Militant or Non-State Armed Groups (NSAGs) During/After The Resolution of The Bakassi Conflict

The Bakassi peninsula conflict and its resolution had absorbed most of Cameroon and Nigeria's socio-political and economic oxygen and the international community in the 80s, 90s, and the first decade of the 21st century. Following military clashes between Cameroon and Nigeria over the peninsula, the conflict was finally resolved through the International Court of Justice (ICJ) verdict in 2002 and the Green Tree Agreement (GTA) 2006. The main thrust of this paper is to examine some militant or Non-State Armed Groups (NSAGs) that operated during and after the resolution of the Bakassi conflict. This paper also aims to examine the reasons why these NSAGs emerged briefly. This paper applied the qualitative research method and, from it, historical consolidation, content analyses and case studies. The study's results revealed seven prominent NSAGs that emerged during/after the resolution of the Bakassi conflict. The ICJ verdict of 2002 and the GTA of 2006 were one of prominent reasons why these NSAGs emerged. The study also found that the desire to control the area's natural resources, fight against Cameroon gendarme brutality, and the poor resettlement of the Bakassi returnees also served as springboards for the emergence of these NSAGs. Through its recommendations, this paper will help the Cameroon government redefine its policies toward ensuring and maintaining lasting peace in the Bakassi peninsula- understanding the reason for the emergence of the NSAGs, its trends, and how best to handle them

Regional Differences of Hepatitis C Virus Infection in Kentucky

Objective: Few studies have been conducted in Kentucky to investigate the statewide prevalence of HCV infection and its associated risk factors. The purpose of this study was to examine the factors related to HCV infection in the state, and specifically to investigate geographical differences of HCV infection between those residing in Appalachian vs. Non-Appalachian counties in Kentucky. Methods: The study sample (n =5205) was selected from a pool of 8300 high-risk individuals participating in a pilot cross-sectional study on HCV conducted by the Kentucky Department for Public Health. The pilot study involved serologically testing participants for antibodies against HCV infection and having participants complete an interview-administered questionnaire at the same time to examine behavioral and socio-demographic characteristics related to HCV infection. Univariate, bivariate, and logistic regression analyses were carried out using SPSS and maps were produced using ArcGIS software. Frequency distribution, adjusted odds ratios (AORs), and corresponding 95% confidence intervals (95% CIs) were reported. Results: Of the 5205 participants selected (2241 males, 2964 females; mean age, 30.4 ± 10.5 years); 9.8% tested positive for anti-HCV antibodies. Residence in Appalachian vs. Non-Appalachian Kentucky was not significantly associated with HCV antibody status. In the multivariate analysis, Blacks (AOR: 0.42, 95% CI: 0.26 – 0.66) and men who have sex with men (MSM) (AOR: 0.36, 95% CI: 0.17 – 0.73) were significantly less likely to be HCV positive after adjusting for all other variables. HCV seropositivity was positively associated with age (AOR: 1.03, 95% CI: 1.02 – 1.04), history of injection drug use (IDU) (AOR: 41.27, 95% CI: 31.94 – 53.31), and presence of tattoos (AOR: 1.49, 4 95% CI: 1.14 – 1.96). Gender was also found to significantly modify the association between residence and HCV antibody status, specifically in the Appalachian region. Conclusion: This was the first statewide analysis to examine the prevalence of HCV infection among high-risk population residing in Appalachia vs. Non-Appalachian counties in Kentucky. The main variables associated with HCV infection in these regions were age, Black race, history of IDU, MSM and presence of tattoos. Addressing these risky behaviors and particular populations through age- and gender-specific preventive and treatment measures may reduce the high prevalence of HCV infection in the state of Kentucky. However, more research is required to further characterize HCV-related risk factors with respect to residence in Appalachian vs. Non-Appalachian to determine how these measures can be effectively implemented

The Violation of Human Rights during the Bakassi Peninsula Conflict from 1965 to 2013

The military skirmishes over the Bakassi peninsula between Cameroon and Nigeria was as a result of the vestiges of colonialism and the discovery of large deposits of natural resources in the area. This study explores the violations of human rights in the Bakassi peninsula during the Bakassi conflict. The study mainly explores how, when and where these human rights abuses were committed, and to a lesser extent why and who committed these abuses. The study adopts the qualitative research method and from it, historical consolidation and content analyses in terms of data collection. The use of secondary data, applied with the thematic approach in this study brings to the fore that there were lots of human rights violations during the Bakassi conflict in the Bakassi peninsula. This study could be utilised by researchers and policy makers to understand human rights trends in the Bakassi peninsula area. Keywords: Cameroon, Nigeria, Violations, Human Rights, Bakassi Peninsula Conflic

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase

Improvement of plant disease classification accuracy with generative model-synthesized training datasets

