Markowitz Minimum Variance Portfolio Optimization using New Machine Learning Methods

The use of improved covariance matrix estimators as an alternative to the sample covariance is considered an important approach for enhancing portfolio optimization. In this thesis, we propose the use of sparse inverse covariance estimation for Markowitz minimum variance portfolio optimization, using existing methodology known as Graphical Lasso [16], which is an algorithm used to estimate the inverse covariance matrix from observations from a multivariate Gaussian distribution. We begin by benchmarking Graphical Lasso, showing the importance of regularization to control sparsity. Experimental results show that Graphical Lasso has a tendency to overestimate the diagonal elements of the estimated inverse covariance matrix as the regularization increases. To remedy this, we introduce a new method of setting the optimal regularization which shows performance that is at least as good as the original method by [16]. Next, we show the application of Graphical Lasso in a bioinformatics gene microarray tissue classification problem where we have a large number of genes relative to the number of samples. We perform dimensionality reduction by estimating graphical Gaussian models using Graphical Lasso, and using gene group average expression levels as opposed to individual expression levels to classify samples. We compare classification performance with the sample covariance, and show that the sample covariance performs better. Finally, we use Graphical Lasso in combination with validation techniques that optimize portfolio criteria (risk, return etc.) and Gaussian likelihood to generate new portfolio strategies to be used for portfolio optimization with and without short selling constraints. We compare performance on synthetic and real stock market data with existing covariance estimators in literature, and show that the newly developed portfolio strategies perform well, although performance of all methods depend on the ratio between the estimation period and number of stocks, and on the presence or absence of short selling constraints

Mental health of doctors in a tertiary hospital in Nigeria

Introduction: doctors are vulnerable to psychiatric morbidity as a result of their busy schedules and multiple role obligations. Yet, they often don't admit they have mental health problems nor are they readily subjected to mental health evaluation by their colleagues due to fear of labeling and general stigma.Methods: a cross-sectional survey of doctors in the service of University of Ilorin Teaching  Hospital, Ilorin, Nigeria was done using a socio-demographic questionnaire and the twelve items General Health Questionnaire (GHQ-12) using a cut-off point of 3 to indicate possibility of psychiatric disorder (GHQ-12 positive). Non-parametric analysis and regression test of factors associated with psychiatric  morbidity was done using SPSS. Level of significance was set at 0.05 p-value. Results: two hundred and forty one doctors representing 68.9% of the doctors participated in the survey. The point prevalence of psychiatric morbidity among the doctors using the GHQ-12 was 14.9%. Being married, non-participation in social activities and perception of work load as being "heavy" were significantly associated with  psychiatric morbidity (p-value < 0.05). Conclusion: the prevalence of psychiatric morbidity among doctors at the University of Ilorin Teaching Hospital was higher than the general population prevalence. Measures to lessen the negative effect of marriage and the perceived heavy work load on mental health of doctors, such as provision of recreational facilities within the hospital and encouragement of doctors' participation in social activities are advanced.Key words: Doctors, psychiatric morbidity, tertiary hospital, Nigeri

Association between socioeconomic factors, adverse childhood experiences, and intimate partner violence among infertile women in South Nigeria

Background: Infertility is a global reproductive health issue with medical, psychological, and socio-economic consequences. In low- and middle-income countries, it is highly stigmatized, disproportionately affecting women. Socio-economic status, adverse childhood experiences, and intimate partner violence are emerging contributors to infertility and its psychosocial burden. This study examines the relationship between socio-economic factors, adverse childhood experiences, and intimate partner violence among infertile women in Nigeria, assessing prevalence and patterns of violence. Methods: A cross-sectional study of 401 infertile women at the Rivers State University Teaching Hospital Port Harcourt, between November 2024 to February 2025 using structured questionnaires to collect data on socio-demographics, childhood adversity, and intimate partner violence. Multivariate analysis of variance and descriptive statistics were used for analysis. Results: Intimate partner violence was highly prevalent, with emotional abuse (72.8%) and physical abuse (65.3%) most common. Socio-economic status did not significantly affect overall violence risk, but low-income women were more likely to experience physical abuse (p=0.001). Adverse childhood experiences were strongly linked to emotional (p=0.000) and physical abuse (p=0.000). The combined effect of socio-economic status and adverse childhood experiences significantly increased emotional abuse and harassment (p=0.023, p=0.002). Conclusions: Infertile women in Nigeria experience a high burden of intimate partner violence, influenced by socio-economic disparities and childhood adversities. Addressing these factors through screening, trauma-informed care, and economic empowerment is essential for improving reproductive health outcomes

