Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural deep eutectic solvents-based green extraction of vanillin: optimization, purification, and bioactivity assessment

The sustainable extraction of natural compounds has recently attracted significant attention. The extraction of high-quality natural vanillin in active form is crucial for its efficient use in various industries, but conventional solvents are not suitable for this purpose. The flammability, volatility, and toxicity of organic solvents can harm extraction personnel, and their waste liquid can cause environmental pollution. Natural deep eutectic solvents (NADES) are cost-effective, environmentally friendly, biodegradable, and non-toxic organic alternative to conventional solvents. In this study, 20 different NADES were tested for the sustainable extraction of natural vanillin. Among these, a DES system composed of choline chloride: 1,4-butanediol: lactic acid exhibited the highest extraction rate (15.9 mg/g). Employing response surface methodology (RSM), optimal extraction conditions were determined, yielding a vanillin content 18.5 mg/g with water content of 33.9%, extraction temperature of 64.6°C, extraction time of 32.3 min, and a solid-liquid ratio of 44.9 mg/mL. Subsequently, the optimized NADES system was then assessed for reusability in extracting vanillin from vanilla pods and kraft lignin over three cycles, retaining 43% of its extraction efficiency and demonstrating potential for waste reduction. Purification of vanillin was achieved through chromatography using a non-polar resin SP700, with ethanol as a desorption eluent and a feed solution pH of 4.0, resulting in the highest vanillin purity. HPLC and GC-MS analyses confirmed purity, while antioxidant activity assays (DPPH and ABTS) showcased significant antioxidant activity of the purified vanillin. Moreover, vanillin exhibited notable antimicrobial activity against a panel of food-borne bacteria. This study introduces an environmentally friendly approach to vanillin extraction highlights using NADES, emphasizing the potential for producing high-quality bioactive vanillin with reduced environmental impact. The applicability of NADES systems extends beyond vanillin, offering a versatile method for extracting diverse natural compounds.
“We know what we do”: an agency-structure analysis of political vigilantism in Ghana

Political vigilantism has come out as an offshoot of the violence that characterises multi-party elections in Ghana. Several studies on political vigilantism give primacy to the role of structural factors in explaining the phenomenon in Ghana. However, such a notion deprives political vigilantes of any sense of agency and further evades how the understanding of the interaction between agency and structure can offer deeper insights into the phenomenon of political vigilantism. The paper argues that political vigilantes should be characterised as agents who can make decisions about the actions that they undertake and not solely determined by the constraints of structure. The actions of political vigilantes are mediated through social structures that shape the options available to them. Using purposive and snowball sampling techniques, 41 key informants were selected for the study in Accra, Kumasi and Tamale. The study employed both primary and secondary data sources. Whereas secondary data was sourced from desk reviews, primary data was collected through interviews and focus group discussions. The narratives were thematically analysed. The study findings show that members of political vigilante groups are conscious agents and for that matter decide and act either autonomously or relationally despite existing pressures from social structures. In addition, the paper shows that the exercise of agency among the vigilantes is inseparable from structure as the structure is reproduced and changed through the actions of the political vigilantes who in turn are shaped through the structures that they occupy. Therefore, policy responses to political violence should consider the two sides of the equation by working simultaneously on the behaviour of political actors and resolving the circumstances that facilitate political violence.
Clustering and GNN prediction with DrugMatrix

In this paper, we propose a novel metric to characterize drug molecules based on their interaction with genes, to tackle dimensionality challenges with the DrugMatrix toxicogenomics dataset. We developed a graph neural network (GNN) that is able to accurately predict this metric and produce informative graph-level vector representations that represent relative similarity between drug molecules, by capturing both structural and functional information of drug molecules. The GNN’s resulting embedding vector representations achieve better performance than both traditional fingerprint representations and the functional property data, in clustering tasks. With its demonstrated efficacy, there is potential for further advancements in the field of toxicogenomics and future applications of GNNs in high-dimensional data analysis.
Strategic human resource management in the 21st century: A review of trends and innovations

