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Metal matrix composites (MMCs) are commonly used in many aerospace and industrial applications. MMCs possess significantly improved properties including high specific strength, specific modulus, damping capacity and good wear resistance compared to unreinforced alloys. The purpose of this paper is to describe the tribological studies of Al-Si alloy–fly ash composites manufactured using powder metallurgy technique.

Al-Si (12 Wt.%) alloy–fly ash composites were developed using powder metallurgy technique. Al-Si alloy powder was used as matrix material, and the fly ash was used as reinforcement. The particle size of Al-Si alloy powder was in the range of 75-300 μm, and the fly ash was in the range of 1-15 μm. The friction and wear characteristics of the composites were studied using a pin-on-disc set up. The test specimen was mated against cast iron disc, and the tests were conducted with the loads of 10, 20 and 30 N, sliding speeds of 0.5, 1 and 1.5 m/s for a sliding distance of 2,000 m.

The effects of load and sliding speed on tribological properties of the base alloy and Al-Si alloy–fly ash composites pins on sliding with cast iron disc are evaluated. The wear rate of Al-Si alloy–fly ash composites is lower than that of base alloy, and it increases with increasing load and sliding speed. The coefficient of friction of Al-Si alloy–fly ash composites is increased as compared with base alloy.

The development of Al-Si alloy–fly ash composites produced by powder metallurgy technique will modernize the automobile and other industries because near net shape at low cost and good mechanical properties are obtained.

There are few papers available on the development and tribological studies of Al-Si alloy–fly ash composites produced by powder metallurgy technique.

The present investigation aims to present the status of planetary science research in India using different scientometric indicators, as reflected in the Web of Science Core Collection database.

The researcher adopted systematic approaches to retrieve the data from the Web of Science Core Collection database for 20 years by using AAS Astronomical subject keywords. A total of 1,504 Indian publications and 55,572 World's publications were considered for analysis. The data were analyzed using the biblioshiny application of bibliometrix to investigate the most productive countries/territories, institutions, authors, research fields, journals, keywords, and h, g-index. The VOSviewer program is used to construct and visualize scientometric networks and analyze the co-occurrence of terms. “Webometric Analyst 2.0” is used to retrieve the Altmetric attention scores for the articles.

The results revealed that the publications on planetary science research has increased over time, with an annual growth rate of 9.66%. The study also revealed the prolific authors and institutions, productive journals and most frequently cited journals. The USA was the major collaborating partner of India. The results also provided valuable information on the citations made to these papers on planetary science, including a total number of citations, average citations per item, cited rate and h-index. There were 28,086 citations to 1,504 papers. The top 67 citation papers were the h-core papers on planetary science in India. Altmetric score for planetary science articles ranged from 1 to 2,418. Twitter (69%), news outlets (16%), blogs (6%), and Facebook (6%) were the most popular Altmetric data resources.

This investigation is the first attempt to employ scientometrics and visualization techniques to planetary science research in India.

Finite impulse response (FIR) digital filters are a general element in several digital signal processing (DSP) systems. In VLSI platform, FIR is a developing filter because the complexity of design grows with the length of the FIR filter and also it has less latency. Generally, the FIR filter is designed dominated by the multiplier and adder. The conventional FIR filters occupy more area because of several numbers of adders and multipliers for filter designs.

To overcome this issue, the Vedic Multiplier (VM) and Moore-based LoopBack Adder (MLBA) approach-based optimal FIR filter were designed in this research. Normally, the coefficient has been generated manually, which performs the FIR filter operation. So, the coefficient was generated from the MATLAB filter design and analysis tool. All pass coefficient was introduced in this research, which performs the processing element (PE). The VM approach was utilized in the PE to multiply the filter inputs and coefficients. This research employs the Moore-based LBA (MLBA) in the accumulator for the adding output of the PE. An MLBA approach is a significantly reduced area and increases speed by applying a looping transform function. Here, the proposed method is called a VM-MLBA-FIR filter. In this research, the FIR filter was done in Field Programmable Gate Array (FPGA) Xilinx by using Verilog code on various Virtex devices.

The experiment results showed that VM-MLBA-FIR filter reduced 26.88% of device utilization and 0.32 W of minimum power consumption compared to the existing PSA-FIR filter.

The experiment results showed that VM-MLBA-FIR filter reduced 26.88% of device utilization and 0.32 W of minimum power consumption compared to the existing PSA-FIR filter.

Digital signal processing (DSP) applications such as finite impulse response (FIR) filter, infinite impulse response and wavelet transformation functions are mainly constructed using multipliers and adders. The performance of any digital applications is dependent on larger size multipliers, area and power dissipation. To optimize power and area, an efficient zero product and feeder register-based multiplier (ZP and FRBM) is proposed. Another challenging task in multipliers is summation of partial products (PP), results in more delay. To address this issue, the modified parallel prefix adder (PPA) is incorporated in multiplier design. In this work, different methods are studied and analyzed for designing FIR filter, optimized with respect to area, power dissipation, speed, throughput, latency and hardware utilization.

The distributed arithmetic (DA)-based reconfigurable FIR design is found to be suitable filter for software-defined radio (SDR) applications. The performance of adder and multipliers in DA-FIR filter restricts the area and power dissipation due to their complexity in terms of generation of sum and carry bits. The hardware implementation time of an adder can be reduced by using PPA which is based on Ling equation. The MDA-RFIR filter is designed for higher filter length (N), i.e. N = 64 with 64 taps and this design is developed using Verilog hardware description language (HDL) and implemented on field-programmable gate array. The design is validated for SDR channel equalizer; both RFIR and SDR are integrated as single system and implemented on Artix-7 development board of part name XC7A100tCSG324.

