude sweep test (LAS), and bending beam rheometer (BBR) test were employed to comprehensively evaluate the effect of warm mix additives on the viscosity-reduction effect, high-temperature performance, fatigue resistance, and low-temperature performance of WMA, respectively. The results showed that the viscosity- reduction effect of organic additives was more significant compared to chemical additives. Besides, organic additives were generally favorable to the high-temperature and fatigue resistance of asphalt binders, but their effects on the low-temperature performance of asphalt binders were highly variable. Chemical additives had a limited effect on the high-temperature and fatigue resistance of asphalt binders. Meanwhile, the chemical additives have a marginally positive and stable impact on the low-temperature performance of asphalt binders. The findings provided a comprehensive basis for the selection and application of warm mix additives. The Author(s), under exclusive licence to Chinese Society of Pavement Engineering 2025. -
Experimental Verification of Gain and Bandwidth Enhancement of Fractal Contoured Metamaterial Inspired Antenna
The performance of any antenna cannot be completely assessed purely on the basis of simulation results. All simulations are made by assuming an ideal environment where the fabrication tolerances and practical losses are not accounted for. Therefore, evidencing the performance experimentally becomes a crucial step. In this work, the experimental validation of a fractal contoured square microstrip antenna with four ring metamaterial structure, hereon referred to as optimized metamaterial inspired square fractal antenna has been presented. It is an extension to previously designed antenna and aims to experimentally verify the enhanced gain and bandwidth of this antenna. The design and simulation of the proposed antenna was accomplished by using Ansys HFSS v18.2. The end-to-end antenna spread area is 23 mm x 23 mm on a 46 mm x 28 mm x 1.6 mm FR4 substrate with ?r of 4.4. The simulated design was fabricated using Nvis 72 Prototyping Machine and measured in an anechoic chamber facility using vector network analyzer. The antenna resonates with the deepest S11 of-39.5 dB in a broad bandwidth of 2.53 GHz from 2.265 GHz to 4.79 GHz with experimental verification. The proposed antenna provides an enhanced gain of 8.81 dB at the most popularly used frequency of 2.5 GHz. The simulation and experimental results of resonance, gain and radiation pattern are found to agree maximally. The fractional bandwidth offered by this proposed antenna is 72.28%. The experimental validation confirms enhanced gain-bandwidth performance in a wide resonance band. Hence, this antenna is well recommended for wireless, energy harvesting rectenna and sub-6 GHz (2.5 GHz to 4.20 GHz) 5G applications. 2022, Advanced Electromagnetics. All rights reserved. -
Experimental, FEA, and machine learning studies on wear behavior of LM13 aluminum hybrid composites reinforced with zircon and graphite
This paper examines applied load and zircon reinforcement influence on LM13 alloy composites wear behavior. LM13 was reinforced with 3?wt.% graphite with 3, 6, 9, and 12 weight percent of zircon utilizing a stir casting technique with a chill end to achieve unidirectional solidification. Wear tests were conducted on specimen's chill end using a pin-on-disc apparatus under loads of 30?N, to 70?N in steps of 10?N incremental. The results indicated that when the amount of zircon went up, the wear rate dropped, reaching a minimum at 9?wt.% zircon, then slightly increasing at 12?wt.%. Specifically, wear rate reduced from 4.2?10?3mm/Nm at 3?wt.% zircon to 2.7?10?3mm/Nm at 9?wt.% zircon, before rising to 3.5?10?3mm/Nm at 12?wt.%, establishing 9?wt.% zircon as the optimum reinforcement. Finite Element Analysis (FEA) had been used to simulate wear behavior, and its predictions aligned well with experimental data, with deviations under 5%. Both experimental and FEA results confirmed that wear rate increases proportionally with applied load. Additionally, machine learning techniques were employed to validate the observed trends, enhancing the reliability of the findings. Microstructural analysis through Field Emission Scanning Electron Microscopy showed evidence of plastic deformation and delamination at higher stress levels, compromising material integrity. Notably, the composite with 9?wt.% zircon exhibited reduced wear deformation and minimal microstructural damage, confirming its effectiveness in improving wear resistance. IMechE 2025 -
Experimentally optimizing a spinning disk by manipulating its mass distribution and radius
