Corporate Governance-Firm Performance Relationship: Empirical Evidence from African Countries. A Principal Components Analysis

This study investigated the relationship between corporate governance and firm performance, with evidence from Africa countries. The review of empirical studies from African countries had established a relationship between corporate governance and firm performance without consensus on a particular kind of relationship; while the result of the analysis using data on the return on assets, returns on equity, price earnings ratio, Tobin’s Q and constructed Performance Index as measures of firm performance and ownership structure as a measure of corporate governance revealed that corporate governance has a positive significant relationship with firm performance in Nigeria. The study concluded that though the agency costs of firms are very high, sound corporate governance is usually responsible for the positive performance of firms across African countries. Keywords: Corporate Governance, Agency theory, Firm Performance. DOI: 10.7176/EJBM/12-17-07 Publication date:June 30th 202

A Rough Set Approach to Dimensionality Reduction for Performance Enhancement in Machine Learning

Machine learning uses complex mathematical algorithms to turn data set into a model for a problem domain. Analysing high dimensional data in their raw form usually causes computational overhead because the higher the size of the data, the higher the time it takes to process it. Therefore, there is a need for a more robust dimensionality reduction approach, among other existing methods, for feature projection (extraction) and selection from data set, which can be passed to a machine learning algorithm for optimal performance. This paper presents a generic mathematical approach for transforming data from a high dimensional space to low dimensional space in such a manner that the intrinsic dimension of the original data is preserved using the concept of indiscernibility, reducts, and the core of the rough set theory. The flue detection dataset available on the Kaggle website was used in this research for demonstration purposes. The original and reduced datasets were tested using a logistic regression machine learning algorithm yielding the same accuracy of 97% with a training time of 25 min and 11 min respectively

Conceptualising the transformative impact of generative AI on the humanisation of brand creative narrative practices

This paper explores the emerging and inherent tensions that exist in integrating generative artificial intelligence (GAI), creative narrative routines and practices (branded storytelling) and the humanisation of brand communications. By integrating across multiple theoretical perspectives, the paper offers conceptualisation that provide novel syntheses and typologies as emergent propositions that enable a better understanding of how GAI is impacting on brand creative narrative routines and practices. We explore the impact of GAI on sentient creative narrative practices and deconstruct the technology-human interaction at play in the humanisation of brand creative narratives. The paper argues that achieving the delicate balance between data-driven precision and emotional authenticity becomes imperative for brands aiming to establish deep connections with their audience. The paper offers conceptual contributions for how digital marketing agencies and GAI adopting marketing teams can improvise innovation by redefining creative narrative dynamic capabilities for evolutionary fitness and sustained advantage of the firm

Impacts of Work Environment on Health Status of Traffic Compliance and Enforcement Corps Members in Ogun State. Nigeria

Background: Traffic Compliance and Enforcement (TRACE) Corps members in Nigeria are occupationally exposed to lots of environmental pollutants due to essential services that they render in the transport industry. Exposure to environmental pollutants has been shown to contribute to the pathogenesis and progression of cardiovascular diseases (CVD). Information about biochemical end points of CVD in Traffic Compliance and Enforcement (TRACE) Corps members who are occupationally exposed to environmental pollutants is lacking. This study therefore investigated the effects of these environmental pollutants on some indices of CVD. Methods: Before enrollment in the study, all subjects, including controls, were informed about the objectives and requirements of the study, as well as the risks and discomfort that might be involved in participating in the study. After this exercise, a total of 234 subjects consented to participate in the study. TRACE (n=195) in various zones in Ogun State. Staff and students at Federal University of Agriculture, Abeokuta (FUNAAB) served as control subjects (n=39). A careful history of their dietary habits and job experience, as well as a detailed history of their occupational habit, was taken. Anthropometric and clinical parameters were assessed using standard methods while biochemical indices of CVD were determined spectrophotometrically using commercial diagnostic kits. One-way analysis of variance (ANOVA) followed by Duncan test was used to analyze the results with p\u3c0.05 considered significant. The relationships between plasma lipids and the anthropometric parameters were also analyzed using Pearson correlations. Results: Results showed that the systolic blood pressure in both male and female TRACE subjects were significantly higher than the control subjects. The diastolic blood pressure remained the same in both the control and TRACE subjects. The pulse in the TRACE male subject was significantly lower than the control subject. The body mass index (BMI), waist circumference and hip circumference of the TRACE female were significantly higher than the control subjects. The umbilical cord circumference of the TRACE male and female subjects were significantly higher than their control counterpart. Plasma Cholesterol in TRACE male was higher than the control male while the plasma cholesterol was lower in TRACE female compared to the control female. Plasma triacylglycerol and phospholipid remain the same in all subjects. HDL cholesterol in the TRACE subjects were 65% and 71% of the control male and control female respectively. Plasma arylesterase in TRACE male and female subjects were both 1.39 times lower than their control counterparts. There was also a significant positive correlation between the plasma cholesterol and weight (r = 0.130; p = 0.047), plasma cholesterol and BMI (r = 0.157; p = 0.021), Plasma triacylglycerol and Pulse (r = 0.130; p = 0.048). The average traffic density was 37 vehicles/min in the sampled TRACE zones. Conclusion: Findings such as increase in systolic blood pressure, anthropometric parameters and lower HDL cholesterol, plasma arylesterase in the TRACE subjects compared to the control from this study indicates that environmental pollutant exposure may disrupt lipid homeostasis and predisposes the TRACE subjects to development of CVD