Mental health of doctors in a tertiary hospital in Nigeria

Abstract Introduction: doctors are vulnerable to psychiatric morbidity as a result of their busy schedules and multiple role obligations. Yet, they ofte

Patterns and predictors of female sexual dysfunction among women of reproductive age attending the gynaecology clinic at Rivers State University Teaching Hospital

Background: Female sexual dysfunction is prevalent globally, yet sexuality remains taboo in many societies, leading to infrequent discussions between women and physicians. This study assessed the patterns and predictors of female sexual dysfunction among reproductive-age women attending the gynaecology clinic at Rivers State University Teaching Hospital, Nigeria. Methods: This cross-sectional study involved 375 reproductive-age women recruited from the hospital’s gynaecological outpatient department. Data were collected via a pre-tested, interviewer-administered, semi-structured questionnaire that gathered socio-demographic information, psychological factors (anxiety, stress, and depression), and categorized levels of sexual dysfunction as low, moderate, or high. Analysis was performed using SPSS version 20, with significance set at p<0.05. Results: Many pregnant participants were in their prime reproductive years. The prevalence of sexual dysfunction was 3.5% for low, 95.4% for moderate, and 1.1% for high levels. Younger age and higher education were significantly associated (p<0.05) with lower dysfunction, and stress was significantly linked (p<0.05) to sexual dysfunction, whereas anxiety and depression were not. Conclusions: Given the high prevalence of sexual dysfunction and its impact on quality of life, healthcare providers should routinely assess sexual function and offer professional support to enhance women’s sexual well-being

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation

Auswirkungen des zukünftigen Klimawandels auf die landwirtschaftliche Produktion im tropischen West Afrika: eine Fallstudie für die Republik Benin