The scientific method enables the experimental study of complex phenomena by isolating key variables. This work explores the significant properties of spinning bodies. Optimizing spinning disks is the primary aim of this work. Optimization is achieved by manipulating the moment of inertia (MOI) of the disk, allowing a longer duration of spin and lowering the rate of energy dissipation. Experiments are designed and conducted to explore the relationship between the radius and mass distribution of the disk and the angular deceleration experienced by it. Effects of the same on energy retention is analyzed. Empirical data is interpreted graphically while accounting for systematic and random uncertainties. Percentage change in duration of spin as a result of percentage change in physical quantities is studied. Moving mass away from the central axis of the spinning disk increases its duration of spin from a constant initial angular velocity. Energy retention is also improved. Increasing the radius of the disk increases the duration of spin and reduces the rate of energy dissipation. The above conclusions are drawn from experiments where the mass and thickness of the disk are controlled along with other necessary factors that can influence the results. The experiments confirm the existing theory relating to the moment of inertia, angular quantities, resistive torques and kinetic energy of spinning disks. The experiments provide insights into the behavior of spinning disks in practical situations, especially in problems concerned with optimization in the field of mechanical engineering. 2026 Veeresha et al., published by Paradigm. -
Experimenting with ONOS scalability on software defined network
In traditional network, a developer cannot change the configuration of a router with software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to make the routers/switches programmable, a new architecture of network has to be developed and this gave rise to Software defined networks. It is the new architecture for Computer Networks in which, the old traditional architecture is slowly depreciated. It is very difficult to adapt new technology especially to decide upon which controller has to be considered and what may be its scalability to compete the dynamic circumstances of networks. Many researches are working on possible solutions and look upon SDN to overcome the traditional network limitations. There are many SDN controllers existing amongst them, some are OpenDaylight, Floodlight, Onos, Ryu, Beacon etc. From the existing multiple controllers serving the SDN services to the network, Onos is one of the Controller. ONOS can be deployed on Docker container and it is accessed using its IP as a host. In this paper, authors are contributing for the evaluation of the Performance to check the Scalability of ONOS controller by taking many scenarios which are experimented on the simulation tool of Mininet, Onos Controller, Docker and iPerf. ONOS Controller?s simulated environments are observed for its throughput evaluated in dynamic conditions of a network over Mesh topology by gradually increasing the number of hosts until its supported by the system with optimum resource utilization. 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Experimenting with resilience and scalability of wifi mininet on small to large SDN networks
Today everything is getting digitized where people want to be wireless by all aspects. There is a high demand of WiFi in every sector. Highest influence on network planning of newly developed network infrastructure is of SDN to meet the futuristic needs of upcoming technology. As a result, newly developed networks have become more adaptive to dynamic circumstances along with enhanced flexibility. Being globally connected, it is inevitable to obtain adequate services from data centers through Wi-Fi support on SDN Networks, which is still a dream. Thus, the target of the experiment performed and presented by the authors of this paper is to implement WiFi support on SDN. Further, authors have also demonstrated the scalability and resilience of SDN based WiFi Network on Mininet by testing performance parameters in various dynamic scenarios. This paper will have a high impact on the end users as SDN technology can be implemented as last mile technology using WiFi SDN. BEIESP. -
Experimenting with scalability of Beacon controller in software defined network
In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance. BEIESP. -
Experimenting with scalability of floodlight controller in software defined networks
Software Defined Network is the booming area of research in the domain of networking. With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of existing multiple controllers providing the SDN functionalities to the network, one of the stellar controllers is Floodlight Controller. This paper is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Floodlight Controller and iPerf. Floodlight Controller is tested in the simulation environment by observing throughput and latency parameters of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes. 2017 IEEE. -
Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine
Human intelligence system simulation has made significant strides in several areas, including clinical decision-making using medical imaging and electronic health records, health referral systems, discovering recommended medications and vaccines, recognizing prescribed errors, and real-time data analysis. Therefore it is essential to discover patterns and transfer knowledge in the medical domain. The obstacles at the level of data collection, data analysis, model development, decision-making, and ethical concerns need to be addressed. It is vital that clinical interpretation tools associated with both hardware and software employed by medical professionals be precisely examined when rendering decisions regarding diagnoses and therapies related to the diagnosis. Computer scientists generally lack training in medical concepts specific to their field. Another crucial aspect is that black box algorithms based on artificial and computational intelligence are opaque and devoid of logical justification. Owing to these limitations, the technique of eXplainable Artificial Intelligence (XAI) models is explored in this chapter, primarily focusing on improving the interpretability of computational models. Specific objectives of this chapter are to: a) discuss the role that CI techniques and methods in the construction of an intelligent health prediction system; b) demonstrate the multiple CI paradigms utilized in medical prediction; and c) present recent case studies to showcase the performance of the computational intelligent models. 2026 Elsevier Inc. All rights reserved. -
