Project Resources Research Articles

Methodological approaches in prioritizing municipal projects: A case study of the municipality of the holy capital (Makkah)

This study aims to develop a robust prioritization model for municipal projects in the Holy Metropolitan Municipality (Makkah) to address the challenges of aligning short-term and long-term objectives. The research explores How multi-criteria decision-making (MCDM) techniques can prioritize municipal projects effectively while ensuring alignment with strategic goals and local needs. The methodology employs the analytic hierarchy process (AHP) and exploratory factor analysis (EFA) to ensure methodological rigor and data adequacy. Data were collected from key stakeholders, including municipal planners and community representatives, to enhance transparency and reliability. The model’s validity was assessed through latent factor analysis, confirming the relevance of identified criteria and factors. Results indicate that flood prevention projects are the highest priority (0.4246), followed by road projects (0.3532), park construction (0.1026), utility projects (0.0776), and digital transformation (0.0416). The study highlights that certain factors are critical for evaluating and prioritizing municipal projects. “Capacity and Demand” emerged as the most influential factor (0.5643), followed by “Strategic Alignment” (0.2013), “Project Interdependence” (0.1088), “Increasing Investment” (0.0950), and “Risk” (0.0306). These findings are significant as they offer a structured, data-driven approach to decision-making aligned with Saudi Vision 2030. The proposed model optimizes resource allocation and project selection, representing a pioneering effort to develop the first prioritization framework specifically tailored to Makkah’s unique municipal needs. Notably, this is the first study to establish a prioritization method specifically for Makkah’s municipal projects, providing valuable contributions to the field.

Identifying parameters impacting WASH STEM education across the globe

Abstract This study seeks to assess the parameters impacting successful water, sanitation and hygiene (WASH) science, technology, engineering, and math (STEM) educational outreach. Previous studies have shown a correlation between underprivileged communities and significant impacts due to water related disasters, such as flooding or water related illnesses. As a result, for several decades the international development community has invested in WASH-related infrastructure. Lessons learned through these investments has shown the need for WASH educational outreach to build community awareness and encourage behavioral change, ultimately ensuring the sustainability and impact of the WASH infrastructure. Many WASH infrastructure programs have incorporated WASH education programs, particularly those led by the community. Often this occurs through primary and secondary schools. While WASH STEM education promotes local project ownership, leading to an increased understanding of water management and infrastructure resiliency, these educational programs are not always successful. There is a knowledge gap in understanding what makes WASH STEM education and outreach effective. This research project investigated some of the parameters influencing students’ performance on an assessment of WASH STEM curricula. This was done through deploying two water resource learning modules and assessments to secondary school students in Cambodia, India, Nicaragua, and the United States. The results revealed a correlation between how well the students comprehended the information according to the assessment and how relevant the students found the material to their own lives. The insight gained from this study can create more impactful WASH STEM education and outreach programs for water resource projects, and beyond.

Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction

Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.

Geological Survey of Brazil Spectral Library v. 1.0: Rock reflectance signatures in the visible to shortwave infrared range

Reflectance spectral libraries are essential reference databases for the mineralogical characterization of geological samples analyzed using visible and infrared spectroscopy. Since 2011, the Geological Survey of Brazil has been actively developing a comprehensive database of reflectance spectral signatures, based on samples collected from systematic geological mapping and mineral resource projects across the Brazilian territory. This spectral library is now available on a digital platform hosted by the Geological Survey of Brazil's geoscientific database, providing quantitative data for the numerical characterization of lithological types and their alteration products, whether due to weathering or hydrothermal processes.

An evaluation theory for participatory design: Design Empowerment Framework (DEF)

ABSTRACT Participatory Design (PD) emerged alongside the push for democracy during the technological advancements of the 1960s. Despite its frequent application, the literature lacks sufficient studies addressing the ‘what’, ‘how much’, and ‘how’ PD contributes in terms of both processes and outcomes – leaving a gap in supporting the arguments for its use. This study seeks to address this gap by explicitly exploring PD as an intervention leading to participant empowerment. It extends its scope by examining how empowerment occurs in existing PD projects through the lens of Theory-Based Evaluation. The proposed framework guides researchers to design, manage, and evaluate PD projects, offering valuable feedback on how the process functions for stakeholders providing project resources. It raises awareness among participants about their role, how they can contribute, and the benefits they can derive from their involvement.

