Search Results (42)

Digital portfolios have become an essential assessment tool in project-based and student-centered learning environments. Unfortunately, students exert minimal effort in creating digital portfolios because they find the writing component unchallenging. This issue is concerning since existing research predominantly focuses on the use of pre-existing platforms for building digital portfolios. With this concern, there is an opportunity to explore more challenging approaches to digital portfolio creation. Consequently, this study employs a project-based learning (PBL) approach within a website design and development course, where 176 undergraduate students completed weekly coding tasks culminating in a self-coded digital portfolio. Using a one-group posttest-only research design, data were collected through a structured questionnaire that included demographic items and validated scales measuring learning effectiveness and ownership of learning. The survey was administered electronically after students submitted their digital portfolio projects. The results reveal that device ownership shows only weak associations with students’ perceptions, while internet connectivity and self-reported academic performance demonstrate stronger relationships with engagement and ownership of learning. Additionally, prior experience with digital portfolios positively influences students’ engagement, motivation, and ownership of learning. Implications of these findings are discussed for supporting the integration of digital portfolios into technical disciplines. Overall, this study contributes to the literature on PBL methodology, expands our understanding of digital portfolio integration, and underscores the significance of student-centered pedagogies. Full article

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes. Full article

To address environmental challenges, governments globally have implemented a range of carbon reduction policies. Based on their mechanisms of action, this study categorizes these policies into regulatory (e.g., carbon taxes and cap-and-trade) and supportive measures (e.g., production cost subsidies, low-carbon technology subsidies, and low-carbon product subsidies). By integrating these approaches, six distinct policy combinations were identified. A supply chain network equilibrium model, incorporating manufacturers, retailers, and demand markets, was developed using variational inequality theory to assess the effectiveness of these combinations. Numerical simulations were conducted with a modified projection algorithm, and the Borda method was applied to evaluate equilibrium outcomes, specifically production levels and emission reductions, under each policy combination, revealing synergies between policies. The analysis revealed three key findings: (1) Among supportive policies, the combination of low-carbon product subsidies with regulatory policies is the most effective, while production cost subsidies are relatively weaker; (2) carbon tax policies combined with supportive policies outperform carbon trading; (3) a higher intensity of regulatory policies enhances the incentive effects of supportive policies. This research offers valuable guidance for governments seeking to design evidence-based policy portfolios that effectively balance emission reduction goals with economic development priorities. Full article

Offshore wind energy has become a critical component of global efforts to transition toward low-carbon and sustainable energy systems, and although Taiwan’s advantageous geographical position has accelerated its progress in this domain, many of Taiwan’s upcoming offshore wind projects remain in a pre-construction phase, raising questions about their viability for complementary wave energy integration. To address this challenge, this study proposes a hybrid Cross-Efficiency Slacks-Based Measure (CE-SBM) Data Envelopment Analysis (DEA) model. Thirteen announced offshore wind projects were evaluated using spatial and resource-related input variables and energy-centric output variables. The self-efficiency results from the SBM stage highlighted several projects—most notably Zhu Ting, Wo Neng, and Chu Tin—as highly effective in resource utilization under their own weighting schemes. However, the subsequent cross-efficiency analysis added a consensus-based perspective, revealing a clear performance hierarchy and identifying inefficiencies in projects such as Greater Changhua Northeast and Winds of September. These findings underscore the value of combining DEA-based models with slacks-based and cross-efficiency features to guide multifaceted energy development. By prioritizing projects with robust efficiency profiles, policymakers and stakeholders can expedite Taiwan’s broader adoption of integrated wind–wave energy systems, ultimately fostering a more reliable and sustainable marine energy portfolio. Full article

The management of public institutions is focused not only on providing and improving public services but also on managing the physical infrastructure that these institutions use—buildings for provision of such services. The focus of this paper is on decision support to the management of individual buildings and the set of such buildings (portfolio) during the planning phase. More precisely, it is directed towards support towards both the decision-maker (DM) and decision-making process (DMP) when planning construction activities/projects such as maintenance, renovation, reconstruction, extension, construction, design/preparation of project-technical documentation, etc. The aforementioned DMP includes the processing of a large amount of diverse data (technical, economic, social, etc.) expressed differently—numerically or descriptively, as well as in different units of measurement, simultaneously taking into account the different wishes and attitudes of stakeholders (consequently meeting their often conflicting goals and criteria). The above indicates that it is a complex and ill-defined multi-criteria problem faced by the DM/planner. On top of that, and knowing that the DM usually does not have all the necessary knowledge and skills, this paper proposes how to overcome these issues by supporting the DM within the DMP during such a planning process. The proposed concept promotes an integral (considering relevant aspects of this management problem) and inclusive (taking into account the views of relevant stakeholders) approach to managing complex construction projects and their portfolios. It is methodologically based on the logic of decision support systems and multi-criteria analysis. The multi-criteria methods used include the Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE) for the evaluation and comparison of alternatives in an integral manner, as well as the Analytic Hierarchy Process (AHP) for determining the weights of criteria and achieving an inclusive and consistent approach to relevant stakeholders (based on the goal tree approach). The concept was tested on the planning of infrastructure management at a university in the Republic of Croatia, and it was proven to be useful because it provided the DM with a basis for decision making. The usefulness of the concept was confirmed by the concordance of the plan obtained using the concept and the activities/projects actually realized. Full article

