Search results

The purpose of this investigation was to evaluate the protective ability of 2-amino-N-octadecylacetamide (AOA) and 2-amino-N-octadecyl-3-(4-hydroxyphenyl) propionamide (AOHP) as corrosion inhibitors for N80 steel in 15 per cent hydrochloric acid (HCl), which may find application as eco-friendly corrosion inhibitors in acidizing processes in the petroleum industry. Due to scale plugging in the well bore, there can be a decline in the crude production rate, and an acidization operation has to be carried out, normally by using 15 per cent HCl to remove the scale plugging. To reduce the aggressive attack of HCl on tubing and casing materials (N80 steel), inhibitors are added to the acid solution during the acidifying process.

Different concentrations of the synthesized inhibitors AOA and AOHP were added to the test solution (15 per cent HCl), and the corrosion inhibition efficiencies of these inhibitors for N80 steel were calculated from weight loss determinations, potentiodynamic polarization scans and alternating current (AC) impedance measurements. The influence of temperature (298-323 K) on the inhibition behavior was studied. Surface examinations were performed by means of Fourier transform infrared spectra and scanning electron microscope.

AOA and AOHP at 150-ppm concentration showed a maximum efficiency of 90.04 and 94.97 per cent, respectively, at 298 K in 15 per cent HCl solution. Both the inhibitors acted as mixed corrosion inhibitors. The adsorption of the corrosion inhibitors at the surface of the N80 steel was the underlying mechanism of corrosion inhibition.

This paper reports the preliminary laboratory results of inhibitors AOA and AOHP for the corrosion prevention of N80 steel casings and tubulars exposed to HCl and may be of practical help to petroleum engineers for carrying out acidization in oil wells after further investigation of the compound at higher temperature.

The study aims to explore the dimensions of Quality 4.0 adoption, prioritization of these dimensions and the influential dimensions and their causal relationships that can guide the smooth adoption of Quality 4.0 to boost organizational performance.

The Quality 4.0 dimensions are explored from the extant literature. The qualitative data were captured from 12 highly experienced experts from diverse industries and academia through structured interview questions and group discussions in multiple phases. The inputs obtained from the experts were analyzed using Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution for dimension priority, and Fuzzy-Decision-Making Trial and Evaluation Laboratory was employed to reveal the influential relationship between them.

The analysis reveals that quality scalability, quality culture and quality conformance are investigated as primary drivers of Quality 4.0 adoption. Data-driven analytical thinking and customer centricity emerge as dynamic dimensions that act as quality deliverable ends. Integrating these methodologies provides a robust framework for understanding and managing Quality 4.0 complexities, offering actionable insights for prioritizing initiatives and addressing interdependencies to ensure successful adoption and implementation.

The practical implications guide industries in creating strategic action plans tailored to their needs and fostering a quality-focused culture. The study also offers valuable insights into government policies, promoting sustainability, efficiency and a circular economy.

The study’s novelty lies in its prioritization and examination of the most influential causes and effects within the Quality 4.0 dimensions. This approach highlights core drivers and critical factors, providing a comprehensive framework for successful implementation.

The purpose of this study is to comprehensively analyze the research article retractions in social sciences over the past decade (2014–2023).

The study used scientometric methods to evaluate the prevalence, patterns and factors contributing to social sciences article retractions. Bibliographic data of retracted articles were collected from the Retraction Watch Database under an agreement signed with the database. Further, citations of the retracted articles were collected from Scopus and Google Scholar. The analysis encompasses performance assessment and citation-based analysis to reveal the trend of retraction and scrutinize the impact of retracted articles.

Over the past decade, article retractions have shown dynamic trends, with notable fluctuations in recent years. Further, investigating the time taken for article retraction reveals the urgency of addressing issues identified soon after publication. Scientific misconduct and publication-related concerns emerge as primary factors leading to retractions. Countries such as Russia, the USA, China and publishers such as Elsevier and Taylor and Francis led in the retractions of social science articles. A significant portion of retracted works had garnered academic attention prior to retraction and even after retraction.

This study can contribute to a better understanding among scholars and stakeholders of the trends and reasons for retractions of research articles in the social sciences.

This paper aims to use hybrid manufacturing (HM) to overcome several drawbacks of material extrusion three-dimensional (3D) printers, such as low dimension ranging from 0.2 to 0.5 µm, resulting in a noticeable staircase effect and elevated surface roughness.

Subtractive manufacturing (SM) through computer numerical control milling is renowned for its precision and superior surface finish. This study integrates additive manufacturing (AM) and SM into a single material extrusion 3D printer platform, creating a HM system. Two sets of specimens, one exclusively printed and the other subjected to both printing and milling, were assessed for dimension accuracy and surface roughness.