In the dynamic landscape of the 21st century, Strategic Human Resource Management (SHRM) stands as a pivotal element for organizational success. This paper reviews the trends and innovations that characterize the evolution of SHRM in contemporary workplaces. As organizations navigate the complexities of a globalized and technologically driven environment, SHRM has witnessed a paradigm shift from a traditional administrative role to a strategic partner in achieving business objectives. The paper explores key trends, such as the integration of technology in HR processes, the rise of remote work, and the increasing importance of employee well-being in shaping SHRM strategies. Innovations in data analytics and artificial intelligence have revolutionized HR functions, enabling data-driven decision-making and predictive analytics for talent management. The paper highlights how these technological advancements have streamlined recruitment, performance management, and employee engagement, thereby enhancing the overall efficiency of SHRM practices. The emergence of remote work as a mainstream paradigm has prompted organizations to reassess traditional approaches to talent acquisition, development, and retention. Strategies for managing a dispersed workforce and fostering a cohesive organizational culture are explored, emphasizing the need for adaptability in SHRM frameworks. Moreover, the paper delves into the growing recognition of employee well-being as a critical component of SHRM. Innovations in this regard encompass initiatives promoting work-life balance, mental health support, and inclusive workplace cultures. Organizations are increasingly acknowledging that a focus on employee well-being not only enhances morale and productivity but also contributes to long-term organizational sustainability. This paper provides a comprehensive overview of the trends and innovations shaping Strategic Human Resource Management in the 21st century. It underscores the pivotal role of SHRM in navigating the complexities of the modern workplace, emphasizing the integration of technology, the impact of remote work, and the prioritization of employee well-being as key drivers in shaping strategic HR practices.
Minutiae-based Triangular Acreage Fingerprint Match Method

Fingerprint recognition systems are the top topics of biometric research and more researchers’ effort is provided to improve high accuracy and speed. Matching methods based on the minutiae points are commonly used and are effective in practice. Algorithms which based on minutia triplets among them are attractive matchers. However, recognizing fingerprints in poor quality images, direction changed, size different fingerprints and so forth is still a very complex job. To overcome these drawbacks, a fingerprint match method used minutiae-based triangular acreage is described here to improve matching accuracy
Enhancing Social Media Safety with Automated Toxicity Classification using Semantic Topic Models

In social media platforms, there is a growing interest in developing automated solutions for toxicity classification using machine learning algorithms. This paper presents a novel approach to automated toxicity classification using semantic topic models, achieving high accuracy and transparency. The report emphasizes the importance of detecting and adjusting for biases to ensure fair categorization. Moreover, the report highlights the challenges of natural language processing tasks, importance of understanding and addressing potential biases in machine learning models. Datasets are produced by the Origami project at Google, labeled by human labelers with adjectives such as "toxic," "very poisonous," "accusation," "danger," "obscene," and "identity hatred. " From the analysis, it shows that proposed method achieved better results on toxicity classification in terms of accuracy (0.85), precision (0.87), recall (0.84) and f1-score (0.85) respectively. For non-toxicity classification, the proposed method achieves accuracy (0.92), precision (0.89), recall (0.88), f1-score (0.88) and AUC-ROC (0.96) respectively.
Compartment-based reconstruction of acquisition-weighted 31P cardiac MRSI reduces sensitivity to cardiac motion and scan planning

