Completion Of Project Research Articles

Development and Evaluation of a Framework for Authentic Online Co-Design: Partnership-Focussed Principles-Driven Online Co-Design.

Co-design in health research involves patient and public involvement and engagement (PPIE) in intervention or service design. Traditionally, co-design is undertaken in-person; however, exploring online delivery is warranted. PPIE in co-design must be considered carefully, and assumptions that in-person approaches will transition automatically to an online environment should be avoided. Currently, there are a lack of evidence-informed approaches to facilitating co-design online. This study aimed to develop and evaluate a framework for authentically adapting health research co-design into an online environment. The initial framework was developed through a literature review, synthesis of in-person co-design principles, and alignment of online strategies. The framework was then applied to a co-design project with 10 participants across relevant PPIE groups (end-users [n = 4], clinicians [n = 2], coaches [n = 2] and clinician-researchers [n = 2]). Participants' experiences of the online co-design process were evaluated via a mixed-methods design using surveys and semi-structured interviews. Evaluation data were analysed using descriptive statistics and reflexive thematic analysis to inform a revised framework. The developed framework, Partnership-focussed Principles-driven Online co-Design (P-POD) was used to design eight 90 min online co-design workshops. Evaluation data involved 46 survey responses, and eight participants were interviewed on project completion. Survey data indicated that the process was satisfying, engaging and adhered to the P-POD framework. Themes derived from interview data describe a respectful and collaborative online culture, valuing of diverse perspectives and space for healthy debate, how power was perceived as being shared but not equal and multiple definitions of success within and beyond the process. A final, refined P-POD framework is presented. With evaluation of the initial P-POD framework showing evidence of adherence to co-design principles, positive participant experiences and goal achievement for both the project and the participants, the refined P-POD framework may be used and evaluated within future intervention or service design. This study involved the participants (end-users, clinicians and service providers) in the co-design process described, interpretation of the results through member-checking interview responses, assisting in development of the final framework and as co-authors for this manuscript.

Labour multiplier driven approach for budgeting project resources

Manpower demand for construction projects is roughly budgeted based on planners’ personal experience. This research study explores an analytical way to characterise project labour demand profiles throughout project duration. Labour multiplier (LM) approach was advanced to characterise the labour demand profiles. Optimistic, most-likely, and pessimistic LM and labour demand profiles at 10 project stages are defined by using manpower and payment data derived from 91 completed public construction projects under 21 project types in Hong Kong. The root-mean-square-error and root-mean-squared-logarithmic-error are used to quantify the difference between the budgeted and the benchmarked manpower. A text-book example is successfully adopted to demonstrate the steps of the proposed approach, validated by two practical projects. This research is the first to study the use of LM to characterise optimistic, most-likely, pessimistic resource demand profiles for validating manpower resource budgets. Also, the approach provides a basis to evaluate the reasonableness of planned resource.

Optimizing Learning: Predicting Research Competency via Statistical Proficiency

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilize the supervised machine learning approach to predict students’ research competency as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, the understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills—can be used to predict students’ research competency as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. The findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

Party affiliation predicts homeowners’ decisions to install solar PV, but partisan gap wanes with improved economics of solar

The perceived risk of climate change and the sense of urgency for an energy transition are both politically polarized, especially in the United States. Yet, we know relatively little about how political polarization affects consumer energy preferences and behaviors. Here, we use the case of residential solar photovoltaics (PV) in New York State to 1) measure the partisan gap in solar adoption rates and 2) test whether more favorable economics of solar PV mute the effect of political identity. Using household-level, longitudinal data that include nearly 63,000 completed residential PV projects, we find evidence of a partisan gap in PV adoption. Democratic homeowners are approximately 1.45 times as likely to adopt solar PV as Republican homeowners. Republicans' rate of adoption is the lowest of all measured groups, behind Independents, unaffiliated voters, and homeowners not registered to vote. Crucially, however, Republicans in our sample appear to be the most attuned to the changing economics and financing options of solar PV. Our estimates suggest that 1) as homeowners' electricity rate increases relative to its long-run average, the adoption gap between Democ-rats and Republicans narrows, 2) that Republican PV adopters obtain systems with higher expected economic value, and 3) Republicans take greater advantage of alternative financing models, like leases and power purchase agreements, especially when the upfront costs of solar are high. The results demonstrate that political identity affects consumers' participation in the energy transition, but local context, including the local economics of solar, may mitigate the effect of personal politics.