Digitalization in agriculture requires critical research into applications of artificial intelligence to various specialization domains. This work aimed at investigating the application of image synthesis technology to the mitigation of the data volume constraint to digital plant disease phenotyping accuracy. We designed an experiment involving the use of a deep convolutional generative adversarial network (DC-GAN) to synthesize photorealistic data for healthy and bacterial spot disease-infected tomato leaves. The training dataset contained 1,272 instances per class. We further employed a 3-block visual geometry group (VGG) convolutional neural network (CNN) model with dropout regularization and 1 epoch to compare classification accuracies of the original dataset and various synthetic datasets. Our results showed that the third DC-GAN synthesized training dataset containing 3,816 synthetic examples of both healthy and bacterial spot infected tomato leaf classes outperformed the original training dataset containing 1,272 real examples of both tomato leaf classes (77.088% accuracy with the former dataset on a 3-block VGG CNN model with dropout regularization and 1 epoch, as compared to 76.447% accuracy with the latter dataset on the same classifier)

Resource rents and inclusive human development in developing countries

This study aims to empirically verify the effects of natural resource rents on inclusive human development in developing countries. The results from the IV Tobit regression show that natural resource rents have a positive direct effect on inclusive human development in developing countries and that this relationship varies by regional groupings, income levels, level of development and export structure. Looking at the transmission mechanisms, when the interactive variables of governance and environmental quality is introduced, the modulating channel through governance exerts a robust negative synergy effect in the sample of developing countries and positive synergy effects for Africa and low-income countries. When the interactive variable of CO2 emissions is introduced for Africa, a negative net effect of natural resource rents on inclusive human development is obtained. This was up to a policy threshold of 25.4412 of CO2 emissions when the negative effect is nullified. For Asia and the Latin America and Caribbean, a positive net effect is obtained. This is up to a CO2 emissions threshold of 29.038 and 3.6752 respectively, when the positive effect is nullified. Besides, the high income and the upper-middle income countries produce a negative net effect of resource rents on inclusive human development through CO2 modulation, with up to positive CO2 emission thresholds of 37.9365 and 23.6257 respectively. Policy implications are highlighted. In summary, contingent on engaged specificities, where conditional effects are negative, negative thresholds for complementary policies have been provided and in scenarios where conditional impacts are positive, actionable positive thresholds have been provided

Women empowerment and environmental sustainability in Africa

This study examines the effect of women’s socioeconomic empowerment on environmental sustainability in Africa over the 1996-2019 period. Results of the system Generalised Method of Moments (GMM) estimator reveal that women’s socioeconomic empowerment is environment enhancing. Moreover, the findings reveal that the environmental impact of women’s socioeconomic empowerment is modulated through GDP per capita and Foreign Direct Investments (FDI), leading to respective net effects of 0.002055 and 0.003478. These positive net effects are offset beyond respective threshold values of 9.513889 and 9.611398. These thresholds of GDP and FDI are critical for complementary policies relating to the link between women empowerment and environmental sustainability. Consequently, for women empowerment to effectively contribute to environmental sustainability in Africa, various governments, either through individual or concerted efforts should endeavour to create enabling business environments capable of attracting substantial FDI necessary to propel sustainable growth. Moreover, the nexus is not linear and hence, governments should also be aware of critical levels of FDI and GDP per capita at which, complementary policies are needed for women’s socioeconomic empowerment to maintain a positive influence on environmental sustainability

Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection

The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using a combination of genetic markers and advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, machine learning algorithms and high-performance computing was employed. First, genetic markers highly associated with disease resistance were identified using next-generation sequencing data and statistical analysis. Then, an adiabatic quantum machine learning algorithm was developed to integrate these markers into a single predictor of disease susceptibility. The results demonstrate that the integrative use of genetic markers and adiabatic quantum machine learning significantly improved the accuracy and efficiency of disease resistance-based marker-assisted plant selection. By leveraging the power of adiabatic quantum computing and genetic markers, more effective and efficient strategies for disease resistance-based marker-assisted plant selection can be developed

Petroleum Products Price Fluctuations and Economic Growth in Cameroon

Commodity exports have over the years been the main source of foreign exchange earnings to most developing countries. This is especially the case with crude oil producing countries such as Cameroon since the discovery of oil in the late 1970s. However, as evident in the economic crises era of the mid 1980s, this exposes the commodity dependent country to heavy external shocks such as price fluctuations which affect the level of growth of the country. It is in this light that this study was conducted to examine the effect of petroleum products (crude oil) price fluctuations on the economic growth of Cameroon. Secondary data from1980 to 2013 were used to estimate the coefficients of the ordinary least square technique used to analyse the dependency between the dependent and independent variables of the phenomenon. The results obtained reveal that petroleum product prices have a positive significant effect on the economic growth of Cameroon, while the volume of trade to GDP (openness) and real interest rate have a negative significant effect on the economic growth of the country. Human factors (demand and supply imbalances, and interest rates) and natural factors (geographical location and resource endowment) are the principal causes of variations in the prices of petroleum products among different regions. From these, it is suggested that for Cameroon to benefit from the global trade process by opening up to the rest of the world, the revenue generated from crude oil exploitation should be re-directed towards investment in both human and physical capital so as to enhance the productive capacity of the nation, especially in the manufacturing and transport sectors