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research. Full article

This study examines the impact of issue-based teaching (IBT) on programming self-efficacy among non-Computer Science students. Grounded in social cognitive theory, the research investigates how IBT influences learning satisfaction and project success compared to traditional metrics. This study employed a mixed-methods approach, combining the quantitative analysis of student performance and self-efficacy measures with qualitative feedback from learning portfolios and project reports. The findings indicate that programming self-efficacy is a stronger predictor of learning satisfaction and project success than traditional performance metrics like grades. For novice programmers, IBT effectively enhances self-efficacy, positively influencing goal identification and performance. This cascade effect highlights the importance of fostering self-efficacy in programming education for non-technical students. Qualitative analysis reveals that IBT contributes to students’ sense of achievement, motivation, and learning satisfaction, encouraging them to view programming as a practical problem-solving tool. This study concludes that IBT offers an effective approach to enhancing interdisciplinary and STEAM education, recommending that educators focus on building self-efficacy through issue-based, learner-centered approaches. Full article

Optimizing resource allocation often requires a trade-off between multiple objectives. Since projects must be fully implemented or not at all, this issue is modeled as an integer programming problem, precisely a knapsack-type problem, where decision variables are binary (1 or 0). Projects may be complementary/supplementary and competitive/conflicting, meaning some are prerequisites for others, while some prevent others from being implemented. In this paper, a two-objective optimization model in the energy sector is developed, and the Non-dominated Sorting Genetic Algorithm III (NSGA III) is adopted to solve it because the NSGA-III method is capable of handling problems with non-linear characteristics as well as having multiple objectives. The objective is to maximize the overall portfolio’s EVA (Economic Value Added). EVA is different from traditional performance measures and is more appropriate because it incorporates the objectives of all stakeholders in a business. Furthermore, because each project generates different kilowatts, maximizing the total production of the portfolio is appropriate. Data from the Greek energy market show optimal solutions on the Pareto efficiency front ranging from (14.7%, 38,000) to (11.91%, 40,750). This paper offers a transparent resource allocation process for similar issues in other sectors. Full article

This research uses data analysis and mining techniques to determine the technological expansion of measurement systems in a public service company. It integrates technical, economic, geographic, and social variables into the analysis using machine learning techniques to discover patterns and relationships in large data sets. The fuzzy logic methodology is applied using the MATLAB programming tool “Fuzzy Logic” to build algorithms that allow for the correct selection of measurement, achieving greater efficiency and precision in the assignment of meter types. The results show that 98% of the metering systems in the significant part are electronic meters, with the “Residential BT” rate being the most extensive data set. Implementing the “fuzzy logic” technique recognizes that more than 60% of the meters are electronic, with the registration of active energy, reactive energy, and demand, allowing for greater control over the marketing variables of the distribution system operator. This research suggests that a future restructuring of electrical metering systems benefits the company and its users. By applying the analysis, a portfolio of viable projects for the replacement of measurement systems is obtained, and they are grouped into two clusters based on the total cost of the technological change. Full article

The Constant Leverage covering strategy for the equity momentum portfolio (CLvg) developed in this project cannot mask its shortcomings by increasing leverage. It has to successfully forecast and avoid more losses than profits to perform better than the momentum portfolio. This approach is different from other covering strategies available in the literature that focus on increasing the right tail of the momentum returns distribution at a faster rate than they increase the left tail. The CLvg strategy only depends on past information and uses the daily volatility of the loser portfolio to determine episodes of high and low volatility. The daily volatility of the loser portfolio has a stronger relationship with large negative momentum returns than the daily volatility of the momentum portfolio. The daily volatility of the loser portfolio also has a weaker relationship with larger positive monthly returns, and it is more predictable because it has a higher volatility persistence. The negative effects of transaction costs on the CLvg strategy are measured using bid and ask prices reported by CRSP from 1992 to 2021. During this period, the stock market presented an average excess return of 9.19% and a Sharpe ratio of 0.61, and 9.74% of its returns were crashes, which is a better performance than the momentum portfolio. The CLvg adjusted by transaction costs presented excess returns of 16.93% and a Sharpe ratio of 0.84, and only 8.31% of its returns were crashes. Full article

Water environment governance of large rivers is an essential measurement of ecological protection in China. The effectiveness of governance depends highly on the fulfillment of strategic CSR (corporate social responsibility) of Chinese leading enterprises. However, the fulfillment of strategic CSR often brings economic burden to the leading enterprises and needs government compensation support. Therefore, this paper explores the impact of government compensation on the fulfillment of strategic CSR of Chinese leading enterprises by constructing a multi-stage portfolio model. The main results show that (1) the prerequisite for leading enterprises to fulfill strategic CSR is sufficient profits; (2) the increase in government compensation can significantly affect the proportion of strategic CSR fulfillment of leading enterprises; (3) only when the portfolio considers both strategic CSR and project benefits, can the leading enterprises obtain the highest economic returns and better fulfill strategic CSR. The results can provide policy reference for the government to stimulate the fulfillment of leading enterprises’ strategic CSR and also give suggestions for the leading enterprises to alleviate the economic pressure brought by fulfilling strategic CSR. Full article