Generating Rainfall IDF Curves Using IMD Reduction Formula and Choosing the Best Distribution for Babylon City, Iraq

ABSTRACTIntensity‐duration‐frequency (IDF) models are considered one of the most important tools used in water resources projects, as well as the design and planning of hydraulic structures such as sewerage channels, bridges, culverts, and road networks. This study aims to generate IDF curves for the Iraqi city of Babylon based on the Indian Meteorological Department (IMD) empirical reduction formula and to choose the optimal distribution that gives the greatest rainfall intensity among the three distributions used in this research (generalized extreme value, Log‐Pearson type III, and Gumbel). This study examined daily rainfall data collected from the Iraqi Authority of Meteorology and Seismic Monitoring for a period of 32 years, from 1991 to 2022. The IMD reduction formula was used to calculate rainfall intensity for shorter durations (5, 10, 20, 30, 60, 120, 360, 720, and 1440 min) and custom return periods (2, 5, 10, 25, 50, and 100 years). To determine the goodness of fit for the data distributions, the Easy Fit 5.6 program was applied with three tests (the χ2 test, the Anderson–Darling test, and the Kolmogorov–Smirnov test). The results showed that all distributions were acceptable for both tests and all storm durations and that the rainfall intensity decreased with increasing duration of the rainstorm. It also showed that rainfall increases rainfall intensity during large return periods. Based on the criteria of AIC and BIC, the LP‐3 distribution was chosen as the best distribution to simulate rainfall in Babylon City using the IDM reduction formula.

Formal processes and informal practices: how policy systems and corruption constrain the delivery of housing in Nigeria

ABSTRACT The delivery of housing is a recognised challenge in many Global South countries due to various institutional logjams within formal systems. This paper delves into formal processes and informal practices in Benin City to investigate how policy systems and corruption constrain the delivery of housing. As Benin City increasingly relies on informal mechanisms, bureaucratic bottlenecks impede progress, and corruption has emerged as a major global issue that hinders housing provision. A qualitative method was adopted, using interviews. The findings illustrate how corrupt practices typify housing delivery in Benin City, leading to inadequate and insufficient houses for residents. Among other recommendations, the paper advocates for adequate and effective supervision and monitoring of official processes geared towards housing production. It also suggests the need for increased transparency and accountability in allocating resources for housing projects. These measures would curb corruption and ensure that residents have access to decent and affordable housing.

Improved Building Information Modeling Based Method for Prioritizing Clash Detection in the Building Construction Design Phase

The rising complexity of construction projects and the industry’s commitment to sustainable practices have driven the extensive adoption of Building Information Modeling (BIM) technology. A core function of BIM is the early identification and resolution of clashes during the design phase, which serves to mitigate costly rework and delays in the construction process. This study presents an advanced method for classifying and prioritizing hard clashes between structural components and mechanical, electrical, and plumbing (MEP) systems. Employing the Best-Worst Method (BWM), this research assigned specific weights to structural and MEP elements based on expert evaluations. Six parameters were incorporated into this prioritization framework: the weights determined by the BWM, outputs from Navisworks software (v2021), the ratio of MEP volume to floor volume, the functional purpose of each floor, and the number of adjacent elements. A custom-developed plugin for Autodesk Navisworks integrated these parameters, enabling real-time automated clash prioritization. Clashes were ranked by criticality through a calculation involving the six parameters, which enhanced the efficiency of clash detection by optimizing time and cost considerations during the design phase. Case study results indicate that beams and columns represent the most critical structural elements, while ducts are identified as the most significant MEP elements. The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation.

Effectiveness of Force Account on Implementation of Construction Projects in Health Sector in Tanzania: A Case of Mbeya Regional Hospita

This study examines the effectiveness of the force account method in implementing construction projects within Tanzania's health sector, specifically at Mbeya Regional Hospital (MRH). Using a pragmatic research philosophy and a mixed-methods approach, the study employed a descriptive design with a sample of 144 respondents selected through stratified random and purposive sampling. Primary data were gathered via questionnaires and interviews, while secondary data were collected through document reviews. The findings revealed strong agreement on the timely completion of projects (mean = 3.7364), the quality of work exceeding traditional methods (mean = 4.4000), and more efficient resource utilization (mean = 3.9455). The study concludes that the force account method is effective in enhancing project timelines, quality, and resource efficiency in healthcare construction. Recommendations include training project teams, improving resource allocation, establishing monitoring frameworks, and engaging local communities to further optimize the effectiveness of force account implementation.