Restoring the Lower Danube River's wetlands: a short report on the hydrological effectiveness of completed projects

Restoring the Lower Danube River's wetlands: a short report on the hydrological effectiveness of completed projects

Workforce forecasting in the building maintenance and repair work: Evaluating machine learning and LSTM models

Effective workforce forecasting is critical to strategic management in construction projects, particularly ensuring staffing is optimized for efficient and timely project completion. This aspect holds particular importance in densely populated urban environments, where the complexity and scale of high-rise building construction exacerbate the need for proactive maintenance strategies. This research aims to compare the efficacy of various machine learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), gradient-boosted decision trees (GBDT), and multi-layer perceptron (MLP) models, in predicting the workforce demand for building maintenance and repair work. Accordingly, 128 sets of data composed of seven indicators collected from the Hong Kong building maintenance and repair contractors were curated to serve as input-dependent variables to forecast the demands for different labor categories in addition to the overall workforce demands. The performance metrics for the four machine learning models were evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and determination coefficient (R2), with GridSearch employed to optimize their hyperparameters for enhanced predictive accuracy. The evaluation using MAE, MSE, RMSE, and R2 revealed that the XGBoost model achieved the most favorable results for predicting workforce demand on the testing sets (0.1790, 0.0582, 0.2412, 0.9915). Compared to MLP (0.1828, 0.0775, 0.2783, 0.8821) and GBDT (0.1828, 0.0828, 0.2878, 0.8739), XGBoost demonstrated a stronger performance in this specific task. Furthermore, the study also explored the potential of a long short-term memory (LSTM) model to forecast the gross value of building maintenance and repair work undertaken by main contractors, which strongly correlates with workforce demand. This approach aimed to capture the consistent growth trend observed across various maintenance activities. Consequently, the models developed in this study serve as valuable tools for optimizing workforce allocation and financial planning, particularly in dynamic and ever-changing environments.

Clarifying Ethical Dilemmas in Sharpening Students’ Artificial Intelligence Proficiency: Dispelling Myths About Using AI Tools in Higher Education

Artificial intelligence has been talked about for over half a century now. Still, it became a fast-growing reality in 2023 through modern technologies, such as Meta AI, Open AI, or ChatGPT, and has created some ethical concerns. This research provides examples of how AI is being used in academia, how it can be used, and how to assess college students’ familiarity with such technologies, their perception of it, and level of usage. Using an AI-generated short survey to gather quantitative and qualitative data through a discussion exercise, 126 undergraduates with four different professors were asked to share their answers and views. The findings show that many of today’s college students in South Florida see the usage of AI as ethical and legal. However, a few respondents remain uncertain due to a lack of clear guidelines from professors and the institution. Thus, most respondents reported that they are familiar with AI as they use it multiple times weekly. Consequently, educators and administrators must sharpen their students’ AI skills so they can be ethical and competitive in the workplace. Implications for students, educators and administrators in the higher education arena are explored. Besides serving as a person’s second brain, using AI can be an excellent way for students to mitigate and overcome procrastination, enhance their productivity, and comprehensively complete academic projects on time. Furthermore, the proper use of AI tools can reduce errors, quickly assess large amounts of data, automate repetitive functions, lead to better decisions, and help learners move forward amid challenging obstacles. As such, academic institutions must do more to ensure they are “sharpening their students’ AI saw” before they graduate and embark on their professional endeavors. Artificial intelligence, when used properly, ethically, and legally following established industry norms and guidelines, offers many transformative benefits across diverse fields to benefit human beings and society. Students pursuing a healthcare career can use AI to aid in early disease detection, accelerate drug discovery, and improve patient care through precision medicine. Graduates in the engineering or transportation industries can use AI to optimize traffic flow, enhance safety with autonomous vehicles, and reduce emissions through predictive maintenance. Moreover, those who remain in the education field after graduation can use AI to facilitate personalized learning experiences tailored to individual student needs while fostering greater engagement and academic success for all learners. The latest advancements underscore AI’s potential to drive innovation, increase efficiency, and address complex challenges while ultimately shaping a more interconnected and prosperous future for everyone in society.