The research question this study poses is how to measure the efficiency of project management activities. The purpose of this article is to quantify the efficiency of the execution of a project portfolio managed by a project management office (PMO) structure. The research subject is a PMO operating within a petrochemical manufacturing company in southern Brazil. The research method is quantitative modeling. The study employed data envelopment analysis (DEA) to calculate the relative efficiencies of projects in three classes according to complexity over a period of four years. Each project is a decision-making unit (DMU), as required by the DEA procedure. One novelty is the calculation of cost- and time-weighted efficiency values, which slightly differ from the average. The main results indicate that the average efficiency for classes of projects roughly stands between 40 and 80%. The results also indicate a learning process guided by the PMO, as the average efficiency increased over three years in two classes of projects, according to the prioritization imposed by the office. The study also pointed out that the most influential variables in determining project efficiency are accuracy in meeting deadlines and the time planned for completion. The most important implication is that, from now on, the company has a theoretical foundation to justify focusing further efforts on reducing and controlling time to completion, not only cost and scope conformity, to increase overall project efficiency. Future research should prioritize investigating management techniques that increase the likelihood of completing projects within their deadlines. Full article

In today’s rapidly evolving information technology sectors, agile methodologies have been employed by software development organizations to facilitate the large-scale, efficient, and swift development of digital products. Notably, it is a widely accepted principle that an increase in project delivery predictability results from more effective portfolio management. Despite the abundant resources within software engineering that address project management and agile development performance, the measurement of portfolio delivery performance integrating sustainability principles is under-researched. This paper aims to underline a solution to bridge this gap by proposing a data-driven scoring model explicitly designed for software firms to integrate sustainability metrics into their project and portfolio performance assessment. The model is primarily aimed at monitoring and enhancing delivery performance while also reinforcing the sustainability of the software development lifecycle. A thorough literature review was conducted to discern gaps in existing practices, followed by development of a scoring model melded with delivery and sustainability metrics seamlessly. Validated through a case study, the findings reveal that the model influences the performance and sustainability dynamics within software development entities. The insights gained from this study underscore the pivotal role of a harmonized delivery and sustainability metrics system in enhancing the sustainability and efficiency of software development undertakings. Full article

Build-to-rent (BTR) is an important pathway for rental housing supply across the cities of Australia. The discourse has become more prevalent as stakeholders seek government support to expand the sector to provide, amongst other reform measures, affordable rental accommodation. This paper examines the effectiveness of BTR as a pathway to affordable housing supply in Australia and identifies the additional reforms needed to increase that supply further. Interviews with key stakeholders were used to assess the success of the asset class to date and how it is likely to perform in increasing affordable rental housing supply, as well as what additional reforms may be required to expedite supply. The paper found that while BTR aims to scale up rental housing supply, its limitations in its present form demonstrate the need for additional reforms. At present, unfavorable taxes, inner-city locational attributes, and ultramodern services make the asset class a high-end housing model. Experts posit that BTR rental prices are justified given the quality amenities and the 24/7 onsite professional management. While it is a premium product, several BTR projects in Australia have included portions of those projects for affordable housing. The study concluded that BTR can generate affordable housing outcomes in Australia. As more developments take place across the private sector, several regulatory reforms in land release and taxes are needed to produce a sizable portfolio of affordable BTR. Without government support and regulation through additional supply-side incentives, development and recurrent holding costs will remain high, making BTR unaffordable to many urban residents. Full article

Achieving a good level of resilience to extreme events caused by severe weather conditions is a major target for operators in modern power systems due to the increasing frequency and intensity of extreme weather phenomena. Moreover, regulatory authorities are pushing transmission and distribution operators to prepare resilience plans suitably supported by Cost–Benefit Analyses (CBAs). In this context, this paper proposes a CBA framework based on Optimization via Simulation (OvS) for the selection of the optimal portfolio of resilience enhancement measures. Starting from a comprehensive set of candidate grid hardening and operational measures, the optimal mix is identified by applying a novel two-step procedure based on an efficient application of the generalized pattern search heuristic technique. Risk indicators for the CBA are quantified, accounting for probabilistic models of climate changes. Moreover, the potential cascading outages due to multiple component failures provoked by extreme events are simulated on selected scenarios. The examples carried out on an IEEE test system show the effectiveness of the approach in identifying the best portfolio of resilience enhancement measures depending on climate change projections and costs of the measures, while the application to the model of a large portion of the Italian EHV transmission system demonstrates the practicability of the approach in real-world studies to support operators in different power system management phases, from planning to operation. Full article