Etiology and electroclinical pattern of late onset epilepsy in Ibadan, Southwestern Nigeria

BackgroundLate onset epilepsy (LOE) is a common neurological problem throughout the world. It is an area that has not been fully explored in the developing countries like Nigeria. The aim of the present study is to  determine the pattern of presentation of late onset epilepsy with the view to identifying the etiologic as well as describe their electro-clinical pattern.Methods120 consecutive patients presenting at the University College Hospital with LOE were recruited. A  detailed history was obtained in every case, and complete neurological examination was performed. EEGs were done in all patients. Contrast CT Scans and MRI were performed.ResultOne hundred and twenty subjects comprising 71 (59.2%) males and 49 (40.8%) females were studied. The ages of the patients ranged between 25 and 85 years with a mean of 53years (sd =14.6). The ages at onset of epilepsy ranged between 25 and 84 years with a mean of 52 (sd=14.8). All the subjects had classifiable seizure types, 31 (25.8%) had generalized seizure. The most common type of seizure was partial seizure diagnosed in 89 (74.2%) subjects. Fifty two (43.3%) of the subjects had abnormal neurological findings. Twenty one (30.9%) had cerebral infarcts and 20 (29.4%) had cerebral tumor. Those with symptomatic epilepsy were more likely to have neurologic deficit, simple partial seizure, secondarily generalized seizure, focal epileptiform discharges and focal slow waves.ConclusionThe most common abnormalities in LOE were cerebral infarct and brain tumor. A careful history, neurological examination and an EEG are adequate in the initial work-up of patients with LOE

Robotics and automated systems in construction: Understanding industry-specific challenges for adoption

© 2019 The Authors The construction industry is a major economic sector, but it is plagued with inefficiencies and low productivity. Robotics and automated systems have the potential to address these shortcomings; however, the level of adoption in the construction industry is very low. This paper presents an investigation into the industry-specific factors that limit the adoption in the construction industry. A mixed research method was employed combining literature review, qualitative and quantitative data collection and analysis. Three focus groups with 28 experts and an online questionnaire were conducted. Principal component and correlation analyses were conducted to group the identified factors and find hidden correlations. The main identified challenges were grouped into four categories and ranked in order of importance: contractor-side economic factors, client-side economic factors, technical and work-culture factors, and weak business case factors. No strong correlation was found among factors. This study will help stakeholders to understand the main industry-specific factors limiting the adoption of robotics and automated systems in the construction industry. The presented findings will support stakeholders to devise mitigation strategies

Data-driven tools for building energy consumption prediction: A review

The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data-driven tool to develop a model using a small sample size, despite the recognition of the tool for producing good results in large data conditions. This study delivers a review of 63 studies with a precise focus on evaluating the performance of data-driven tools based on certain conditions; i.e., data properties, the type of energy considered, and the type of building explored. This review identifies gaps in research and proposes future directions in the field of data-driven building energy consumption prediction. Based on the studies reviewed, the outcome of the evaluation of the data-driven tools performance shows that Support Vector Machine (SVM) produced better performance than other data-driven tools in the majority of the review studies. SVM, Artificial Neural Network (ANN), and Random Forest (RF) produced better performances in more studies than statistical tools such as Linear Regression (LR) and Autoregressive Integrated Moving Average (ARIMA). However, it is deduced that none of the reviewed tools are predominantly better than the other tools in all conditions. It is clear that data-driven tools have their strengths and weaknesses, and tend to elicit distinctive results in different conditions. Hence, this study provides a proposed guideline for the selection tool based on strengths and weaknesses in different conditions

Stroke in patients with diabetes mellitus: a study from North Western Nigeria.

