Salim Ahmed, Khushboo Kumari and Durgeshwer Singh
Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous…
Abstract
Purpose
Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.
Design/methodology/approach
The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.
Findings
Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.
Social implications
Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.
Originality/value
This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.
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Patrick Ajibade and Ndakasharwa Muchaonyerwa
This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and…
Abstract
Purpose
This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and the need for the graduates to be equipped with analytics skills. Combined with basic data, text mining and analytics, knowledge classification and information audit skills would benefit libraries and improve resource allocation. Agile institutional libraries in this big data era success hinge on the ability to perform depth analytics of both data and text to generate useful insight for information literacy training and information governance.
Design/methodology/approach
This paper adopted a living-lab methodology to use existing technology to conduct system analysis and LMS audit of an academic library of one of the highly ranked universities in the world. One of the benefits of this approach is the ability to apply technological innovation and tools to carry out research that is relevant to the context of LIS or other research fields such as management, education, humanities and social sciences. The techniques allow us to gain access to publicly available information because of system audits that were performed. The level of responsiveness of the online library was accessed, and basic information audits were conducted.
Findings
This study indicated skill gaps in the LIS training and the academic libraries in response to the fourth industrial technologies. This study argued that the role of skill acquisition and how it can foster data-driven library management operations. Hence, data mining, text mining and analytics are needed to probe into such massive, big data housed in the various libraries’ repositories. This study, however, indicated that without retraining of librarians or including this analytics programming in the LIS curriculum, the libraries would not be able to reap the benefits these techniques provided.
Research limitations/implications
This paper covered research within the general and academic libraries and the broader LIS fields. The same principle and concept is very important for both public and private libraries with substantial usage and patrons.
Practical implications
This paper indicated that librarianship training must fill the gaps within the LIS training. This can be done by including data mining, data analytics, text mining and processing in the curriculum. This skill will enable the news graduates to have skills to assist the library managers in making informed decisions based on user-generated content (UGC), LMS system audits and information audits. Thus, this paper provided practical insights and suggested solutions for academic libraries to improve the agility of information services.
Social implications
The academic librarian can improve institutional and LMS management through insights that are generated from the user. This study indicated that libraries' UGC could serve as robust insights into library management.
Originality/value
This paper argued that the librarian expertise transcends information literacy and knowledge classification and debated the interwoven of LMS and data analytics, text mining and analysis as a solution to improve efficient resources and training.
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Ketshepileone Shiela Matlhoko, Jana Franie Vermaas, Natasha Cronjé and Sean van der Merwe
The South African wool industry is integral to the country's agricultural sector, particularly sheep farming and wool production. Small-scale farmers play a vital role in this…
Abstract
Purpose
The South African wool industry is integral to the country's agricultural sector, particularly sheep farming and wool production. Small-scale farmers play a vital role in this industry and contribute to employment and food security in rural communities. However, these farmers face numerous challenges, including a lack of funding, poor farming practices and difficulty selling their wool at fair prices. This study aims to address these challenges, the University of Free State launched a wool value chain project for small-scale farmers.
Design/methodology/approach
In this project, one of the studies conducted assessed the effectiveness of different detergents suitable for traditional wool scouring methods for small-scale farmers who lack access to sophisticated machinery. The investigation was conducted by scouring 160 wool samples using three different detergents and filtered water as a control. The wool samples were then evaluated for their cleanliness, brightness and fibre properties through a combination of scanning electron microscopy, spectrophotometry and statistical analysis at different scouring times (3, 10, 15 and 20 min, respectively).
Findings
The results showed that the combination of scouring time and the type of scouring solution used could significantly impact wool quality. It was found that using a combination of standard detergent or Woolwash as a scouring solution with a scouring time of 10–15 min resulted in the best outcome in terms of fibre property, wool colour and scouring loss.
Originality/value
This study demonstrated that traditional wool scouring methods could be an option for small-scale farmers and anyone who want to learn how to scour wool without expensive machinery to make wool products.
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Nair Ul Islam and Ruqaiya Khanam
This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI…
Abstract
Purpose
This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.
Design/methodology/approach
Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.
Findings
The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.
Originality/value
This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.