Data in support of high rate of pregnancy related deaths in Maiduguri,Borno State,Northeast Nigeria

Pregnancy relateddeaths(PRD)arepublichealthconcerninmost developing countriesandNigeriainparticular.Despitetheefforts put inbytheconcernedauthorities,PRDremainsanintegralpart of maternalmortalityormaternaldeathsinNigeriaingeneraland Borno stateinparticular,asevidencedfromtherecordsobtained from UmaruShehuHospital,Maiduguri(astatehospitalinthe state capital.ThedatacontainsfrequencyofPRDinmonthsand grouped intogynaecology,ante-natalandpost-natal,andlabour obtained frommid-2009tomid-2017.Thestatisticalanalysisof the datamayrevealtheextentofincidenceorepidemiologyof PRD isinthestat

Machine Learning Priority Rule (MLPR) For Solving Resource-Constrained Project Scheduling Problems

This paper introduces a machine learning priority rule for solving non-preemptive resource-constrained project scheduling problems (RCPSP). The objective is to find a schedule of the project’s tasks that minimizes the total completion time of the project satisfying the precedence and resource constraints. Priority rule based scheduling technique is a scheduling method for constructing feasible schedules of the jobs of projects. This approach is made up of two parts: a priority rule to determine the activity list and a schedule generation scheme which constructs the feasible schedule of the constructed activity list. Different scheduling methods use one of these schemes to construct schedules to obtain the overall project completion time. Quite a number of priority rules are available; selecting the best one for a particular input problem is extremely difficult. We present a machine learning priority rule which assembles a set of priority rules, and uses machine learning strategies to choose the one with the best performance at every point in time to construct an activity list of a project. The one with better performance is used most frequently. This removes the problem of manually searching for the best priority rule amongst the dozens of rules that are available. We used our approach to solve a fictitious project with 11 activities from Pm Knowledge Center. Four priority rules were combined. We used serial schedule generation scheme to generate our schedules. Our result showed that the total completion time of the project obtained with our approach competes favorably with the completion times gotten with the component priority rules. We then went further and compared our algorithm with 9 other available priority rules. Our results showed that the completion time got using our algorithm compete favorably with the total 13 priority rules available in the literature

Machine Learning Heuristic for Solving Multi-Mode Resource-Constrained Project Scheduling Problems

The non-preemptive resource-constrained project scheduling problem is considered in this work. It is assumed that each activity has many ways of execution and the objective is to find a schedule that minimizes the project’s completion time (multi-mode RCPSP). Methods that are based on priority rules do not always give the needed very good results when used to solve multi-mode RCPSP. In solving large real-life problems quickly though, these methods are absolutely necessary. Hence good methods based on priority rules to get the primary results for metaheuristic algorithms are needed. This work presents a novel method based on priority rules to calculate the primary solutions for metaheuristic algorithms. It is a machine learning approach. This algorithm first of all uses Preprocessing to reduce the project data in order to speed up the process. It then employs a mode assignment procedure to obtain the mode of each job. After which the algorithm uses machine learning priority rule to get the precedence feasible activity list of the project’s tasks. Finally, it then uses the Serial Schedule Generation Scheme to get the total completion time of the project. In our experiments, we use our algorithm to solve some problems in the literature that was solved with metaheuristic procedures. We compared our results with the initial solutions the authors started with, and our results competes favorably with the initial solutions, making our algorithm a good entry point for metaheuristic procedures

Minimization of Failed Roads - A Hybrid Resource-Constrained Project Scheduling Problem

Causes of failed roads and the reasons why most roads stay consistently failed in some nations of the world, like Nigeria, may be attributed to many factors, salient among them may be corruption and recession ultimately. Corruption in the award of road construction contracts make roads not to be properly done, to meet set standards thereby failing almost immediately they are completed. So, if corruption is minimized in awarding road construction contracts, the number of failed roads maybe minimized. This paper introduces some solution methods to minimize corruption in road construction projects so that good and sustainable roads are constructed even if there is also recession. In our experiment, we formulated the construction of real life 5km asphalt road as a hybrid resource constrained project scheduling problem (HRCPSP). Using priority based project scheduling technique, our results show the number of skilled workers needed in each period which gives the idea of the amount of fund needed in each of the periods. We constructed two Gantt diagrams: when resources are unconstrained and when resources are constrained to the minimal demand of jobs in the eligible set in each period. The unconstrained Gantt diagram helps to know the maximum amount of fund that should be released to the engineers in each period. This helps to curb corruption. The constrained Gantt diagram helps to know the minimum amount that should be released to the engineers for work to go on and the project to get to completion stage even there is recession. This helps project to be completed even if there is recession

Solving Project Delays and Abandonment Using Hybrid Resource-Constrained Project Scheduling Models