Time Performance Analysis Using Earned Value on the Construction of the Aranaway Kiram Bridge in Banjar Baru City

When implementing a project in the field, it is fairly uncommon for numerous projects to be delayed or even halted. This also has an impact on project cost escalation, resulting in project losses. Earned value analysis can help manage construction projectsefficiently and effectively. The results value analysis was done to estimate the extent to which the project was carried out according to the work plan. This assessment was performed on the Aranaway Kiram Bridge Construction project in Banjarbaru City. The project was aimed to determine weekly time performance and project completion time. The method employed was earned value analysis. According to the results of the analysis method (earned value analysis), the results from the 3rdto the 18thweek experienced delays, as evidenced by the performance of the Schedule Variance (SV) which was Negative (-) and the Schedule Performance Index (SPI) which was less than 1, but work accelerated in the 19th week of the Banjarbaru City Aranaway Kiram Bridge Construction Project, as evidenced by the SV performance which was Positive (+) and SPI greater than 1. The research results estimated the project's completion time as 253 days.

Application of BIM in Civil Engineering

This paper explores how the use of Building Information Modeling (BIM) can be a beneficial platform for Civil engineers. The utilization of BIM (Building Information Modeling) is becoming a hot issue for many public work projects. Also, its market seems to be expanded as BIM is applied in procurement constructions. This concept is the basis of Building Information Modeling (BIM) technology. There is now a growing interest in the adoption of this technology within the Architecture, Engineering and Construction (AEC) industry. The capacity of BIM for civil engineering to study many possibilities enhances results and offers data-driven confidence that projects can be completed on time and under budget. BIM provides shared information throughout the course of a project, producing powerful outcomes. The method of building information modelling (BIM), which is based on 3D models, is the cornerstone of the digital transformation. The information around design assets may be created and managed by civil engineers, resulting in increased teamwork, shared data, and project completion that is both on schedule and within budget. Next, the idea of Building Information Modelling (BIM) and its practical benefits are described. Main part of the paper is devoted to discussion about what kind of difficulties we may encounter during the implementation of the BIM technology and how they are related to the potential benefits.

A NOVEL BIM-ENABLED SOFTWARE BASED SOLUTION FOR MITIGATING CONSTRUCTION RISK MANAGEMENT

This paper presents a novel Software based solution to mitigate the most prevalent construction risks with systematic implementation of Building Information Modeling Software at the lifecycle stages of the project and provides practical recommendations for industry practitioners. The construction industry has a complex and uncertain process presenting different opportunities and challenges at various project phases. Incorporating traditional risk management methods with BIM help identify and manage risks effectively. This research paper presents a systematic approach to implementing BIM throughout the lifecycle of an eight-story building project, from feasibility to construction, to manage major risks in each phase. The paper addresses a gap in the literature by identifying and mitigating prominent risks in the construction industry of Pakistan through BIM modeling, based on a review of 180 common risks and detailed interviews. The research methodology involved surveys and data analysis to identify and prioritize the most significant risks and suitable BIM mitigation measures. The study suggests that BIM’s collaboration software resolves communication issues while Clash Detection mitigates Design-related risks before the start of construction. Simulation of Planned vs. Actual Progress and accurate Quantity Estimates achieved by BIM’s estimation software contributes to the successful completion of project. BIM and its tools have the potential to revolutionize the construction process if used as a structured risk management tool throughout the project lifecycle. The paper advises designing out most project risks in the Planning and Design phase, while reserving monitoring and control for later stages as early identification and management of risks is critical for success.