A hybrid metaheuristic and simulation approach towards green project scheduling

AbstractThis research tackles the environmental concern of greenhouse gas emissions in the execution of projects, with a focus on multi-site projects where the transportation of resources is a major source of emissions. Despite growing consciousness among consumers and stakeholders about sustainability, the domain of project scheduling has often overlooked the environmental impact. This paper seeks to bridge this oversight by exploring how to reduce greenhouse gas emissions during both project activities and resource transportation. A novel approach is proposed, combining a simulation model with an improved non-dominated sorted genetic algorithm. The simulation model incorporates the stochastic nature of emission rates and costs. This method is further refined with innovative techniques such as magnet-based crossover and mode reassignment. The former is a genetic algorithm operation inspired by magnetic attraction, which allows for a more diverse and effective exploration of solutions by aligning similar ’genes’ from parent solutions. The latter is a strategy for reallocating resources during project execution to optimize efficiency and reduce emissions. The efficacy of the proposed method is validated through testing on 2810 scenarios from established benchmark libraries, 100 additional scenarios adhering to the conventional multi-site problems, and a case study. The Best-Worst Method (BWM) is applied for identifying the best solution. The findings indicate substantial enhancements compared to traditional methods with a 12.7% decrease in project duration, 11.4% in costs, and a remarkable 13.6% reduction in greenhouse gas emissions.

P4PC: A Portal for Bioinformatics Resources of piRNAs and circRNAs

Background: PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs) are two kinds of non-coding RNAs (ncRNAs) that play important roles in epigenetic regulation, transcriptional regulation, post-transcriptional regulation of many biological processes. Although there exist various resources, it is still challenging to select such resources for specific research projects on ncRNAs. Methods: In order to facilitate researchers in finding the appropriate bioinformatics sources for studying ncRNAs, we created a novel portal named P4PC that provides computational tools and data sources of piRNAs and circRNAs. Results: 249 computational tools, 126 databases and 420 papers are manually curated in P4PC. All entries in P4PC are classified in 5 groups and 26 subgroups. The list of resources is summarized in the first page of each group. Conclusion: According to their research proposes, users can quickly select proper resources for their research projects by viewing detail information and comments in P4PC. Database URL is http://www.ibiomedical.net/Portal4PC/ and https://43.138.46.5/Portal4PC/.

Machine Learning Prediction of Recycled Concrete Powder with Experimental validation and Life Cycle Assessment study

The environmental effect of the construction sector has recently drawn attention, leading to research which promotes sustainability [1,2]. Therefore, different researcher recommends different alternative materials such as fly ash [3], waste glass [4], red mud [5] and solid waste [6]. Environmental sustainability and mechanical performance are two major concerns in the construction industry. Furthermore, the complex internal relationships between the components of such concrete determine the mix design, which is crucial for attaining the required compressive strength. This study addresses two key issues associated with the current concrete production. First, it uses recycled concrete powder (RCP) as a cement replacement (0%, 5%, 10%, and 15%) to promote sustainability and the undesirable impact of cement on the environment. Secondly, it proposes a predictive model based on machine learning for the compressive strength of RCP based concrete. A comprehensive dataset containing 270 experimental data points was compiled to train and test various machine learning (ML) models. The experimental results indicated that the optimum RCP mix with 10% replacement, achieved a compressive strength that was 15.8% higher than that of the reference concrete without RCP. Furthermore, scanning electronic microscopy indicates that internal structure improved with RCP due to filling and pozzolanic reaction. ML models indicate that Gradient Boosting was found to be the most precise, exhibiting the highest coefficient of determination with the lowest values for root mean squared error and mean absolute error. These findings provide valuable insights for engineers, contractors, and stakeholders, facilitating enhanced design optimization and promoting the efficient use of resources in concrete construction projects.