Explainable AI for Diabetic Retinopathy Screening: Enhancing Clinician Trust in Deep Learning Predictions
Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, with early detection being critical for effective intervention. While deep learning models have demonstrated exceptional performance in automated DR screening, their black box nature has limited clinical adoption due to concerns about interpretability and trust. This paper presents a comprehensive explainable AI (XAI) framework that integrates multiple visualization techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), attention mechanisms, and feature attribution methods, to provide clinically meaningful explanations for DR predictions. We evaluate our approach on the publicly available EyePACS and Messidor-2 datasets, achieving 94.3% accuracy while generating interpretable heatmaps that highlight lesion-specific regions. A clinical validation study involving 15 ophthalmologists demonstrates that our XAI-augmented system increases diagnostic confidence by 23% and reduces review time by 31% compared to non-explainable models. Our findings suggest that transparent AI systems can effectively bridge the gap between algorithmic performance and clinical trust, paving the way for broader adoption of AI-assisted DR screening in healthcare settings. 2026 IEEE. -
Explainable AI for Heart Disease prediction: A Clinical Transparency Route Experiment
In this paper, a proposeable explainable machine learning procedure on estimating the danger of heart attack will be proposed with a stacked ensemble of XGBoost, Random Forest, and Multi-layered perceptron (MLP). The data set of UCI Heart Disease was preprocessed by normalization, imputation, and SMOTE to address the imbalance problem and the variables were optimized with the help of the feature engineering. The model performance was measured using accuracy, precision, recall, F1-score and ROC-AUC. In order to make the results more interpretable, Explainable AI were applied with SHAP and LIME, and the most relevant risk factors including troponin, cholesterol, and blood pressure were indicated.. In this paper, it is shown that ensemble learning in XAI can yield plausible, interpretable, and clinically practical data to complement enhanced cardiovascular diagnostics. 2025 IEEE. -
Explainable AI for Heart Disease prediction: A Clinical Transparency Route Experiment
In this paper, a proposeable explainable machine learning procedure on estimating the danger of heart attack will be proposed with a stacked ensemble of XGBoost, Random Forest, and Multi-layered perceptron (MLP). The data set of UCI Heart Disease was preprocessed by normalization, imputation, and SMOTE to address the imbalance problem and the variables were optimized with the help of the feature engineering. The model performance was measured using accuracy, precision, recall, F1-score and ROC-AUC. In order to make the results more interpretable, Explainable AI were applied with SHAP and LIME, and the most relevant risk factors including troponin, cholesterol, and blood pressure were indicated.. In this paper, it is shown that ensemble learning in XAI can yield plausible, interpretable, and clinically practical data to complement enhanced cardiovascular diagnostics. 2025 IEEE. -
Explainable AI for Secure and Trustworthy Autonomous Network Management
Rise of AI-driven autonomous networks for managing complex, dynamic infrastructures. While AI optimizes performance, it acts as a black box. This lack of transparency undermines trust and security, making it challenging to validate decisions, detect adversarial attacks, and understand why an AI model made a specific routing, security, or resource allocation decision. Security blind spots face significant challenges in detecting subtle adversarial manipulations or policy exploits because the reasoning behind the model's decisions is hidden. Additionally, poor diagnosability occurs when a network fault or performance degradation occurs, making root cause analysis slow and complex. Hence, the network operators are hesitant to cede control to systems whose actions they cannot verify or audit. Explainable AI (XAI) is critical for bridging this gap, ensuring management decisions are transparent, interpretable, and defensible. The proposed model makes real-Time management decisions. This model uses post-hoc techniques to generate explanations for each decision. It presents actionable insights and cross-references explanations against security policies and known threat patterns to flag anomalous reasoning. 2025 IEEE.