Motivation: 31P magnetic resonance spectroscopic imaging (31P MRSI) is a powerful technique for investigating the metabolic effects of treatments for heart failure in vivo, allowing a better understanding of their mechanism of action in patient cohorts. Unfortunately, cardiac 31P MRSI is fundamentally limited by low SNR, which leads to compromises in acquisition, such as no cardiac or respiratory gating or low spatial resolution, in order to achieve reasonable scan times. Spectroscopy with linear algebra modeling (SLAM) reconstruction may be able to address these challenges and therefore improve repeatability by incorporating a segmented localizer into the reconstruction. Methods: Six healthy volunteers were scanned twice in a test–retest procedure to allow quantification of repeatability. Each scan consisted of anatomical localizers and two acquisition-weighted (AW) 31P MRSI acquisitions, which were acquired with and without cardiac gating. Five patients with heart failure with a preserved ejection fraction were then scanned with the same 31P MRSI sequence without cardiac gating. All 31P MRSI datasets were reconstructed with both conventional Fourier transform (FT)-based reconstruction and SLAM reconstruction, which were compared statistically. The effect of shifting the 31P MRSI acquisition field of view was also investigated. Results: In the healthy volunteer cohort, the spectral fit of the SLAM reconstructions had significantly improved Cramer–Rao lower bounds (CRLBs) compared to the FT-based reconstruction of non-cardiac gated data, as well as improved coefficients of variability and repeatability. The SLAM reconstruction found a significant difference in the PCr/ATP ratio between the healthy volunteer and patient cohorts, which the FT-based reconstruction did not find. Furthermore, the SLAM reconstruction was less influenced by the placement of the field of view (FOV) of the 31P MRSI acquisition in post hoc analysis. Discussion: The experimental benefits of the SLAM reconstruction for AW data were demonstrated by the improvements in fit confidence and repeatability seen in the healthy volunteer cohort and post hoc FOV analysis. The benefit of SLAM reconstruction of AW data for clinical studies was then illustrated by the patient cohort, which suggested improved sensitivity to clinically significant changes in the PCr/ATP ratio.
New Functional Motifs for the Targeted Localization of Proteins to the Nucleolus in Drosophila and Human Cells

The nucleolus is a significant nuclear organelle that is primarily known for its role in ribosome biogenesis. However, emerging evidence suggests that the nucleolus may have additional functions. Particularly, it is involved in the organization of the three-dimensional structure of the genome. The nucleolus acts as a platform for the clustering of repressed chromatin, although this process is not yet fully understood, especially in the context of Drosophila. One way to study the regions of the genome that cluster near the nucleolus in Drosophila demands the identification of a reliable nucleolus-localizing signal (NoLS) motif(s) that can highly specifically recruit the protein of interest to the nucleolus. Here, we tested a series of various NoLS motifs from proteins of different species, as well as some of their combinations, for the ability to drive the nucleolar localization of the chimeric H2B-GFP protein. Several short motifs were found to effectively localize the H2B-GFP protein to the nucleolus in over 40% of transfected Drosophila S2 cells. Furthermore, it was demonstrated that NoLS motifs derived from Drosophila proteins exhibited greater efficiency compared to that of those from other species.
Full Analysis of Sierra Leone Bauxite and Possibilities of Bauxite Residue Filtration

Chemical analysis, classical or instrumental using ICP and the XRF spectrometry, offers complete information on elemental composition and major chemical constituents of bauxite and bauxite residues resulted from the Bayer process. The XRD diffractometric analysis provides information on quantitative mineralogical composition of these materials and it enables the real identification of main chemical combinations, whose reactivity is essential in driving the process of obtaining alumina through the Bayer process. Mineralogical components identified in the bauxite samples were Gibbsite (30-40 %), Al – goethite (20-30 %) and hematite (less than 5 %). About 40 % of the Sierra Leone bauxite mineralogical phases were found in amorphous state, but all these amorphous phases are also originating from the above crystalline components, under the excessive wearing during bauxite deposit formation and its metamorphic transformations. The minor crystalline components from bauxite are: kaolin, quartz, zircon, ilmenite and anatase, but their concentration is below 2 % (close to the detection limit of the XRD analysis). The main mineralogical components identified in red mud are the Al-goethite (35 %) and the hematite (12 %). The minor crystalline components from red mud are: gibbsite, katoite, sodalite, calcite, rutile and anatase. Experimental data of this type of bauxite have shown that a compact mass of approximately 50-55 % of bauxite passes fully untransformed through all important stages of the Bayer process. This material of uniform size and composition can form a structure with low hydraulic resistance, easy filterable both on conventional or hyperbaric steam filters.
COVID-19 Testing Trend: A Retrospective Analysis of the Three Major Pandemic Waves in Punjab, Pakistan