The African fulcrum to bend the curve of the climate crisis to a just transition

From a current impact of under 4% of global greenhouse gas emissions, rapid industrialization and population growth in Africa could dramatically change the continent’s emissions profile. In this study, we develop an analytic framework to quantify future scenarios and project that, in mid- and green-growth scenarios, Africa’s emissions would amount to just 4%–13% of the planned carbon savings in major economies. However, in a high-growth scenario without climate-conscious development, African emissions could jeopardize global mitigation efforts. Less than 20 nations could account for 80%–90% of the continent’s emissions, highlighting the critical role of green growth pathways centered on rapid clean energy adoption in just a few countries to transform the continent’s energy landscape. A 20-fold increase in investment and project completion rates is required to meet the renewable energy targets in these countries’ Nationally Determined Contributions (NDCs). Our analysis underscores the need for nuanced country-specific strategies that prioritize equity and financial support for optimal climate and development progress in Africa.

External Debt, Transmission Channels, and Economic Growth: Evidence of Debt Overhang and Crowding-Out Effect

This study investigates the intricate relationship between external debt, debt service, and economic growth by using the panel data of 32 Asian Developing Economies (ADE) spanning 1995 to 2020. Employing a two-step system generalized method of moments (GMM) and a dynamic common correlated estimate (DCCE) model, the research explores key macroeconomic channels through which debt influences growth and rigorously tests for debt overhang and crowding-out effects. Findings reveal that public and private investment, total factor productivity, and national savings are pivotal channels transmitting the non-linear effect of external debt on economic growth. Notably, only private and public investment convey the non-linear effects of debt service to economic growth, while productivity and savings convey the linear effect. Evidence of debt overhang and crowding-out effects is identified in the sampled economies. The study suggests strategic measures for developing countries, emphasizing the productive use of accumulated debt, enhanced debt management, and timely project completion. Furthermore, it advocates for fostering economic growth through increased productivity, domestic savings, and private sector expansion to reduce dependence on foreign debt, facilitating both debt repayment and economic self-sufficiency.

Application of the Least Cost Analysis Method to Determine the Optimal Cost and Duration for Delayed Projects

The construction project of the Polresta Denpasar Multi-Purpose Building experienced delays due to the Eid al-Fitr holiday, resulting in a 9.35% setback, equivalent to 19 days. In the 20th week, the planned progress was 66.52%, but actual progress was only 57.17%. With 76 days remaining for the project's completion, the least cost analysis method was applied to find the optimal cost and time for completing the remaining work by increasing the workforce. This approach aims to ensure the project is completed on time and the contractor avoids penalties. The penalty is 1/1000 (1 ‰) of the contract value. The analysis used primary data such as interviews and project observations, and secondary data including contractor unit price analysis, budget plans (RAB), and S-curves. The data was analyzed from the remaining project work, crashing duration, to finding the cost slope for each task, and then using the least cost analysis method to determine the optimal cost and time. The analysis results showed that the optimized project cost after acceleration was Rp 3,077,713,130.90, with the project duration reduced from 76 days to 57 days, accelerated by 19 days. This acceleration saved costs by Rp 18,428,493.93. By applying this method, the project is expected to be completed faster without compromising quality, and the contractor can avoid delay penalties. This approach helps in time efficiency and budget management, ensuring all resources are used optimally and the project is completed on target.

Wspólnotowy potencjał sztuki street artu: efemeryczne wspólnoty wokół efemerycznej sztuki

The article analyzes the phenomenon of street art in the context of its impact on the creation of local or global communities of both creators and audiences. It takes into consideration various forms of expression such as graffiti, community murals, or interventions by individual artists. It examines to what extent the communities formed around artistic activities are lasting and cohesive. Graffiti, as a form of art typical of the hip-hop subculture, represents expressions by groups that are closed and anonymous for the majority of society. Despite strong connections, these groups are usually temporary and may disband after the completion of specific projects. Other forms of street art are typically created by individual artists seeking to develop a unique style. Open, global networks of enthusiasts, activists, and organizations form around them, with the internet and social media serving as platforms used to maintain the connection between artists and their audience. Some street artists (e.g. JR or Swoon) are particularly sensitive to social issues and contribute to forming and sustaining local communities through their creations, even if they do not necessarily belong to them. A specific form of street art are community murals commissioned by a community to enhance its identity and cohesion. Street art and graffiti, therefore, have the ability to create communities on various levels, even despite their informal and ephemeral nature.