Resource-constrained project scheduling models either the single-mode or the multi-mode case finds minimum schedule that minimizes the completion time of a project with constant per period renewable resource. That the level of provided resources, each period must be constant, does not reflect a real-life situation and hence makes these models inappropriate for solving project delays and abandonment. We present a Hybrid resource-constrained project scheduling problem (Hybrid RCPSP), the single-mode case and the multi-mode case for solving delays and abandonment of projects. These models are combination of the existing single-mode and multi-mode RCPSP models with some added assumptions. Our method essentially formulates the network project as a Hybrid RCPSP (single-mode or the multi-mode) and then finds the minimal schedule that minimizes the completion time of the project using priority rule based scheduling technique, while the level of the renewable resource availability varies. The idea is that if a completion time of the project can be minimized then, that project cannot be delayed or abandoned. We performed our method on a real-life building construction project (a fenced three-bedroom bungalow), a fictitious single-mode and multi-mode network projects. Our result of the real-life building construction project, show that to solve project delays and abandonment, the level of per period available resource should vary and our result of the fictitious Single-Mode and Multi-mode RCPSP show that no matter how small (even at zero level in some time periods), the per period amount and how long the length of the period, the projects will not be delayed or even abandone

On the Epidemiology and Statistical Analysis of HIV/AIDS Patients in the Insurgency Affected States of Nigeria

BACKGROUND: The effect of insurgencies on a nation regarding the economy, education, health and infrastructure cannot be overemphasised. AIM: This research is therefore focused on analysing the incidence of HIV/AIDS disease in states affected by the activities of the Boko Haram insurgency in Nigeria. MATERIAL AND METHODS: The data collected refer to the period from 2004 to 2017, reporting information on 16,102 patients and including the age, gender, year of diagnosing and status of the patients. Descriptive, Chi-square test of independence and Correlation analyses were performed using Statistical Package for Social Sciences (SPSS) version 20. RESULTS: It was discovered that the majority of those living with HIV/AIDS in these Boko Haram ravaged areas are females between the age group of 30 years to 39 years. Reported cases of HIV/AIDS started increasing significantly from age 20, and the highest number of reported cases of HIV/AIDS was recorded in the year 2017. CONCLUSION: The status of the patient was found to be dependent on both the gender and age of the patients’ treatment, though the strength of the linear relationship between status and age is not significantly different from zero

Classes of Ordinary Differential Equations Obtained for the Probability Functions of Linear Failure Rate and Generalized Linear Failure Rate Distributions

The linear failure rate (hazard) and generalized linear failure rate (hazard) distributions are uniquely identified by their linear hazard functions. In this paper, homogenous ordinary differential equations (ODES) of different orders were obtained for the probability functions of linear failure rate and generalized linear failure rate distributions. This is possible since the aforementioned probability functions of the distributions are differentiable and the former distribution is a particular case of the later. Differentiation and modified product rule were used to derive the required ODEs, whose solutions are the respective probability functions. The different conditions necessary for the existence of the ODEs were obtained and it is in consistent with the support that defined the various probability functions considered. The parameters that defined each distribution greatly affect the nature of the ODEs obtained. This method provides new ways of classifying and approximating other probability distributions apart from one considered in this research. Algorithms for implementation can be helpful in improving the results

Shortest Path Planning Algorithm – A Particle Swarm Optimization (PSO) Approach

Path planning for a mobile robot is a difficult task and has been widely studied in robotics. The objective of recent researches is not just to find feasible paths but to find paths that are optimal with respect to distance covered and safety of the robot. Techniques based on optimization have been proposed to solve this problem but some of them used techniques that may converge to local minimum. In this paper, we present a global path planning algorithm for a mobile robot in a known environment with static obstacles. This algorithm finds the optimal path with respect to distance covered. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. Our customized algorithm generates the coordinates of the search space and passes the result to the PSO algorithm which then uses the coordinate values to determine the optimal path from start to finish. We perform our experiments using four different environments with population size 100 each in a 10 x 10 grid terrain and our results are favorable

A Fast Path Planning Algorithm for a Mobile Robot

The path planning problem finds a collision free path for an object from its start position to its goal position while avoiding obstacles and self-collisions. Many methods have been proposed to solve this problem but they are not optimization based. Most of the existing methods find feasible paths but the objective of this current research is to find optimal paths in respect of time, distance covered and safety of the robot. This paper introduces a novel optimization-based method that finds the shortest distance in the shortest time. It uses particle swarm optimization (PSO) algorithm as the base optimization algorithm and a customized algorithm which generates the coordinates of the search space. We experimentally show that the distance covered and the generated points are not affected by the sample size of generated points, hence, we can use a small sample size with minimum time and get optimal results, emphasizing the fact that with little time, optimal paths can be generated in any known environment

Fast and Optimal Path Planning Algorithm (FAOPPA) for a Mobile Robot

Motion planning problem though widely studied in robotics is a difficult problem. It finds a feasible path from an initial position to a final position in an environment with obstacles. Recent researches do not just aim to find feasible paths but to find paths that are optimal in respect to time, distance and safety of the robots. Optimization based techniques have been proposed to solve this problem but some of them used techniques that may converge to local minimum and they seldom consider the speed of the technique. Hence this paper presents a fast and global motion planning algorithm for a mobile robot in a known environment with static obstacles. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. The coordinate values when generated by the customized algorithm are passed to the PSO algorithm which uses them to determine the shortest path between the two given end positions. We perform our experiments using four different environments with population sizes 100, 50, 20 and 10 in a 10 × 10 grid and our results are favorable