Background/objectives: There is some evidence in the literature of under-testing of COVID-19 cases in Pakistan. This study aims to explore COVID-19 testing trends and the factors affecting them in a lower middle-income country for future infectious disease policy-making and intervention strategies. Methodology: The study was conducted as a serial cross-sectional study during the three major peaks from March 2020 to June 2021 on 1616 participants in Punjab, Pakistan. This is the first study to explore COVID-19 testing trends in association with flu-like symptoms (FLS) and the factors affecting all three major waves in Pakistan. Results: The results show that in all three waves, only 18.8% reported COVID-19 tested despite that 86.7% thought they had already had COVID-19, with 51.3% reporting having FLS and 35.6% with exposure to FLS from their families and 19.8% of positive testing rate among their family members. Out of the survey participants, 66% received vaccination, and over 80% had their eligible family members immunized. Fear of contracting COVID-19 was 69.7% in all three waves. Factors positively associated with the uptake of testing were the age group of 31-40 years with an adjusted odds ratio of 3.27 (95% confidence interval (CI): 2.09-5.12) for the second wave and an adjusted odds ratio of 13.75 (95% CI: 9.43-20.01) for the third wave and traveling abroad with odds of 3.08 times when the reference was inland traveling. The adjusted odds ratio to test for FLS was 1.62 (95% CI: 1.21-2.16). Conclusion: In this study, there is convincing evidence of COVID-19 under-testing and thus under-reporting. This study also suggests that fear-based interventions may be counterproductive; however, economic factors such as education, employment, and traveling are significant in guiding the behavior for infectious disease prevention and management.
Chemical Fingerprinting of Petroleum Hydrocarbons

Introduction 44 Oil Hydrocarbon Fingerprinting Methodologies 47 Source-Specific Target Hydrocarbons 47 Tiered Analytical Approach 51 Revised Nordtest Methodology for Oil Spill Identification 58 Level 1 58 Level 2 59 Level 3 59 Conclusion 59 Two-Dimensional GC: An Emerging Technique for Fingerprinting Hydrocarbons 60 Chemical Composition of Oil and Petroleum Products and Spill Identification 61 Chemical Composition Features of Crude Oil 62 Background Hydrocarbons: Distinguishing Biogenic Hydrocarbons from Petrogenic Hydrocarbons in Oil-Contaminated Samples 64 General Chemical Composition Features of Refined Products 67 Light Distillates 67 Midrange Distillates 68 Classic Heavy Residual Fuel 70 Lubricating Oil 70 Waste Oil 71 PAH Fingerprints of Oils and Petroleum Products 71 Distribution of Alkylated PAH Homologues and Other EPA Priority PAHs 71 Recommended Diagnostic Ratios of PAHs 72 PAH Isomer and Cluster PAH Analysis 75 Methyl Phenanthrenes 76 Methyl Dibenzothiophenes 76 Other Relative Ratios of PAH Isomers 77
Comparison of MIMIC-III and MIMIC-IV for big data analytics of health informatics