Life Cycle Cost Analysis in Flat Buildings (Case Study of Polresta Bukittinggi Flat Construction Project)

The housing crisis in various cities in Indonesia has become an increasingly urgent issue. Rapid population growth and urbanization are major factors contributing to this crisis. One proposed solution is the development of high-rise building projects as a more practical and space-saving housing solution. However, in the context of high-rise construction, economic planning is crucial to ensure project sustainability. This research utilizes a quantitative method with secondary data collection. Life Cycle Cost (LCC) analysis is employed to control project costs from the initial stage to project completion, including construction, operational, maintenance, and demolition costs. The analysis results indicate that initial construction costs, operational costs, maintenance costs, and demolition costs all need to be considered in project planning. Maintenance and upkeep costs are crucial aspects in ensuring the building's sustainability throughout its economic life. By considering all these factors, the total life cycle cost of the construction of Rusun Polresta Bukittinggi during its operational period of 50 years amounts to Rp 32,064,519,750.22. The Annual Equivalent (AE) analysis shows an average annual cost of is Rp. 6,287,507,789.17.

Collaborative Learning Integrated Pancasila Profile Based on Cultural Diversity to Improve Critical Thinking Ability

Critical thinking is the ability to analyze, interpret and evaluate information objectively and rationally needed in the 21st century to face future challenges through innovative approaches in order to take advantage of opportunities in the digital and global era. Critical thinking skills at the elementary school level are still low and need to be improved through collaborative learning according to the implementation of the independent curriculum. The profile of Pancasila students is the skills that Indonesian students must have based on the noble values of Pancasila. This study aims to test the application of collaborative learning integrated with the Pancasila student profile based on cultural diversity with the Project Based Learning learning method to improve critical thinking skills. This study uses quantitative research pre-experiment method of one group pretest-posttest design. The subjects of this study involved 18 grade 4 students who were divided into small groups and calculated using the N-Gain test. The N-Gain test is a measuring technique to determine the effectiveness of a treatment before and after learning. The results of this study found the overall average N-Gain results showed moderate criteria between 0.3≤g≤0.7 and quite effective at a percentage between 56-75. Another result of this study, by doing collaborative learning students can become peer tutors and innovate to complete projects given by teachers from the development of an independent curriculum integrated with the Pancasila learner profile. Therefore, collaborative learning integrated with the Pancasila learner profile is concluded to improve critical thinking skills.

AutoRepo: A general framework for multimodal LLM-based automated construction reporting

Ensuring the safety, quality, and timely completion of construction projects is critical, and construction inspections play an important role in achieving these objectives. Nevertheless, the predominantly manual approach to inspections frequently results in inefficiencies and inadequate information management. These methods often fail to provide a comprehensive and detailed assessment, resulting in regulatory oversight and potential safety hazards. To address this issue, this paper presents a novel framework named AutoRepo for automated generation of construction inspection reports. In AutoRepo, the unmanned vehicles efficiently perform construction inspections and collect scene information, while the multimodal large language model (LLM) is used to automatically generate the inspection reports. The framework was applied and tested on a real-world construction site, demonstrating its potential to speed up the inspection process, significantly reduce waste of resource, and generate high-quality inspection reports that meet regulatory standards. Thus, this research highlights the great potential of multimodal LLMs in revolutionizing construction inspection practices, signaling a significant leap forward towards a more efficient and safer construction management paradigm.