Genome-wide association study of prevalent and persistent cervical high-risk human papillomavirus (HPV) infection

Background: Genetic factors may influence the susceptibility to high-risk (hr) human papillomavirus (HPV) infection and persistence. We conducted the first genome-wide association study (GWAS) to identify variants associated with cervical hrHPV infection and persistence. Methods: Participants were 517 Nigerian women evaluated at baseline and 6 months follow-up visits for HPV. HPV was characterized using SPF10/LiPA25. hrHPV infection was positive if at least one carcinogenic HPV genotype was detected in a sample provided at the baseline visit and persistent if at least one carcinogenic HPV genotype was detected in each of the samples provided at the baseline and follow-up visits. Genotyping was done using the Illumina Multi-Ethnic Genotyping Array (MEGA) and imputation was done using the African Genome Resources Haplotype Reference Panel. Association analysis was done for hrHPV infection (125 cases/392 controls) and for persistent hrHPV infection (51 cases/355 controls) under additive genetic models adjusted for age, HIV status and the first principal component (PC) of the genotypes. Results: The mean (±SD) age of the study participants was 38 (±8) years, 48% were HIV negative, 24% were hrHPV positive and 10% had persistent hrHPV infections. No single variant reached genome-wide significance (p < 5 X 10− 8). The top three variants associated with hrHPV infections were intronic variants clustered in KLF12 (all OR: 7.06, p = 1.43 × 10− 6). The top variants associated with cervical hrHPV persistence were in DAP (OR: 6.86, p = 7.15 × 10− 8), NR5A2 (OR: 3.65, p = 2.03 × 10− 7) and MIR365–2 (OR: 7.71, p = 2.63 × 10− 7) gene regions. Conclusions: This exploratory GWAS yielded suggestive candidate risk loci for cervical hrHPV infection and persistence. The identified loci have biological annotation and functional data supporting their role in hrHPV infection and persistence. Given our limited sample size, larger discovery and replication studies are warranted to further characterize the reported associations

On XLE index constituents’ social media based sentiment informing the index trend and volatility prediction

Collective intelligence represented as sentiment extracted from social media mining found applications in various areas. Numerous studies involving machine learning modelling have demonstrated that such sentiment information may or may not have predictive power on the stock market trend. This research investigates the predictive information of sentiment regarding the Energy Select Sector related XLE index and of its constituents, on the index and its volatility, based on a novel robust machine learning approach. While we demonstrate that sentiment does not have any impact on any of the trend prediction scenarios investigated here related to XLE and its constituents, the sentiment’s impact on volatility predictions is significant. The proposed volatility prediction modelling approach, based on Jordan and Elman recurrent neural networks, demonstrates that the addition of sentiment or sentiment moment reduces the prediction root mean square error (RMSE) to about one third. The experiments we conducted also demonstrate that the addition of sentiment reduces the RMSE for 24 out of the 36 stocks/constituents, representing 87.9% of the index weight. This is the first study in the literature relating to the prediction of the market trend or the volatility based on an index and its constituents’ sentiment