Environmental interlinked problems such as human-induced land cover change, water scarcity, loss in soil fertility, and anthropogenic climate change are expected to affect the viability of agriculture and increase food insecurity in many developing countries. Climate change is certainly the most serious of these challenges for the twenty-first century. The poorest regions of the world – tropical West Africa included – are the most vulnerable due to their high dependence on climate and weather sensitive activities such as agriculture, and the widespread poverty that limits the institutional and economic capacities to adapt to the new stresses brought about by climate change. Climate change is already acting negatively on the poor smallholders of tropical West Africa whose livelihoods dependent mainly on rain-fed agriculture that remains the cornerstone of the economy in the region. Adaptation of the agricultural systems to climate change effects is, therefore, crucial to secure the livelihoods of these rural communities. Since information is a key for decision-making, it is important to provide well-founded information on the magnitude of the impacts in order to design appropriate and sustainable adaptation strategies. Considering the case of agricultural production in the Republic of Benin, this study aims at using large-scale climatic predictors to assess the potential impacts of past and future climate change on agricultural productivity at a country scale in West Africa. Climate signals from large-scale circulation were used because state-of-the art regional climate models (RCM) still do not perfectly resolve synoptic and mesoscale convective processes. It was hypothesised that in rain-fed systems with low investments in agricultural inputs, yield variations are widely governed by climatic factors. Starting with pineapple, a perennial fruit crops, the study further considered some annual crops such as cotton in the group of fibre crops, maize, sorghum and rice in the group of cereals, cowpeas and groundnuts belonging to the legume crops, and cassava and yams which are root and tuber crops. Thus the selected crops represented the three known groups of photosynthetic pathways (i.e. CAM, C3, and C4 plants). In the study, use was made of the historical agricultural yield statistics for the Republic of Benin, observed precipitation and mean near-surface air temperature data from the Climatic Research Unit (CRU TS 3.1) and the corresponding variables simulated by the regional climate model (RCM) REMO. REMO RCM was driven at its boundaries by the global climate model ECHAM 5. Simulations with different greenhouse gas concentrations (SRES-A1B and B1 emission scenarios) and transient land cover change scenarios for present-day and future conditions were considered. The CRU data were submitted to empirical orthogonal functions analysis over the north hemispheric part of Africa to obtain large-scale observed climate predictors and associated consistent variability modes. REMO RCM data for the same region were projected on the derived climate patterns to get simulated climate predictors. By means of cross-validated Model Output Statistics (MOS) approach combined with Bayesian model averaging (BMA) techniques, the observed climate predictors and the crop predictand were further on used to derive robust statistical relationships. The robust statistical crop models perform well with high goodness-of-fit coefficients (e.g. for all combined crop models: 0.49 ≤ R2 ≤ 0.99; 0.28 ≤ Brier-Skill-Score ≤ 0.90). Provided that REMO RCM captures the main features of the real African climate system and thus is able to reproduce its inter-annual variability, the time-independent statistical transfer functions were then used to translate future climate change signal from the simulated climate predictors into attainable crop yields/crop yield changes. The results confirm that precipitation and air temperature governed agricultural production in Benin in general, and particularly, pineapple yield variations are mainly influenced by temperature. Furthermore, the projected yield changes under future anthropogenic climate change during the first-half of the 21st century amount up to -12.5% for both maize and groundnuts, and -11%, -29%, -33% for pineapple, cassava, and cowpeas respectively. Meanwhile yield gain of up to +10% for sorghum and yams, +24% for cotton, and +39% for rice are expected. Over the time period 2001 – 2050, on average the future yield changes range between -3% and -13% under REMO SRES–B1 (GHG)+LCC, -2% and -11% under REMO SRES–A1B (GHG only),and -3% and -14% under REMO SRES–A1B (GHG)+LCC for pineapple, maize, sorghum, groundnuts, cowpeas and cassava. In the meantime for yams, cotton and rice, the average yield gains lie in interval of about +2% to +7% under REMO SRES–B1 (GHG)+LCC, +0.1% and +12% under REMO SRES–A1B (GHG only), and +3% and +10% under REMO SRES–A1B (GHG)+LCC. For sorghum, although the long-term average future yield depicts a reduction there are tendencies towards increasing yields in the future. The results also reveal that the increases in mean air temperature more than the changes in precipitation patterns are responsible for the projected yield changes. As well the results suggest that the reductions in pineapple yields cannot be attributed to the land cover/land use changes across sub-Saharan Africa. The production of groundnuts and in particular yams and cotton will profit from the on-going land use/land cover changes while the other crops will face detrimental effects. Henceforth, policymakers should take effective measures to limit the on-going land degradation processes and all other anthropogenic actions responsible for temperature increase. Biotechnological improvement of the cultivated crop varieties towards development of set of seed varieties adapted to hotter and dry conditions should be included in the breeding pipeline programs. Amongst other solutions, application of appropriate climate-smart agricultural practices and conservation agriculture are also required to offset the negative impacts of climate change in agriculture.In vielen Entwicklungsländern gefährden Umweltprobleme wie die tiefgreifende Veränderung der Landoberfläche, Wasserknappheit, Bodendegradation und der anthropogene Klimawandel die Leistung¬sfähigkeit der Landwirtschaft und erhöhen