Background: Stroke is one of the major causes of morbidity and mortality worldwide and apart from being exceedingly harmful in diabetics, stroke is a disabling disorder. The study was undertaken to describe the clinical characteristics, outcome pattern and predictors of mortality in a cohort of diabetic patients presenting with stroke in two tertiary health facilities in North Western Nigeria. Method: Out of all stroke patients seen from June 2007 to February 2011, persons with diabetes mellitus presenting with stroke in the emergency unit of the two tertiary hospitals in Kano were consecutively recruited for the study. Classification of stroke into hemorrhagic and infarctive subtypes was based on brain computerized tomography (CT), brain magnetic resonance imaging (MRI) and World Health Organization (WHO) criteria. Follow-up period was for thirty days. Result: Out of the five hundred and thirty six stroke patients seen during the study period, 85 (15.9%) patients, comprising 48 (56.5%) males, had diabetes. Thirty eight (44.7%) of the identified diabetics were previously undiagnosed. Sixty four (75.3%) had infarctive stroke. One-month case fatality rate was 30.6%. Factors associated with death included male sex, past history of TIA, abnormal respiratory pattern, hemorrhagic stroke, aspiration pneumonitis, and worsening GCS. Aspiration pneumonitis and worsening GCS were independent predictors of one month mortality of stroke in the patients. Conclusion: In DM patients studied, infarctive stroke was more common, case fatality was 30.6%. Male gender, past history of TIA, abnormal respiratory pattern, hemorrhagic stroke, aspiration pneumonitis, and worsening Glasgow Coma Score (GCS) were associated with mortality. Aspiration pneumonitis and worsening GCS were independent predictors of one month mortality of stroke in diabetic patients

Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy

© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire lifecycle. In this study therefore, a disassembly and deconstruction analytics system is developed to provide buildings’ end-of-life performance assessment from the design stage. The system architecture builds on the existing building information modelling capabilities in managing building design and construction process. The architecture is made up of four different layers namely (i) Data storage layer, (ii) Semantic layer, (iii) Analytics and functional models layer and (iv) Application layer. The four layers are logically connected to function as a single system. Three key functionalities of the disassembly and deconstruction analytics system namely (i) Building Whole Life Performance Analytics (ii) Building Element Deconstruction Analytics and (iii) Design for Deconstruction Advisor are implemented as plug-in in Revit 2017. Three scenarios of a case study building design were used to test and evaluate the performance of the system. The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy. The disassembly and deconstruction analytics system would ensure that buildings are designed with design for disassembly and deconstruction principles that guarantee efficient materials recovery in mind. The disassembly and deconstruction analytics tool could also serve as a decision support platform that government and planners can use to evaluate the level of compliance of building designs to circular economy and sustainability requirements

Investigating profitability performance of construction projects using big data: A project analytics approach

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implications for the efficient planning and delivery of construction projects. Setting up right profit margins and its continuous tracking as projects progress are vital management tasks that require data-driven decision support. Existing profit estimation measures use a company or industry wide benchmarks to guide these decisions. These benchmarks are oftentimes unreliable as they do not factor in project-specific variations. As a result, projects are wrongly estimated using uniform rates that eventually end up with entirely unusual margins either due to underspends or overruns. This study proposed a project analytics approach where Big Data is harnessed to understand the profitability distribution of different types of construction projects. To this end, Big Data architecture is recommended, and a prototype implementation is shown to store and analyse large amounts of projects data. Our data analysis revealed that profit margins evolve, and the profitability performance varies across several project attributes. These insights shall be incorporated as knowledge to machine learning algorithms to predict project margins accurately. The proposed approach enabled the fast exploration of data to understand the underlying pattern in the profitability performance for different types of construction projects

Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to eliminate redundant features optimally is challenging, especially if the parameters of these algorithms are not tuned. In this study, a particle swarm optimization is proposed both for feature selection and parameters tuning of the gradient boosting machine technique on 1,349,239 data points of an incident dataset. The predictive ability of the proposed method compared to conventional tree-based methods revealed near-perfect predictions of the proposed model on test data (classification accuracy − 0.878 and coefficient of determination − 0.93) for the two outcome variables ACCIDENT and INJURYFREQ. The high predictive power obtained reveals that injuries do not occur in a chaotic fashion, but that underlying patterns and trends exist that can be uncovered and captured via machine learning when applied to sufficiently large datasets. Also, key relationships identified will assist safety managers to understand possible risk combinations that cause accidents; helping to trigger proactive risk mitigation plans

Interleukin-6 (\u3cem\u3eIL-6\u3c/em\u3e) rs1800796 and Cyclin Dependent Kinase Inhibitor (\u3cem\u3eCDKN2A/CDKN2B\u3c/em\u3e) rs2383207 Are Associated with Ischemic Stroke in Indigenous West African Men