The use of a health big data via Medical Information Mart for Intensive Care (MIMIC) data sets has significant advancements in health informatics and clinical research of electronic health records (EHRs) in hospital systems. MIMIC-III and MIMIC-IV data sets are the two latest publicly available iterations of data from electronic records that possess distinct features, variables, and structures in two separate large relational databases. This study aimed to provide big data analytics comparisons of MIMIC-III and MIMIC-IV for experiential learning of health informatics from EHRs.Both data sets were quantitatively and qualitatively evaluated based on dataset properties (data, list of tables, timeline, patient encounters, software, system), data mining and visual categories (i.e., data size, data structure and schema, features/variables, data quality and completeness, clinical focus, access and use, dashboards, and visualization types), data usability heuristics and utilization (i.e., usability, complexity, granularity, applicability, and capacity). Results showed significant difference of 100 patients across 26 tables for MIMIC-III compared to 2,520 patients across 31 tables for MIMIC-IV data sets, respectively. There were 1716 diagnoses (ICD-9) and 503 procedures (ICD-9) for MIMIC-III. There were 262 diseases categories with >1000 disease instances and >2000 treatments for MIMIC-IV with no ICD codes. Moreover, these data sets contained large (big data) charting events in MIMIC-III of 758,356 rows, and MIMIC-IV eICU with charting events of 1,477,163 for nursing, and respiratory of 176,089 rows, respectively. The results suggest that MIMIC-III provided detailed information for retrospective clinical studies and operations in critical care with high data granularity in terms of re-admission (calculated fields from its admission table), length of stay, prescriptions, caregivers, and diagnosis and procedure (ICD-9). However, it lacked clinical capacity because of no diagnosis or charting event offset times or APACHE IV (Acute Physiology and Chronic Health Evaluation) scores that were in the MIMIC-IV eICU dataset. Hence, MIMIC-IV showed higher data granularity and capacity. MIMIC-IV eICU dataset introduces enhanced data attributes, more sophisticated patient trajectory tracking at the ICU unit level, and improved detailed information from electronic records. Moreover, MIMIC-IV has high usability, complexity, and applicability to critical care but lacking hospital operational data of re-admissions, caregivers, and ICD codes that MIMIC-III contained. However, MIMIC-IV dataset contained complex data schemas of treatment strings, nurse care plans, lab results, medications, microbiology, as well as infusion drug and respiratory charting. The big data analytics of MIMIC-III and MIMIC-IV needs to be further investigated for AI applications to demonstrate its usefulness for dynamic decision-making in health care.
Multiple Embedded Inequalities and Cultural Diversity in Educational Systems: A Theoretical and Empirical Exploration

This article explores the social construction of cultural diversity in education, with a view to social justice. It examines how educational systems organize ethno-cultural difference and how this process contributes to inequalities. Theoretical resources are drawn from social philosophy as well as from recent developments in social organisation theory. It is argued that ethnic minority pupils face multiple sources of inequalities, of a social (redistributive) and cultural (recognition) nature. The first section argues that these dimensions are made more ‘durable’ through institutionalized patterns of norms and routines. The second section develops this proposal at three empirical levels: the macro-level of educational systems, where national narratives interact with structural educational patterns to shape ethnic inequalities; the intermediate level of local spaces of interdependency, where we scrutinize segregation and institutional discrimination; and, finally in this section, we look at single-school policies of difference as the product of educational ‘niches’. In the last section, the author argues that these multiple embedded inequalities significantly determine pupils' opportunities, identity and ‘capability’ building. New perspectives for multilevel approaches to inequality and qualitative international comparison are suggested.
Stress and translation : implication of 16S rRNA methylations in Escherichia coli and characterization of a toxin-antitoxin system of Sinorhizobium meliloti

Bacteria are able to live in a large variety of environments and they face constantly changing conditions. Therefore they have to adapt quickly to their metabolism using different regulations at the transcriptional and translational levels. Those types of regulation are extensively studied and well characterized. However, the implications of the ribosome in modulation of translation during stress response remains poorly understood. In this context of ribosomal regulation, the heterogeneity of the machinery could play a relevant role. Indeed, the ribosome is not an invariable particle and its components (rRNAs, r-proteins) and their modifications may vary. Modifications of ribosomal RNAs are clustered in the functional sites of the ribosome and are particularly conserved, underlying their potential importance. However their physiological role is still unclear. We focused on methylations of the 16S rRNA and investigated their role in translation under favourable and stressful conditions. We successfully demonstrated that lack of some methylations increases translation under stressful and non stressful conditions. So, lack of methylation may give an advantage to ribosomes during stress response. Another way to act on translation under stressful conditions resides in targeting mRNAs. This is particularly the case for endoribonuclease toxins that are specifically produced during detrimental conditions. Thus, we characterized S. meliloti toxin-antitoxin system HicAB. We plan to use it in order to study the response to HicA toxin of mutants lacking some ribosomal modifications.