ENVIRONMENTAL RISK ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE DEVELOPMENT USING GIS IN LAGOS STATE

Transportation infrastructure development poses significant environmental risks, including habitat disruption, air and water pollution, and climate change. This study develops a GIS-based framework for ecological risk assessment of transportation infrastructure development. This research identifies high-risk areas and prioritizes mitigation measures by integrating spatial analysis, multi-criteria decision analysis, and expert consultations. The GIS-based approach evaluates environmental sensitivity, proximity to ecosystems, and potential hazards. Results provide valuable insights for sustainable transportation planning, policy-making, and ecological management. This study explored its application in solving Environmental Risk Assessment of Transportation Infrastructure Development GIS application in environmental Risk Assessment monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in Transportation system monitoring and soil heavy metal concentration monitoring, respectively. A major function of planners is to promote the best use of a community's land and resources for different construction projects; especially critical are infrastructure projects on which economic development relies. Transportation projects typically involve both environmental and economic issues facing a community as it grows and changes. Considerations of sustainability, as well as the widespread use of collaborative planning, design, and construction, require tools that facilitate long-term impact analysis and easy communication among built-environment professionals. Environmental impact assessment (EIA) is instrumental in studying transportation project impacts, but it is a time-consuming process because of the large number of dependent and independent variables involved. A vulnerability grade map and road distribution map were produced, providing an overall vulnerability score for each of the three planning alternatives considered. A road alignment was indicated as the preferred corridor, which was further recommended for a project development and environmental (PD&E) study.

THE SOLUTION TO THE PROJECT SCHEDULING PROBLEM BY USING AN IMPROVED GENETIC ALGORITHM

Nowadays, managing and allocating resources for projects has become increasingly essential for managers. A critical factor affecting the success of a project is the work assignment plan for workers to optimize the completion time. Current solutions to project scheduling problems have not been thoroughly addressed; thus, in this study, we model the labor assignment process in project production as a scheduling problem. To solve this problem, we use an improved genetic algorithm named GA-RT (Genetic Algorithm with Random Crossover and Negative Tournament Selection) and conduct experiments on the iMOPSE standard dataset. Experimental results show that the proposed GA-RT algorithm can effectively solve the project scheduling problem, achieving better performance compared to existing algorithms.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

PECULIARITIES OF THE ACTIVITIES OF WOMEN CULTURAL AMBASSADORS WHO CREATE A POSITIVE IMAGE OF UKRAINE IN THE WORLD IN THE CONTEXT OF THE RUSSIAN-UKRAINIAN WAR

The article emphasises that public diplomacy is one of those areas and dimensions where a woman, using all her skills, can best prove herself. After all, public diplomacy really has no restrictions, reservations, templates, frameworks, clearly defined rules and canons, protocol and regulations. It can have a wide variety of manifestations, include different tools, and at the same time speak freely to any foreign-language, foreign-culture audience. Today, especially in times of war, a woman of any profession can become influential in the field of public diplomacy, and this will be valuable in helping Ukraine. However, it is now important to support Ukrainian women, open up new opportunities for development, teach public diplomacy tools, promote the potential of public diplomacy at the community level, and create special organisations, platforms, initiate discussion and expert platforms so that Ukrainian women unite, share experiences in public diplomacy, seek and find material and spiritual resources for projects, and thus feel more confident and bold - this will be invaluable to our country. Everyone can engage in public diplomacy. And in the context of Russia's current brutal war against Ukraine, it is a duty for all Ukrainians: both those who have recently been forced to move abroad and those who have long lived in exile and are part of the Ukrainian diaspora. Even those who leave Ukraine for a short time for one reason or another unwittingly become ambassadors of their country. Today, any step, contact, speech, appeal, or larger action by a Ukrainian man or a Ukrainian woman may result in the attention and positive attitude of a foreigner who is a representative of the civil society of a particular country, which in turn may provide financial or military assistance to Ukraine. However, on the other hand, public diplomacy, in my expert opinion, is not for everyone. It is, indeed, a very thorough and complex phenomenon, something that requires flair, experience, practice, knowledge, skill, mastery and considerable resources, both intellectual, spiritual and material.