The Social and Political Dimensions of the Ebola Response: Global Inequality, Climate Change, and Infectious Disease

The 2014 Ebola crisis has highlighted public-health vulnerabilities in Liberia, Sierra Leone, and Guinea – countries ravaged by extreme poverty, deforestation and mining-related disruption of livelihoods and ecosystems, and bloody civil wars in the cases of Liberia and Sierra Leone. Ebola’s emergence and impact are grounded in the legacy of colonialism and its creation of enduring inequalities within African nations and globally, via neoliberalism and the Washington Consensus. Recent experiences with new and emerging diseases such as SARS and various strains of HN influenzas have demonstrated the effectiveness of a coordinated local and global public health and education-oriented response to contain epidemics. To what extent is international assistance to fight Ebola strengthening local public health and medical capacity in a sustainable way, so that other emerging disease threats, which are accelerating with climate change, may be met successfully? This chapter considers the wide-ranging socio-political, medical, legal and environmental factors that have contributed to the rapid spread of Ebola, with particular emphasis on the politics of the global and public health response and the role of gender, social inequality, colonialism and racism as they relate to the mobilization and establishment of the public health infrastructure required to combat Ebola and other emerging diseases in times of climate change

YOLOv7 Applied to Livestock Image Detection and Segmentation Tasks in Cattle Grazing Behavior, Monitor and Intrusions

You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLO version 7 (YOLOv7) model is a variant of YOLO. The objective of this paper is to apply YOLOv7 to livestock image detection and segmentation tasks in cattle grazing behavior, monitor and intrusions. Data obtained revealed that YOLOv7 performs better in terms of speed and accuracy with a mAP of 0.95 than the baseline techniques

Perceptions about mental healthcare for people with epilepsy in Africa.

BACKGROUND: Mental illness is commonly comorbid with epilepsy. In sub-Saharan Africa there exists limited access to neurological and psychiatric services predisposing to a "treatment gap" in epilepsy and mental healthcare. AIMS: To understand healthcare providers' knowledge, attitudes, and management practices toward epilepsy and comorbid mental illness in sub-Saharan Africa. METHODS: A cross-sectional online survey following the STROBE guidance was conducted among healthcare providers in sub-Saharan Africa. Eleven questions looking to ascertain clinician demographics, knowledge of epilepsy, and comorbid mental illness as well as management practices were developed. FINDINGS: Of 203 responses most (92%) respondents recognized a bi-directional relationship between mental health and epilepsy. However, mental illness screening in people newly diagnosed with epilepsy was infrequently performed (14%). Only 1 in 7 (16%) respondents had high confidence in their clinical competence at managing psychiatric comorbidities. Most would value further training (74%) and improvements to be made in current management practices within their local healthcare settings (94%). CONCLUSIONS: This pilot study highlights the need to improve the awareness of management of mental disorders in populations with epilepsy within sub-Saharan Africa in health providers there

Predicting S&P 500 based on its constituents and their social media derived sentiment

Collective intelligence, represented as sentiment extracted from social media mining, is encountered in various applications. Numerous studies involving machine learning modelling have demonstrated that such sentiment information may or may not have predictive power on the stock market trend, depending on the application and the data used. This work proposes, for the first time, an approach to predicting S&P 500 based on the closing stock prices and sentiment data of the S&P 500 constituents. One of the significant complexities of our framework is due to the high dimensionality of the dataset to analyse, which is based on a large number of constituents and their sentiments, and their lagging. Another significant complexity is due to the fact that the relationship between the response and the explanatory variables is time-varying in the highly volatile stock market data, and it is difficult to capture. We propose a predictive modelling approach based on a methodology specifically designed to effectively address the above challenges and to devise efficient predictive models based on Jordan and Elman recurrent neural networks. We further propose a hybrid trading model that incorporates a technical analysis, and the application of machine learning and evolutionary optimisation techniques. We prove that our unprecedented and innovative constituent and sentiment based approach is efficient in predicting S&P 500, and thus may be used to maximise investment portfolios regardless of whether the market is bullish or bearish