The outcomes were promising, with postmilling accuracy reaching 99.94%. Significant reductions in surface roughness were observed at 90° (93.4% decrease from 15.598 to 1.030 µm), 45° (89% decrease from 26.727 to 2.946 µm) and the face plane (71% decrease from 12.176 to 3.535 µm).

The 3D printer was custom-built based on material extrusion and modified with an additional milling tool on the same gantry. An economic evaluation based on cost-manufacturing demonstrated that constructing this dual-function 3D printer costs less than US$560 in materials, offering valuable insights for researchers looking to replicate a similar machine.

The modified general 3D printer platform offered an easy way to postprocessing without removing the workpiece from the bed. This mechanism can reduce the downtime of changing the machine. The proven increased dimension accuracy and reduced surface roughness value increase the value of 3D-printed specimens.

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

The purpose of this paper is to focus on the prediction of Coronavirus 2019 (COVID-19) using X-ray image.

This study proposed convolutional neural network (CNN) approach to predict COVID-19.

Prediction of COVID-19 using CNN.

The work has implemented multiple CNN models to classify chest X-ray of affected patients by using their chest scans. According to three models, the ResNet-50 is advantageous because of its high service reliability.

The COVID-19 pandemic, starting in early 2020, has significantly compromised global commitment to the 2030 Agenda for Sustainable Development Goals, notably affecting areas like food security (SDG 2) and the economy (SDG 8). Informal economy platform employees have been among the most impacted. In India alone, 7.7 million workers in the informal economy have suffered, with nearly 90% of unskilled and semi-skilled workers experiencing income loss. The widespread income loss among a significant portion of the workforce has led to disruptions in demand and supply mechanisms, thereby worsening food insecurity. This study investigates the determinants of the food consumption score (FCS) to serve as an indicator of food security within informal-economy households. A longitudinal survey of 2,830 unskilled and semi-skilled employees, including drivers, domestic workers, delivery personnel, beauticians, street vendors, small business owners, and self-employed individuals, was conducted. The findings show a significant shift towards borderline household FCS during the pandemic, with a sharp decline in daily consumption of dairy products and non-vegetarian items, indicating reduced protein intake. Consuming two or fewer meals per day increases the likelihood of poor FCS, highlighting the need for systematic interventions to ensure three regular meals per day. Moreover, insufficient government support for adequate food intake in informal economy households calls for redesigned assistance programs. Policymakers should prioritize practical solutions, such as community-based food distribution centers and mobile food vans, to ensure the delivery of nutritious food to vulnerable populations in Bangalore.

Mauritius is a Small Island Development State (SIDS) with limited resources, and it has been witnessed that many containers used for storing household and industrial products are made from plastic. When discarded as waste, those plastic containers pose a serious environmental and economic challenge for Mauritius. Moreover, landfill space is getting increasingly scarce, and plastic waste is contaminating both land and water. Therefore, it is of the utmost necessity to develop solutions for Mauritius' plastic wastes. Due to its abundance and accessibility, plastic waste is a promising material for recycling and energy production. One potential solution is the use of machine learning and artificial intelligence (AI) to predict household plastic consumption, allowing policymakers to design effective strategies and initiatives to reduce plastic waste. Such information is a critical component to be able to efficiently plan for the collection and routing of trucks when collecting recyclable plastics. The development of new strategies for the recycling of plastic waste and development of new industry can address the import and export potential of the country to achieve self-sustainability as well as contribute to reduction in plastic pollution and amount of waste landfilled. These plastics can thereafter be used effectively for recycling and for the making of 3D printing filaments which fall under the SDGs 9 (Industry, Innovation and Infrastructure) and 12 (Responsible consumption and production).

The purpose of this paper is to identify and screen continuous innovation capability enablers (CICEs) in Indonesia’s manufacturing sectors, develop a relationship among these enablers and determine their driving power and dependence power in the sector.

The initial CICEs identification process is based on a literature review, while a fuzzy Delphi method (FDM) was used for the screening process of CICEs. Total interpretive structural modelling (TISM) was used to develop contextual relationships among various CICEs. The results of the TISM are used as an input for the matrix of cross-impact multiplications applied to classification (MICMAC) to classify the driving power and dependence powers of the CICEs.

This paper selected 16 CICEs classified in seven dimensions. TISM results and MICMAC analysis show that leadership, as well as climate and culture, are enablers with the highest driving power and lowest dependence powers; followed by information technology. The results of this study indicate that efforts to continuously develop innovation capabilities in the Indonesian manufacturing industries are strongly influenced by their leadership capability, climate and culture, also information technology-related capability.

The framework assessed in this study provides business managers and policymakers to obtain a bigger picture in developing policies with evidence-based strategy and priority in regard to continuous innovation capability.

The results will be useful for business managers and policymakers to understand the relationship between CICEs and identify key CICEs in Indonesia’s manufacturing sectors, which were previously non-existent.