Practices and Challenges on the Relationship to Support the Goals of Impact Ranked Universities in the Philippines

This study investigated the collaborative efforts of Philippine Higher Education Institutions (PHEIs) with NGOs, government agencies, and other stakeholders in advancing the Sustainable Development Goals (SDGs). By leveraging their academic capabilities, PHEIs ranked by the Times Higher Education Impact Ranking played a crucial role in addressing global challenges through a multi-sectoral approach. This study emphasized the importance of collaborating with regional NGOs and government agencies for policy-making and multi-sectoral dialogue in promoting participatory decision-making. It also highlighted the significance of international collaborations in standardizing data gathering and sharing best practices, thus enhancing the quality and impact of research related to public policy and sustainable development. PHEIs' engagement with NGOs was pivotal in mobilizing resources for community-targeted projects, including disaster risk reduction, health and well-being, educational outreach, and environmental conservation. These initiatives contributed to immediate community needs and built long-term resilience and sustainability. Findings revealed that PHEIs were not only centers of learning but also key players in the global sustainable development agenda. Their ability to bridge knowledge gaps, coupled with the operational reach of NGOs and the policy influence of government bodies, formed a powerful force for societal transformation. As the world progressed towards the 2030 Agenda, it was essential for PHEIs to strengthen partnerships, expand research scope, and enhance community engagement practices. This would not only advance the SDGs but also shape an inclusive and resilient sustainable future. The study called for continued innovation in partnership models and inclusive strategies that engaged all societal sectors, ensuring substantial and measurable progress towards the SDGs. The ongoing commitment of PHEIs would be crucial in achieving a sustainable world for future generations.

Estimation of Reservoir Storage Capacity Using the Gould-Dincer Formula with the Aid of Possibility Theory

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be 1271×106 m3, and that for D = 0.5 was 634.5×106 m3. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal [315,5679]×106 m3 and for D = 0.5 the value is interval [158,2839]×106 m3, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions.

Influence of macroeconomic factors on construction costs: an analysis of project cases

This study explores the relationship between price increase and project cost increase under macroeconomic shocks by considering the hierarchical structure within price categories, diversification of the cost breakdown structure, infrastructure sector, delivery system, construction methods, and type of bid. The analysis of a large construction company with five large projects as embedded units of analysis reveals significant variations in the relative influence of specific factors across the projects. Unit price categories located higher in the hierarchical breakdown structure display lower vulnerability to macroeconomic changes due to their unique and group-specific influencing factors while price categories influenced by macroeconomic factors depart at lower values in the hierarchical structure. Projects with low-cost breakdown structure diversification can either mitigate or exacerbate cost increases depending on construction methods (off-site or on-site), price drivers of most representative materials (global or local), and delivery systems risk allocation. Under macroeconomic crisis periods, public entities may consider giving high priority to projects more reliant on materials driven by regional dynamics (e.g., concrete pouring) over those driven by global market conditions (e.g., steel, aluminum) while prioritizing integrated delivery systems (e.g., design-build) that promote enhanced collaboration, proactive risk management, aligned incentives, and adaptability for managing complexities and volatilities. Complementary, for projects with higher reliance on global market-driven materials, the results suggest that contractors should prioritize early procurement and acquisition of highly volatile resources in projects under macroeconomic crises.

Historical insights, current challenges: tracking marine biodiversity in an urban harbor ecosystem in the face of climate change

The Boston Harbor Islands is the only coastal drumlin archipelago in the USA, featuring a distinctive and uncommon geological intertidal habitat known as mixed coarse substrate, which supports a range of coastal species and ecological processes. Recently designated as one of America’s 11 most endangered historic places due to climate change impacts, coastal adaptation and restoration efforts are crucial to their preservation. Such efforts can benefit from historic and current knowledge of endemic and emergent biodiversity. To investigate broad trends in coastal biodiversity, we compiled an inventory of marine coastal macroalgae, macroinvertebrates, fish, mammals, and shorebirds observed in the harbor since 1861. Records span 159 years, consisting of 451 unique taxa from 19 phyla. Analysis of average taxonomic distinctness (AvTD) revealed increases in diversity towards the end of the twentieth and early twenty-first century, likely associated with improved water quality (dissolved oxygen; AvTD > 85, p = 0.01) due to harbor restoration in the 1980s. Macroinvertebrates comprised 50% of the records, making this the most diverse taxonomic group in the time series. A significant increase of non-indigenous species, primarily macroinvertebrates and macroalgae, was observed over the last 20 years near human infrastructure and across multiple islands, a consequence of global change and characteristic of most urban harbors. The mixed coarse intertidal habitat, which makes up > 70% of Boston Harbor’s inner islands and supports high macroinvertebrate and macroalgal diversity (47% of species records), is not routinely monitored; our findings serve as a foundational resource for climate adaptation projects and decision-making.