Model and solutions to campus parking space allocation problem.

M. Sc. University of KwaZulu-Natal, Durban 2013.Parking is considered a major land use challenge in campus planning. The problem can be in terms of scarcity (few available spaces compared to demand) or management (ineffi cient usage of available facilities). Many studies have looked at the parking problem from the administrative and management points of view. However, it is believed that mathematical models and optimiza- tion can provide substantial solution to the parking problem. This study investigates a model for allocating car parking spaces in the university environment and improves on the constraints to address the reserved parking policy on campus. An investigation of both the exact and heuristic techniques was undergone to provide solutions to this model with a case study of the University of KwaZulu-Natal (UKZN), Westville Campus. The optimization model was tested with four different set of data that were generated to mimic real life situations of parking supply and demand on campus for reserved and unreserved parking spaces. These datasets consist of the number of parking lots and offi ce buildings in the case study. The study also investigate some optimization algorithms that can be used to obtain solutions to this problem. An exact solution of the model was generated with CPLEX solver (as incorporated in AIMMS software). Further investigation of the performance of the three meta-heuristics to solve this problem was done. A comparative study of the performance of these techniques was conducted. Results obtained from the meta-heuristic algorithms indicate that the algorithms used can successfully solve the parking allocation problem and can give solutions that are near optimal. The parking allocation and fitness value for each of the meta-heuristic algorithms on the sets of data used were obtained and compared to each other and also to the ones obtained from CPLEX solver. The results suggest that PSwarm performs better and faster than the other two algorithms and gives solutions that are close to the exact solutions obtained from CPLEX solver

COVID-19 Syndrome: Nexus with Herbivory and Exposure Dynamics for Monitoring Livestock Welfare and Agro-Environment

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a public health emergency that turns the year 2020–2021 into annus horribilis for millions of people across international boundaries. The interspecies transmission of this zoonotic virus and mutated variants are aided by exposure dynamics of infected aerosols, fomites and intermediate reservoirs. The spike in the first, second and third waves of coronavirus confirms that herd immunity is not yet reached and everyone including livestock is still vulnerable to the infection. Of serious concern are the communitarian nature of agrarians in the livestock sector, aerogenous spread of the virus and attendant cytocidal effect in permissive cells following activation of pathogen recognition receptors, replication cycles, virulent mutations, seasonal spike in infection rates, flurry of reinfections and excess mortalities that can affect animal welfare and food security. As the capacity to either resist or be susceptible to infection is influenced by numerous factors, identifying coronavirus-associated variants and correlating exposure dynamics with viral aerosols, spirometry indices, comorbidities, susceptible blood types, cellular miRNA binding sites and multisystem inflammatory syndrome remains a challenge where the lethal zoonotic infections are prevalent in the livestock industry, being the hub of dairy, fur, meat and egg production. This review provides insights into the complexity of the disease burden and recommends precision smart-farming models for upscaling biosecurity measures and adoption of digitalised technologies (robotic drones) powered by multiparametric sensors and radio modem systems for real-time tracking of infectious strains in the agro-environment and managing the transition into the new-normal realities in the livestock industry

On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets

Missing values or data is one popular characteristic of real-world datasets, especially healthcare data. This could be frustrating when using machine learning algorithms on such datasets, simply because most machine learning models perform poorly in the presence of missing values. The aim of this study is to compare the performance of seven imputation techniques, namely Mean imputation, Median Imputation, Last Observation carried Forward (LOCF) imputation, K-Nearest Neighbor (KNN) imputation, Interpolation imputation, Missforest imputation, and Multiple imputation by Chained Equations (MICE), on three healthcare datasets. Some percentage of missing values - 10\%, 15\%, 20\% and 25\% - were introduced into the dataset, and the imputation techniques were employed to impute these missing values. The comparison of their performance was evaluated by using root mean squared error (RMSE) and mean absolute error (MAE). The results show that Missforest imputation performs the best followed by MICE imputation. Additionally, we try to determine whether it is better to perform feature selection before imputation or vice versa by using the following metrics - the recall, precision, f1-score and accuracy. Due to the fact that there are few literature on this and some debate on the subject among researchers, we hope that the results from this experiment will encourage data scientists and researchers to perform imputation first before feature selection when dealing with data containing missing values

An Exact Solution for Allocating Car Parking Spaces on Campus

All over the world, especially in the university environment, planning managers and traffic engineers are constantly faced with the problem of inadequate allocation of car parking spaces to demanded users. Users could either prefer reserved parking spaces to unreserved parking spaces or vice versa. This makes the campus parking manager to be faced with two basic problem which are: the problem of allocating the actual number of available reserved spaces to users without any conflict over the same parking space, and the problem of determining the number of parking permit to be issued for parking lot with unreserved spaces. Hence, an optimal or available solution to the problem is required. This paper investigates a model for allocating car parking spaces, adds a constraint to address the reserved parking policy in a university environment and solves the parking allocation problem using an exact solution method. The result obtained gives the value of the objective function and the optimal allocation of users to each parking lot.Comment: An International Multidiscinary Conference on Research, Development and Practices in Science, Technology, Education, Arts, Management & the Social Science (iSTEAMS). Conference Centre, University of Ibandan, Nigeria. 30 May - 01 June 201