From Formation to Evaluation: Optimising groupwork in STEM Degree Apprenticeships

Engineering degree apprenticeships provide three to six years of job opportunities and training that lead to university qualifications up to and equivalent of a postgraduate degree. Within an apprentice’s time in higher education (HE), many are required to complete groupwork projects which bring several benefits and some challenges. This paper explores the effects and limitations of group work in the context of degree apprenticeships whilst discussing a plethora of group selection and assessment method solutions. This includes student self-selection methods in comparison to teacher-selected and randomisation for group selection; a suitable method of selection should be unbiased and optimise advantages of collaborative working. Through understanding the most effective form of assessment for apprentices, an academic policy can be adapted and influenced – acknowledging the learning differences between undergraduate students and degree apprentices. As part of this work, a comprehensive literature review was carried and a survey was conducted and completed by the Bsc Digital and Technology Solutions students, BEng Applied Professional Engineering Programme students among others at the University of Warwick. The results of the survey provided quantitative and qualitative data surrounding group work. Furthermore, the paper suggests appropriate solutions to group work issues surrounding the effectiveness of group work for degree apprentices, optimal group selection-methods, group performance, ensuring equal contributions and assessment and grading methods. The paper addresses some of the problems encountered by apprentices during their studies such as the free-rider problem and unequal division of work - providing solutions towards minimising such problems, including the integration of web-based tools such as MS Planner.

Power and Interest Matrix Stakeholders of the Geothermal Development Project in Indonesia

Indonesia has 23 GW of geothermal reserves and is ranked second with the largest geothermal installed capacity in the world. Currently, the electricity generated from geothermal energy is 2,418 MW, that only 10% of geothermal energy utilization in Indonesia. This is because geothermal projects are capital-intensive, complex, and sensitive to uncertainty and risk thus make the investment less viable. Based on the literature review, simplifying the geothermal development process can reduce project costs and accelerate project completion to make it more attractive One of them is the effectiveness of coordination between stakeholders related to geothermal development. This research aims to identify key stakeholders and map them based on the power and interest of each key stakeholder involved in geothermal development using the Stakeholder Analysis approach. The Delphi method is employed in this study, a group of experts contributes insights, revealing a clear pattern in identifying and mapping stakeholders in geothermal development in Indonesia. The final round result of expert validation led to the inclusion of 14 stakeholders in the key stakeholder category from 30 identified stakeholders initially. Among the 14 key stakeholders, an assessment based on power and interest was conducted. The results showed that 11 stakeholders were categorized as players, while 3 key stakeholders were classified as context setters.

Narratives in exposomics: A reversed heuristic determinism?

Since the completion of the Human Genome Project (HGP), biomedical sciences have moved away from a gene-centred view and towards a multi-factorial one in which environment, broadly speaking, plays a central role in the determination of human health and disease. Environmental exposures have been shown to be highly prevalent in disease causation. They are considered as complementary to genetic factors in the etiology of diseases, hence the introduction of the concept of the "exposome" as encompassing the totality of human environmental exposures, from conception onwards (Wild in Cancer Epidemiol Biomark Prev 14:1847-1850, 2005), and the launch of the Human Exposome Project (HEP) which aims to complement the HGP. At first sight, and seen as complementary to the genome, the exposome could thus appear as contributing to the rise of novel postgenomic deterministic narratives which place the environment at their core. Is this really the case? If so, what sort of determinism is at work in exposomics research? Is it a case of environmental determinism, and if so, in what sense? Or is it a new sort of deterministic view? In this paper, we first show that causal narratives in exposomics are still very similar to gene-centred deterministic narratives. They correspond to a form of Laplacian determinism and, above all, to what Claude Bernard called the "determinism of a phenomenon". Second, we introduce the notion of "reversed heuristic determinism" to characterize the specific deterministic narratives present in exposomics. Indeed, the accepted sorts of external environmental exposures conceived as being at the origins of diseases are determined, methodologically speaking, by their identifiable internal and biological markers. We conclude by highlighting the most relevant implications of the presence of this heuristic determinism in exposomics research.