so das Risiko von Nahrungs-mittelknappheit. Von diesen miteinander verwobenen Bedrohungen ist der Klimawandel im 21. Jahrhundert sicherlich die bedeutendste. Die höchste Vulnerabilität weisen die ärmsten Regionen der Welt – unter anderen Westafrika – auf, sowohl wegen der großen Bedeutung von klima- und wettersensitiven Wirtschaftsektoren wie der Landwirtschaft als auch wegen der verbreiteten Armut. Diese schränkt die staatlichen und wirtschaftlichen Anpassungs¬kapazitäten an die neuen Herausforderungen durch den Klimawandel ein. Westafrikanische Kleinbauern, deren Lebensunterhalt wesentlich vom traditionellen Regenfeldbau – dem Eckpfeiler der regionalen Wirtschaft – abhängt, bekommen die negativen Auswirkungen bereits zu spüren. Die Adaption der agroökonomischen Systeme an den Klimawandel ist eine unbedingte Notwendigkeit für die Sicherung der Lebensgrundlage dieser ländlichen Gebiete. Da Wissen die Basis für Entscheidungen darstellt, sind belastbare Informationen über das Ausmaß der Auswirkungen wichtig, um angemessene und nachhaltige Anpassungsstrategien zu entwickeln. Am Beispiel der Republik Benin untersucht diese Studie das Potenzial von makroskaligen klimatischen Prädiktoren zur Erfassung und Quantifizierung des potentiellen Einflusses von beobachteten und künftigen Klimaänderungen auf die landwirtschaftliche Produktion eines westafrikanischen Landes. Die Auswirkungen der großskaligen Zirkulation wurden herangezogen, da auch moderne Regionale Klimamodelle (RCMs) Schwierigkeiten haben, klein- oder mesoskalige synoptische und insbesondere konvektive Prozesse überzeugend zu simulieren. Zugrunde liegt die Annahme, dass Schwankungen des landwirtschaftlichen Ertrags in auf Regenfeldbau basierenden landwirtschaftlichen Systemen mit geringen Kapitaleinsatz zu weiten Teilen auf klimatische Faktoren zurückzuführen sind. Untersucht werden die Ananas als perennierende Pflanze sowie einige einjährige Feldfrüchte wie Baumwolle aus der Gruppe der Faserpflanzen, die Getreidearten Mais, Sorghumhirse und Reis, die Hülsenfrüchte Augenbohne und Erdnuss sowie die Knollen- und Wurzelfrüchte Maniok und Yams. Somit repräsentieren die ausgewählten Feldfrüchte die drei bekannten Photosynthese-Wege, nämlich CAM, C3 und C4. Die vorliegende Studie verwendet historische Ertragsstatistiken der Republik Benin, Beobachtungsdaten der Climate Research Unit für den monatlichen Niederschlag sowie die bodennahe Mitteltemperatur (CRU TS 3.1) und die entsprechenden Variablen simuliert durch das REMO RCM. Dieses Regionalmodell wird an seinen Rändern durch das globale Klimamodell ECHAM 5 angetrieben. Es werden Modellsimulationen mit unterschiedlichen Randbedingungen im Hinblick auf Treibhausgaskonzentrationen (die Szenarien SRES-B1 und SRES-A1B) und Veränderungen der Landbedeckung (LCC) berücksichtigt. Mittels Hauptkomponentenanalyse werden aus den CRU-Daten für den nordhemisphärischen Teil Afrikas Zeitreihen und räumliche Muster für großskalige Prädiktoren gewonnen. Um mit diesen konsistente Prädiktoren für die Simulationen zu erhalten, werden die Datenfelder des REMO RCMs auf die so gewonnenen Raummuster projiziert. Für die beobachteten Zeitreihen der Prädiktoren und die zeitliche Entwicklung der unterschiedlichen Feldfrüchte als Prädiktant werden mittels eines kombinierten Ansatzes aus kreuzvalidierten Model Output Statistics (MOS) und Bayesian Model Averaging (BMA) Techniken robuste statistische Zusammenhänge erfasst. Die resultierenden statistischen Modelle zeigen gute Performance, beispielsweise gilt für alle erzeugten Modelle 0,49 ≤ R² ≤ 0,99 und 0,28 ≤ Brier-Skill-Score ≤ 0,90. Da das REMO RCM die Hauptcharakteristika des beobachteten Klimas in Afrika erzeugt und daher die interannuelle Variabilität realistisch reproduziert, können mithilfe der zeitunabhängigen statistischen Transferfunktionen Klimaänderungssignale, gewonnen aus den simulierten Prädiktoren, in zu erwartende Veränderungen der Ernteerträge übersetzt werden. Die Ergebnisse bestätigen, dass Niederschlag und bodennahe Temperatur allgemein die landwirtschaftliche Produktion bestimmen und insbesondere die Schwankungen in den Ananas¬-erträgen primär thermisch bedingt scheinen. Weiterhin finden sich unter den simulierten künftigen Klimabedingungen projizierte Ertragsänderungen von bis zu -12,5% für Mais und Erdnuss und -11% , -29% und -33% für Ananas, Maniok und Augenbohne. Zugleich werden Ertragssteigerungen von +10% für Sorghumhirse und Yams, +24% für Baumwolle und +39% für Reis projiziert. Diese Änderungen sind abhängig von den Randbedingungen. Im Mittel betragen die simulierten Änderungen der Erträge während der Periode von 2001 bis 2050 zwischen -13% und -3% für SRES-B1 + LCC, -11% und -2% für SRES-A1B sowie -14% bis -3% für SRES-A1B + LCC für Ananas, Mais, Sorghumhirse, Erdnuss, Augenbohne und Maniok. Daneben finden sich für Yams, Baumwolle und Reis Zuwächse im Ernteertrag, die in Intervallen zwischen +2% bis +7% für SRES-B1 + LCC, +0.1% bis +12% für SRES-A1B und +3% bis +10% für SRES-A1B + LCC liegen. Obwohl die durchschnittliche Veränderung im Ertrag der Sorghumhirse negativ ist, lassen sich auch Tendenzen hin zu positiven Veränderungen feststellen. Die Ergebnisse zeigen zudem, dass die projizierte Zunahme der mittleren Lufttemperatur die simulierten Ernteerträge stärker beeinflusst als Veränderungen in den Niederschlagsmustern. Weiterhin scheint im Fall der Ananas der simulierte Rückgang im Ertrag nicht auf Veränderungen bei Landnutzung oder Landoberflächenbedeckung im subsaharischen Afrika zurückführbar. Die Erdnuss- und insbesondere Yams- und Baumwollerzeugung werden von den Veränderungen in der Landoberflächenbedeckung, die für die übrigen Feldfrüchte nachteilige Effekte bedeuten, profitieren. Zukünftig sollten politische Entscheidungsträger wirksame Maßnahmen einleiten, um die fortschreitende Landdegradation sowie alle anderen anthropogenen Prozesse, die zur globalen Erwärmung beitragen, einzuschränken. Biotechnologische Verbesserungen der verwendeten Nutzpflanzen, um an heißere und trockenere Bedingungen angepasste Varianten zu erzeugen, sollten in die bestehenden Aufzuchtprogramme integriert werden. Weiterhin sind unter anderem die Anwendung von geeigneten, klimaintelligenten landwirtschaftlichen Verfahren sowie eine nachhaltige Agrarwirtschaft notwendig, um die Schäden des Klimawandels auf die Landwirtschaft auszugleichen