The MDA-RFIR for N = 64 is optimized about 33% in terms of area-delay, power-speed product and energy efficiency. The theoretical and practical comparisons have been done, and the practically obtained results are compared with existing DA-RFIR designs in terms of throughput, latency, area-delay, power-speed product and energy efficiency are better about 3.5 times, 31, 45 and 29%, respectively.

The MDA-RFIR for N = 64 is optimized about 33% in terms of area-delay, power-speed product and energy efficiency.

The paper aims to address the formulation of zirconium and oxalicum additive-based lubricants for use in slide ways to meet the demands of high positioning exactness based on reduction in stick–slip and coefficient of friction over a wide speed range and compares the same with commercially available lubricant.

An investigation into the frictional properties and stick-slip behavior of lubricating oil is carried out using linear reciprocating tribometer and correlated with ultraviolet spectroscopic analysis.

It is observed that these transition metal additive compounds support in increasing the flexibility of the molecular chains leading to improved lubricity.

The lubricant additives considered for the current study are based on transition metals zirconium and oxalicum. It is observed that these additive compounds support in increasing the flexibility of the molecular chains, leading to improved lubricity.

Quite a few statistical and artificial neural network (ANN) models have been developed for the mass appraisal of the real estate by the municipalities. The purpose of this paper is to report the results of a research conducted to compare the prediction accuracy of the three most used models: multiple regression model, additive nonparametric regression, and ANN.

The three models were developed using the housing database of a town with 33,342 residential houses. In this database, the cutoff point for higher priced homes was $88 per square foot of living area.

The research confirmed that using statistical and ANN models are reliable and cost‐effective methods for mass appraisal of residential housing.

It was found that any of the three models can be used, with similar accuracy, for lower and medium‐priced houses, but the ANN is considerably more accurate for higher priced houses.

Supply chain structure, characteristics, and applicable policies differ between developing and developed countries. While most supply chain management research is directed toward supply chains in developed countries, the authors wish to explore the financial impact of disruptions on supply chains in a developing country. The purpose of this paper is to highlight the importance of effective supply chain management practices that could help avoid or mitigate disruptions in Indian companies. The authors study the stock market impact of supply chain disruptions in Indian companies. The authors also aim to understand the difference in financial implications from disruptions between companies in India and the USA.

Event study methodology is applied on supply chain disruptions data from Indian companies. The data are compiled from public news release in Indian press. A data set of 301 disruptions for a ten-year period from 2003-2012 is analyzed. Stock valuation of a company is used to assess the financial impact.

The results show that Indian companies on average lose −2.88 percent of shareholder wealth in an 11-day window covering the event day and five days pre- and post-disruption announcement. A significant stock decline was observed as early as three days prior to announcement, indicating possibility of insider trading and information differentials between investors. Irrespective of the location and responsibility of a disruption, companies experience significant negative returns. Company size, book-to-market ratio, and debt-to-equity ratio were found to be insignificant in affecting the stock market reactions to disruptions. The authors also compiled supply chain disruptions data for US companies. When compared to the US companies, Indian companies register a significantly higher stock decline in the event of a disruption.

Supply chain disruptions data from India and the USA are analyzed. Broad applicability of results across countries may require studying other developing countries. The research demonstrates potential effectiveness of investment in supply chain management initiatives. It also motivates research focussed specifically on supply chains in developing countries.

Supply chain decision makers in India could benefit from investment in disruptions management and mitigation practices. The results provide a valuation of effective supply chain management. The findings provide guidance for investors in making decisions when supply chains face disruptions.

The paper studies the financial consequences of supply chain disruptions in a developing country. The study is valuable because of increasing globalization, outsourcing, and the economic role of developing countries.

The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.

Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.

The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.

Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

To facilitate innovative software development, more and more software development teams (SDTs) turn to agile methods. Such agile methods develop both extensive and efficient software responses to a client’s requirement change. However, the antecedents of successful agile software development are poorly understood. The authors goal is to propose a model of how power asymmetry and paradoxical leadership interact and affect agility in SDTs, which in turn affect their capacity to innovate. By leveraging insights from research on individuals’ cognition, the authors argue that developers with relatively higher power evaluate their contributions to their teams more ambivalently, which increases their delay or postponement of their contributions to their teams’ tasks. As a result, power asymmetry is negatively related to software teams’ response extensiveness and efficiency. Second, and drawing on leadership studies on behavioral complexity, the authors consider the moderating role of paradoxical leadership that a team receives as an important moderating factor to this effect. The authors argue that, when team leaders exhibit paradoxical leadership behaviors, high-power individuals’ ambivalence is less likely to emerge; hence, transforming power asymmetry to an asset for the enhancement of agility in the SDT.

As the normality concept for frictional dilatant material has a serious drawback, the key feature in this numerical study is that the material here is characterized by elastic-viscoplastic constitutive relation with plastic non-normality effect for two different hardness functions. The paper aims to discuss this issue.

Quasi-static, mode I plane strain crack tip fields have been investigated for a plastically compressible isotropic hardening–softening–hardening material under small-scale yielding conditions. Finite deformation, finite element calculations are carried out in front of the crack with a blunt notch. For comparison purpose a few results of a hardening material are also provided.

The present numerical calculations show that crack tip deformation and the field quantities near the tip significantly depend on the combination of plastic compressibility and slope of the hardness function. Furthermore, the consideration of plastic non-normality flow rule makes the crack tip deformation as well as the field quantities significantly different as compared to those results when the constitutive equation exhibits plastic normality.

To the best of the authors’ knowledge, analyses, related to the constitutive relation exhibiting plastic non-normality in the context of plastic compressibility and softening (or softening hardening) on the near tip fields, are not explored in the literature.