Faculty of Engineeringhttps://hdl.handle.net/20.500.12504/32024-03-29T01:32:16Z2024-03-29T01:32:16ZInvestment decision modeling for transboundary project portfolio selectionKizito, Paul MubiruChristopher, SenfukaMaureen, Ssempijjahttps://hdl.handle.net/20.500.12504/16362024-03-23T00:20:42Z2021-09-01T00:00:00ZInvestment decision modeling for transboundary project portfolio selection
Kizito, Paul Mubiru; Christopher, Senfuka; Maureen, Ssempijja
Joint activities for global water partnerships in regional development programs are usually facilitated by implementing
transboundary water projects. Most projects are however hampered by the absence of a clear economic base for making
investment decisions. In this paper, we propose a zero-one integer programming model to determine the optimal decisions for
selection of project portfolios on transboundary waters; where project selection is based on several time periods in the future.
The objective is to determine whether to undertake a project or not; so that the net present value of investment returns is
maximized to support needy communities. A numerical example is presented for illustration; demonstrating the optimal choice
of investment projects under budget constraints. The zero-one integer programming model provides a feasible solution for choice
of transboundary project investment decisions; given the competing nature of capital budgets prior project implementation. The
proposed model can be efficient; where limited funds among competing projects serve as a basis for project selection criteria; a
decision for facilitation enhancement towards water partnership for regional development.
2021-09-01T00:00:00ZModelling and optimization of residential electricity load under stochastic demandKizito, Paul MubiruSsempijja, Maureen Nalubowahttps://hdl.handle.net/20.500.12504/16352024-03-22T00:20:40Z2024-01-01T00:00:00ZModelling and optimization of residential electricity load under stochastic demand
Kizito, Paul Mubiru; Ssempijja, Maureen Nalubowa
The paper considers a modelling framework for a set of households in residential areas using electricity as a form of
energy for domestic consumption. Considering the demand and availability of units for electricity consumption,
optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this
problem as a stochastic decision-making process model where electricity demand is characterized by Markovian
demand. The demand and supply phenomena govern the loading and operational framework, where shortage costs
are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty
households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly
intervals over a period of four months. The major problem focussed on determining an optimal electricity loading
decision to minimize consumption costs as demand changes from one state to another. Considering a multi-period
planning horizon, an optimal decision was determined for loading or not loading additional electricity units using the
Markov decision process approach. The model was tested, and the results demonstrated the existence of optimal
state-dependent decision and consumption costs considering the case study used in this study. The proposed model
can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for
electricity distribution systems to residential areas were realized, with subsequently enhanced service reliability to
essential energy market customers.
2024-01-01T00:00:00ZCOVIDiStress diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemicangélique, M. BlackburnSara, VestergrenThe COVIDiStRESS II Consortiumhttps://hdl.handle.net/20.500.12504/16182024-02-24T00:10:55Z2022-01-01T00:00:00ZCOVIDiStress diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic
angélique, M. Blackburn; Sara, Vestergren; The COVIDiStRESS II Consortium
During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access
global survey to understand and improve individuals’ experiences related to the crisis. a year later,
we extended this line of research by launching a new survey to address the dynamic landscape of the
pandemic. this survey was released with the goal of addressing diversity, equity, and inclusion by
working with over 150 researchers across the globe who collected data in 48 languages and dialects
across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000
responses. the dataset allows cross-cultural study of psychological wellbeing and behaviours a year
into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and
scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-
access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiStRESS
dataset has facilitated government policy decisions regarding health crises, this dataset can be used by
researchers and policy makers to inform research, decisions, and policy.
2022-01-01T00:00:00ZField-based methods for measuring greenhouse gases emissions from on-site sanitation systems: A systematic review of published literaturePrativa, PoudelAnish, GhimireGuy, HowardBarbara, EvansMiller, Camargo-Valero A.Freya, MillsOlivia, ReddySubodh, SharmaSarana, TuladharAbraham, GeremewKenan, OkurutBaba, NgomManish, BaidyaSheila, Dangolhttps://hdl.handle.net/20.500.12504/14702023-10-25T07:16:36Z2023-04-01T00:00:00ZField-based methods for measuring greenhouse gases emissions from on-site sanitation systems: A systematic review of published literature
Prativa, Poudel; Anish, Ghimire; Guy, Howard; Barbara, Evans; Miller, Camargo-Valero A.; Freya, Mills; Olivia, Reddy; Subodh, Sharma; Sarana, Tuladhar; Abraham, Geremew; Kenan, Okurut; Baba, Ngom; Manish, Baidya; Sheila, Dangol
On-site sanitation systems (OSS) are a source of greenhouse gas (GHG) emissions. Although ef-
forts have been made recently to measure and quantify emissions from septic tanks using various
field-based methods, the vast majority of published literature reporting GHG emissions from OSS
units (e.g., pits and tanks) is based on non-empirical evidence. This systematic review presents an
overview and limitations of field-based methods used for the quantification of GHG emissions
from OSS. Papers published in English were searched in three databases: Google Scholar,
PubMed, and Directory of Articles and Journals. Peer-reviewed papers that reported field-based
methods applied to containment units in OSS were included in this study. Only eight out of
2085 papers met the inclusion criteria with septic tanks as the sole technology reported and were
thus, considered for the review. Most of the studies have been conducted in middle- and high-
income countries. Field-based measurements of GHGs are conducted using a flux chamber (FC)
and the most commonly used FC methods are (a) the modified simple static FC, (b) automated
static FC, and (c) floating FC. Data reported in published studies do not provide sufficient in-
formation on the calibration and validation of the results from the FCs used. The complex FC
designs, laborious fieldwork operations, and reliance on expensive, specialist equipment, suggest
that such methods may not be suitable in Low and Middle-Income countries (LMICs), where re-
sources and access to laboratory facilities are limited. Also, the complexity of pits and tank ty-
pology in LMICs (i.e., unstandardised designs and sizes) may be a challenge to the use of FCs with
fixed dimensions and set operational conditions. The variation in the quantification methods and resulting emission rates among the studies indicates that gaps prevail in the use of existing
methods. Therefore, there is still a need for a simple field-based, easily adaptable FC method with
adequate calibration and validation that can help in reliably quantifying the emissions from
different OSS in any LMICs.
2023-04-01T00:00:00Z