Cyclic Loading on Composite Repair of Corroded Steel Pipelines

The study aims to determine how cyclic loading affects the structural integrity and lifespan of composite repair systems used to restore corroded steel pipelines. As specified in Annexure C of the ISO 24817 repair code, pipe specimens are machined to produce flaws with 80% wall loss. Testing under static and cyclic pressure loading is done as per ASTM D2992 and ASTM D2143. Static pressure loading is accomplished by continually pressurizing the pipe specimen, and burst pressure is assessed. Various Rc-ratios or levels of cyclic loading severity are used in cyclic pressure loading tests. Each case's number of cycles before failure is determined experimentally, and the service de-rating factor is assessed in accordance with ISO 24817. The 235 bar pressure was sustained by the static-loaded repaired pipe specimens with 80% wall loss, and the failure was catastrophic. At around 7000 cycles, the cyclically loaded repaired samples with 80% wall loss failed, and the failure manifests as debonding or a leak

GC-MS analysis of yellow pigmented Macrococcus equipercicus isolated from alfalfa rhizosphere soil fields of Coimbatore

The rhizosphere of plant possesses important microflora, which secretes wide chemical compounds including secondary metabolites necessary for plant growth and development. The microbial flora of alfalfa plant rhizosphere soil region was explored for functional activity and we found upto ten different pigmented colonies. Due to good functional diversity, this yellow pigmented colony was taken for further studies. Thus, the culture was molecularly characterized and identified for potent bioactive components responsible for antimicrobial activity. The selected culture mass was cultured and secondary metabolites were produced and extracted using ethyl acetate and subjected to GC-MS analysis. The antimicrobial study revealed selective activity against Streptococcus pneumonia, and Proteus sp with zone of inhibition to be 18 and 20 mm respectively.  Molecular identification of the isolate by 16S rRNA sequencing showed the isolate as Macrococcus equipercicus with 100 % similarity. Based on GC-MS analysis report 25bioactive compounds were identified and 13-docosenamide, hexadecanoic acid esters and quercetin were found in ethyl acetate extract. Conclusion: Thus the yellow pigmented gram positive cocci M.equipercicus isolated from Medicago sativa possessed wide antibacterial activity due to presence of quercetin. Through the studies, we were able to identify potent antibacterial compound producing bacteria from M. sativa plant rhizosphere soil

Self‐powered e‐Skin based on integrated flexible organic photovoltaics and transparent touch sensors

There is a growing interest in the large area, lightweight, low-power electronic skin (e-Skin), consisting of a multitude of sensors over conformable surfaces. The use of multifunctional sensors is always challenging, especially when their energy requirements are considered. Herein, the heterogeneous integration of custom-made flexible organic photovoltaic (OPV) cells is demonstrated with a large area touch sensor array. The OPV can offer power density of more than 0.32 μW cm−2 at 1500 lux, which is sufficient to meet the instantaneous demand of the array of touch sensors. In addition to energy harvesting, it is shown that the OPVs can perform shadow sensing for proximity and gesture recognition, which are crucial features needed in the e-Skin, particularly for safe interaction in the industrial domain. Along with pressure sensing (sensitivity of up to 0.26 kPa−1 in the range of 1–10 kPa) and spatial information, the touch sensors made of indium tin oxide and monolayer graphene have shown >70% transparency, which allow light to pass through them to reach the bottom OPV layer. With better resource management and space utilization, the presented stacked integration of transparent touch-sensing layer and OPVs can evolve into a futuristic energy-autonomous e-Skin that can “see” and “feel.

Performance assessment on manufacturing of unfired bricks using industrial wastes

This paper presents eco-friendly unburnt bricks made up of fly ash, waste plastic powder, waste glass powder, lime, gypsum and crusher sand as alternatives to conventional burnt clay bricks for sustainable development. The research focuses on the maximum utilization of industrial waste in eco-friendly unburnt brick production. Materials are characterized according to their chemical and geotechnical properties. In this research, we use a milled waste glass powder of size less than 600μm and plastic powder obtained from plastic waste of size less than 600μm are added along with crushed sand, gypsum, lime and fly ash with various mix proportions concerning FaL-G mix concept. All the proportions were taken on a weight basis. Compressive strength, water absorption, and efflorescence are the key parameters chosen for comparing the innovative brick with conventional fly ash brick. There are five different mixes (Type A, B, C, D & E) are made in this research. The plastic and glass powders are replaced by crusher sand at the increased rate of 2% in every mix whereas 2%,4%,6%,8%, and 10%. It was found that the type B bricks have 17.63% strength was increased when compared to base mix. From the test results, type B bricks have enhanced mechanical performance when compared to all other mixes

