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

Determination of Optimal Number of Servers at Network Queuing Nodes to Reduce Waiting Time in a Tertiary Institution Clinic in Bida, Nigeria

In this paper, a network queuing model that determines optimal numbers of servers at the nodes of the school clinic network queuing system to  reduce waiting time of the patients has been presented. The relevant data was collected for a period four weeks, through direct observations and interviews. The number of arrivals and departures were also obtained. The total expected waiting time of the patient in the current system before modification was 50minutes with total number of 10 servers in all the nodes, while the total new expected waiting time of patient in the system after modification was reduced to 19 minutes with total number of 17 servers in all the nodes. The study has determined optimal number of servers at the nodes of the school clinic network system. Results from this study is an important information to the management of the school clinic for proper planning and better service delivery. Keywords: Network Queuing System, Nodes, Servers, School Clinic

Effects of nitrogen levels and harvest interval on the growth and yield of Moringa (Moringa Oleifera Lam) in Sudan Savanna of Nigeria

Field experiment was conducted under irrigation at Bayero University Kano, during 2011/2012 dry season to investigate the effects of different nitrogen levels and harvest interval on growth and leaf yield of Moringa  (Moringa oleifera (Lam)). The treatments consisted of four levels of nitrogen (0, 50, 100 and 150kg N ha-1) and three times of harvest interval (2, 3, and 4 weeks). These were arranged in a split plot design with the nitrogen levels allocated to the main plots while the harvest interval allocated to the sub plots and replicated four times. The data collected were subjected to analysis of variance (ANOVA) using the general linear model of GenStat and significant different means were separated using DMRT. The result shows that applications of nitrogen significantly increased plant height, number of leaflets per plant, plant stem diameter, number of  branches per plant, fresh and dry weights per plant. Generally, 150kg N ha-1 level gave highest values for all  the growth and yield characters assessed. The highest fresh and dry total leaf yields were also obtained with  150kg N ha-1 level. Increase in harvest interval significantly increased fresh and dry leaf yields of Moringa with  the highest yields obtained from 4 weeks harvest interval. Nitrogen and harvest interval interaction was found  to be significant on fresh and dry leaf yields of Moringa, which indicated that high yields were supported by  150kg N ha-1 at 4 weeks harvest interval.Keywords: Moringa, harvest interval, Nitrogen level, yiel

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

Effects of nitrogen levels and harvest ferquency on the growth and leaf quality of moringa (Moringa oleifera Lam) in Sudan Savanna of nigeria

Field experiment was conducted under irrigation at teaching and research farm of Faculty of Agriculture, Bayero University Kano, during 2011/2012 dry season to investigate the effects of different nitrogen levels and harvest frequency on growth and leaf quality of Moringa (Moringa oleifera (Lam)). The treatments consisted of four levels of nitrogen (0, 50, 100 and 150 kg N ha-1) and three times of harvest frequency (2, 3, and 4 weeks). These were arranged in a split plot design with the nitrogen levels allocated to the main plots while the harvest frequency allocated to the sub plots and replicated four times. The data collected were subjected to analysis of variance (ANOVA) using GenStat and significant different means were separated using DMRT. The result shows that application of nitrogen significantly (P<0.05) increase plant height, number of leaflets plant-1, plant stem diameter, number of branches plant-1, fresh and dry weights plant-1. Generally, the growth characters assessed gave maximum value with 150 kg N ha-1 treatment. At first harvest highest protein content of the leaf was also obtained with the higher Nitrogen level (150 kg N ha-1), highest Magnesium was obtained with 100 kg N ha-1 while highest quantity of phosphorus and potassium were obtained with 50 kg N ha-1. The increase in harvest frequency significantly (P<0.05) increased dry leaf quality of Moringa with the highest protein quality obtained from 4 weeks harvest frequency. Nitrogen and harvest frequency interaction was found to be significant (P<0.05) on dry leaf quality of Moringa.Keywords: Moringa, nitrogen, harvest frequency and leaf qualit

Production and characterization of activated carbon from leather waste, sawdust, and lignite

Powdered activated carbon (PAC) was prepared from leather buffing waste, sawdust and lignite by carbonization at temperatures between 500 – 800oC followed by steam activation. Experimental results reveal a general decrease in yield of carbon residue with increase in temperature of carbonization. Samples of lignite recorded the highest yield (49.80 – 67.70%) followed by leather buffing dust (30.70 – 39.70%) and sawdust (25.10 – 37.20%). Activated carbon from these precursors, were also evaluated for percentage ash, fixed carbon, pH and bulk density. Adsorption studies carried out with methylene blue indicate that low temperature carbonization of precursors such as leather buffing waste favour production of carbon with better adsorption efficiency while high temperature carbonization produced carbon with better efficiency from sawdust and lignite. Activated carbon from sawdust and leather buffing waste show result which compare favourably with the reference carbon used. These carbons are recommended for use in the adsorption of dyes or decolourization of organic compounds and other substances in aqueous solutions.Key words: Activated carbon, carbonization, Steam activation, Adsorption efficiency, Leather buffing wast

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

Awareness, Satisfaction and Willingness to Pay Remuneration for Architectural Services among Clients in Nigeria

This study assessed relationships between awareness, satisfaction, and willing(ness)-to-pay (WTP) remuneration for architectural services from client perspectives toward improving the public image and business performance of architects in Nigeria. Likert ratings of 16 officially approved architectural services based on residential developments from 97 respondents using descriptive statistics, t-tests, and regression analysis revealed that clients obtained architectural information through word-of-mouth referrals from friends, colleagues, architects and finally, through digital media. Respondents were significantly more aware (mean 2.75 on a 4-point Likert scale) than were WTP for architectural services (mean 2.12), P = 0.000. Clients were WTP for only production of construction drawings and site supervision. Awareness significantly predicted WTP (β = −0.7, Exp (β) = 4.106, P = 0.003) in a model including age and income which explained 36% of the variance in WTP. Satisfaction with architectural services negatively predicted WTP (β = −0.77, Exp(β)=0.462, P = 0.16), implying that client satisfaction, a key performance indicator for architects, was no guarantee for WTP. Revisions to professional fees and code of ethics are recommended to allow architects and allied professionals advertise and market their services through online and social media outlets. Architects should also leverage on interior, furniture, fittings, and component design to improve remuneration and business performance

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

Review of urological cancers in Damaturu, Nigeria

 Background: Urological cancers (UC) remain a major global burden and a significant cause of high morbidity and mortality. In other to plan and tackle this burden, there is need to audit the pattern of these malignancies in our locality which is currently lacking.  Hence, the objectives of our study were to describe the histological pattern, frequency and demographic characteristics of urological cancers seen in Damaturu.Methods: The reports of all the urological specimens that were histologically diagnosed as malignant between November 2017 to October 2019 in the histopathology department of Yobe state university teaching hospital, were analyzed. The department keeps newly established cancer registry for the state.Results:  Fifty-five UC were diagnosed, with male to female ratio of 13.8:1. These UC constitute 34.1% of the 161 urological specimens assessed and 26.4% of all types of medical cancers diagnosed in the period of study. Prostate cancer dominates the UC (41; 73.8%), followed by bladder cancers (11; 19.8%) and the remaining kidney, penis and testicular cancers (1; 1.8%) each.Conclusions: Urological cancers are very common in our region, particularly prostate cancers in which majority were poorly differentiated. This report though analyzed few cancers, the proportion of UC is high. Urothelial cancer of the bladder is now more frequent than squamous cell carcinoma. This study can serve as basis for future epidemiologic studies targeting at the risk factors, awareness and prevention of UC