Background—Inherited genetic variations offer a possible explanation for the observed peculiarities of stroke in sub – Saharan African populations. Interleukin–6 polymorphisms have been previously associated with ischemic stroke in some non-African populations. Aim—Herein we investigated, for the first time, the association of genetic polymorphisms of IL-6 and CDKN2A- CDKN2B and other genes with ischemic stroke among indigenous West African participants in the Stroke Investigative Research and Education Network (SIREN) Study. Methods—Twenty-three previously identified single nucleotide polymorphisms (SNPs) in 14 genes of relevance to the neurobiology of ischemic stroke were investigated. Logistic regression models adjusting for known cardiovascular disease risk factors were constructed to assess the associations of the 24 SNPs in rigorously phenotyped cases (N=429) of ischemic stroke (Men = 198; Women = 231) and stroke– free (N=483) controls (Men = 236; Women = 247). Results—Interleukin-6 (IL6) rs1800796 (C minor allele; frequency: West Africans = 8.6%) was significantly associated with ischemic stroke in men (OR = 2.006, 95% CI = [1.065, 3.777], p = 0.031) with hypertension in the model but not in women. In addition, rs2383207 in CDKN2A/CDKN2B (minor allele A with frequency: West Africans = 1.7%) was also associated with ischemic stroke in men (OR = 2.550, 95% CI = [1.027, 6.331], p = 0.044) with primary covariates in the model, but not in women. Polymorphisms in other genes did not show significant association with ischemic stroke. Conclusion—Polymorphisms rs1800796 in IL6 gene and rs2383207 in CDKN2A/CDKN2B gene have significant associations with ischemic stroke in indigenous West African men. CDKN2A/CDKN2B SNP rs2383207 is independently associated with ischemic stroke in indigenous West African men. Further research should focus on the contributions of inflammatory genes and other genetic polymorphisms, as well as the influence of sex on the neurobiology of stroke in people of African ancestry

Cloud computing in construction industry: Use cases, benefits and challenges

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive and operational advantage, the diffusion of the technologies in the industry follows a steep curve. This study therefore highlights the current contributions and use cases of cloud computing technologies in construction practices. As such, a systematic review was carried out using ninety-two (92) peer-reviewed publications, published within a ten-year period of 2009-2019. A key highlight of the research findings is that cloud computing is an innovation delivery enabler for other emerging technologies (building information modelling, internet of things, virtual reality, augmented reality, big data analytics, mobile computing) in the construction industry. As such, this paper brings to the fore, current and future application areas of cloud computing vis-à-vis other emerging technologies in the construction industry. The paper also identifies barriers to the broader adoption of cloud computing in the construction industry and discusses strategies for overcoming these barriers

Exploring Overlaps Between the Genomic and Environmental Determinants of LVH and Stroke: A Multicenter Study in West Africa

Background Whether left ventricular hypertrophy (LVH) is determined by similar genomic and environmental risk factors with stroke, or is simply an intermediate stroke marker, is unknown. Objectives We present a research plan and preliminary findings to explore the overlap in the genomic and environmental determinants of LVH and stroke among Africans participating in the SIREN (Stroke Investigative Research and Education Network) study. Methods SIREN is a transnational, multicenter study involving acute stroke patients and age-, ethnicity-, and sex-matched control subjects recruited from 9 sites in Ghana and Nigeria. Genomic and environmental risk factors and other relevant phenotypes for stroke and LVH are being collected and compared using standard techniques. Results This preliminary analysis included only 725 stroke patients (mean age 59.1 ± 13.2 years; 54.3% male). Fifty-five percent of the stroke subjects had LVH with greater proportion among women (51.6% vs. 48.4%; p \u3c 0.001). Those with LVH were younger (57.9 ± 12.8 vs. 60.6 ± 13.4; p = 0.006) and had higher mean systolic and diastolic blood pressure (167.1/99.5 mm Hg vs 151.7/90.6 mm Hg; p \u3c 0.001). Uncontrolled blood pressure at presentation was prevalent in subjects with LVH (76.2% vs. 57.7%; p \u3c 0.001). Significant independent predictors of LVH were age \u3c45 years (adjusted odds ratio [AOR]: 1.91; 95% confidence interval [CI]: 1.14 to 3.19), female sex (AOR: 2.01; 95% CI: 1.44 to 2.81), and diastolic blood pressure \u3e 90 mm Hg (AOR: 2.10; 95% CI: 1.39 to 3.19; p \u3c 0.001). Conclusions The prevalence of LVH was high among stroke patients especially the younger ones, suggesting a genetic component to LVH. Hypertension was a major modifiable risk factor for stroke as well as LVH. It is envisaged that the SIREN project will elucidate polygenic overlap (if present) between LVH and stroke among Africans, thereby defining the role of LVH as a putative intermediate cardiovascular phenotype and therapeutic target to inform interventions to reduce stroke risk in populations of African ancestry