Dearth of psychiatrists in Nigeria

No Abstract. Nigerian Medical Practitioner Vol. 47(6) 2005: 127-12

Predistortion as a cost-effective means to tackling nonlinearity in radio over fibre links

Radio over fibre technology in combination with the enormous bandwidth capabilities offered by an optical fibre was studied due to the advantage of a centralized signal-­‐processing architecture. Fibre-­‐based wireless access schemes employing sub-­‐carrier multiplexing radio over fibre links provide the unique capacity to support a number of services such as cellular network, common antennae television and wireless local area network applications simultaneously. The theory and design of a radio over fibre link was studied, with the objective of providing an economically viable high performance link to be used in a digital cellular system. The nonlinearity in the link system, especially in the optical source, revealed a limitation in the dynamic range performance when transmitting analog and multilevel digital signals over the fibre-­‐optic link channels. A digital baseband linearization technique using a radial basis function neural network that learns from the input-­‐output data samples of the link was presented for compensating the nonlinearity impairments. The bit error rate performance analysis of the link was performed using Monte-­‐Carlo and importance sampling techniques to reveal the overall system improvements with the addition of the neural network. The importance sampling technique realized the required bit error rate performance for a typical digital optical receiver in the radio over fibre link when transmitting digital signals. A neural-­‐fuzzy based predistortion controlled technique showed the ability to compensate for varying input signal amplitudes into the radio over fibre link, allowing for an overall robust and adaptable link system

Modélisation du bilan hydrologique du bassin versant du Klou : contribution à la gestion durable des ressources en eau dans le Zou

Thèse, Université d'Abomey-Calavi, 2007The table of contents for this item can be shared with the requester. The requester may then choose one chapter, up to 10% of the item, as per the Fair Dealing provision of the Canadian Copyright ActLa présente étude vise à contribuer à la gestion durable des ressources en eau dans le Zou, en adaptant un modèle hydrologique régional aux processus hydrologiques et au transport de sédiments dans le bassin versant du Klou, qui tient compte du climat, du couvert végétal et des pratiques socio-économiques. Pour ce faire, le modèle SWAT, un modèle hydrologique physique semidistribué à interface SIG a été sélectionné, calibré et validé pour le bassin versant étudié. A partir des données à savoir : les modèles numériques de terrain (MNT) pour la zone d’étude, les données climatiques, les cartes numériques des sols et les caractéristiques physiques des sols, les cartes numériques d’utilisation des terres, et les données sur les paramètres physiques des principales cultures de la zone d’étude, le modèle SWAT a partitionné le bassin versant en des sous-bassins et sous-unités homogènes appelées HRU (unité de réponse hydrologique). Le bilan hydrologique a ainsi été calculé pour chaque unité homogène (HRU) avant d’être agrégé à l’échelle du sous-bassin, puis du bassin. Ceci augmente la précision des résultats