An Energy Efficient EdgeAI Autoencoder for Reinforcement Learning

In EdgeAI embedded devices that exploit reinforcement learning (RL), it is essential to reduce the number of actions taken by the agent in the real world and minimize the compute-intensive policies learning process. Convolutional autoencoders (AEs) has demonstrated great improvement for speeding up the policy learning time when attached to the RL agent, by compressing the high dimensional input data into a small latent representation for feeding the RL agent.Despite reducing the policy learning time, AE adds a significant computational and memory complexity to the model which contributes to the increase in the total computation and the model size. In this paper, we propose a model for speeding up the policy learning process of RL agent with the use of AE neural networks, which engages binary and ternary precision to address the high complexity overhead without deteriorating the policy that an RL agent learns. Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) compress weights into 1 and 2 bits representations, which result in significant compression of the model size and memory as well as simplifying multiply-accumulate (MAC) operations. We evaluate the performance of our model in three RL environments including DonkeyCar, Miniworld sidewalk, and Miniworld Object Pickup, which emulate various real-world applications with different levels of complexity. With proper hyperparameter optimization and architecture exploration, TNN models achieve near the same average reward, Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) performance as the full-precision model while reducing the model size by 10x compared to full-precision and 3x compared to BNNs. However, in BNN models the average reward drops up to 12% - 25% compared to the full-precision even after increasing its model size by 4x. We designed and implemented a scalable hardware accelerator which is configurable in terms of the number of processing elements (PEs) and memory data width to achieve the best power, performance, and energy efficiency trade-off for EdgeAI embedded devices. The proposed hardware implemented on Artix-7 FPGA dissipates 250 uJ energy while meeting 30 frames per second (FPS) throughput requirements. The hardware is configurable to reach an efficiency of over 1 TOP/J on FPGA implementation. The proposed hardware accelerator is synthesized and placed-and-routed in 14nm FinFET ASIC technology which brings down the power dissipation to 3.9 uJ and maximum throughput of 1,250 FPS. Compared to the state of the art TNN implementations on the same target platform, our hardware is 5x and 4.4x (2.2x if technology scaled) more energy efficient on FPGA and ASIC, respectively

Halal servicescape in the metaverse

Purpose: With increasing advances in emerging technologies including the metaverse and a continued rise in Muslim-friendly tourism, hospitality providers need to understand the opportunities and challenges involved in capitalizing on the metaverse phenomenon to design new service environments or servicescapes for their Muslim customers. This paper aims to develop a conceptual model of a servicescape in the metaverse that caters to the needs of Muslims and to advance a research agenda in this field. Design/methodology/approach: The main methodology for this conceptual study is a multidisciplinary literature review. Accordingly, this study synthesized relevant literature on service environments and halal markets from the services marketing, Islamic marketing and computer science fields to advance a logical framework built on seminal servicescape models and the Stimulus-Organism-Response framework. Findings: This paper provides several contributions. First, this study identifies the experienscape as a suitable foundational servicescape model for halal markets in the metaverse. Second, the authors introduce the “5 Ps halal metaverse component,” which elaborates on the associated opportunities and challenges in catering to the needs of Muslim metaverse travelers. Third, this study develops the halal metaverse servicescape model, which factors the relevant media metaverse components. Finally, the authors propose key managerial implications around four strategic areas and provide a comprehensive research agenda in the concluding section. Research limitations/implications: Given the conceptual nature of this study, further empirical research is required to ascertain the variables and key relationships proposed in the conceptual model. Practical implications: The findings of this study highlight the multi-stakeholder and multidisciplinary approaches needed to create a metaverse for halal markets. In addition, the insights help developers and managers to better understand the implications of the metaverse for halal markets and provide them with strategic considerations to better design service landscapes for Muslims in the metaverse. Originality/value: To the best of the authors’ knowledge, this is the first conceptual paper that develops a servicescape model in the metaverse in the context of Muslim consumers and comprehensively discusses its challenges and opportunities, thereby advancing the literature on servicescapes for the metaverse as well as service environments optimized for Muslim markets

REVIEW ON SHELL AND TUBE HEAT EXCHANGER USING NANOFLUIDS

Different types of heat exchangers are extensively used in various industries to transfer the heat between cold and hot fluids. The key role of the heat exchanger is to transfer heat at maximum rate .Shell and Tube heat exchangers are having special importance in boilers, oil coolers, condensers, pre-heaters. Shell and Tube heat exchanger is one such heat exchanger, provides more area for heat transfer between two fluids in comparison with other type of heat exchanger. To intensify heat transfer with minimum pumping power innovative heat transfer fluids called Nano fluids have become the major area of research now a days. This Review paper summarizes the important articles published on the effect of the heat transfer characteristics in shell and tube heat exchangers using Nano fluids. The review of previous works by researchers suggests that Nano fluids have great potential in augmentation of heat transfer of a heat exchanger. Nano fluid is advanced heat transfer fluid for next generatio

Design and Analysis of a Self-balancing Bicycle Model

Abstract This paper discusses the design and analysis of a self-balancing bicycle model. The objective was to develop an efficient design that can be fabricated in the future. The different methods of balancing a bicycle were studied to develop an optimal design. Solidworks 17 and Ansys 18 have been used for modeling, simulation, and analysis of the structure. The use of a Control Moment Gyroscope (CMG) to balance the bicycle model was studied and the results show that the effect of precession increases with an increase in rpm and the weight of the flywheel. Thus, a bicycle model actuated with CMG is far more stable and less prone to accidental tilts and toppling than one without. The studied data can be used for future research.</jats:p