Hasil untuk "Engineering economy"

Caichi Liu, Tao Gong, Jun Zhang et al.

Abstract Developing earth-abundant transition-metal phosphides electrocatalysts with high activity and good durability toward alkaline hydrogen evolution reaction (HER) is crucial for sustainable hydrogen energy economy. However, their intrinsic activity is limited by the inadequate hydrogen adsorption energy. Herein, we reported a novel self-supported heterostructure catalyst in the form of Ni2P-NiSe2, which is obtained by phosphorization of NiSe2 nanosheet arrays grown on the carbon cloth. The heterostructure catalyst exhibits excellent HER performance in 1 M KOH, only requiring an overpotential of 66 mV at a current density of 10 mA cm−2 with a Tafel slope of 72.6 mV dec-1 and showing excellent long-term durability. The experimental and theoretical calculation results demonstrate the strongly electronic interaction between Ni2P and NiSe2, resulting in the optimized Gibbs free energy of hydrogen and water adsorption. This work concerning the regulation of electronic structure through interface engineering may offer a deep insight to explore superior catalysts.

Rahman Daiyan, Thành Trần-Phú, Priyank V. Kumar et al.

Critical to the feasibility of electrochemical reduction of waste NOx (NOxRR), as a sustainable pathway and to close the NOx cycle for the emerging NH3 economy, is the requirement of inexpensive, scalable and selective catalysts that can generate NH4+ with high yield, as indicated by our economic modelling.

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Roberto Verdecchia, Justus Bogner

From its first adoption in the late 80s, qualitative research has slowly but steadily made a name for itself in what was, and perhaps still is, the predominantly quantitative software engineering (SE) research landscape. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we reflect on the state of qualitative SE research with a focus group of experts. Among other things, we discuss why qualitative SE research is important, how it evolved over time, common impediments faced while practicing it today, and what the future of qualitative SE research might look like. Joining the conversation are Rashina Hoda (Monash University, Australia), Carolyn Seaman (University of Maryland, United States), and Klaas Stol (University College Cork, Ireland). The content of this paper is a faithful account of our conversation from October 25, 2025, which we moderated and edited for our column.

Dipin Khati, Yijin Liu, David N. Palacio et al.

Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.

Nina Raoult, Natalie Douglas, Natasha MacBean et al.

Abstract Accurately predicting terrestrial ecosystem responses to climate change over long‐timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimization, focusing on carbon‐water‐vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterizing errors due to observation biases and model‐data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple data sets, and including the spin‐up and historical period in the assimilation window. We outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation data sets, harness ML advances, and enhance the predictive capabilities of LSMs.

Rexford Asianoah, Brink Botha, Ayo Adeniran et al.

Commercial property development and investment (CPDI) is considered to be one of major investments which stimulate economic growth in many countries around the globe. Ghana is no exception. CPDI benefits include employment creation, tax revenue to the government, generation of income for investors and GDP increase. However, CPDI suffers from inherent risks when it comes to its planning, execution and management stage. Some of the inherent risks can be identified, assessed prior to execution if effective financial and professional analyses are conducted. Hence, the purpose of this study was to critically assess financial and professional risks on CPDI projects in Accra, Ghana. Based on this, an extensive literature review was conducted on the key variables such as PESTEL, strategic factors, PMBOK, financial and professional feasibility. The proposition is that these variables have significant effect on CPDI projects. Quantitative approach was employed to collect field data from the property practitioners within Accra enclave as respondents and, a survey of questionnaires was distributed using probability random sampling technique. Structural equation modelling was used to analyse the data gathered from the study respondents where 67% response rate was achieved. Analysis of the data proved that PESTEL analysis, strategic factors and PMBOK statistically have significant effect on CPDI projects risk assessment.

Abdelhalem Mahmoud Shahen, Mesbah Fathy Sharaf

In this study, we review recent research on how digital payment technologies (DPTs) promote financial inclusion (FI) across the world. Drawing on empirical studies from the past decade, we show that digital payment systems have helped reduce financial exclusion—particularly in developing economies—by expanding access to essential financial services for underserved groups. The paper also highlights the role of demographic factors such as age and gender, with evidence of higher adoption among youth and women. We identify the main indicators used to measure digital payment adoption and FI, providing a foundation for future empirical analysis. To deepen understanding, we call for combining macroeconomic data with rigorous econometric approaches to better capture how DPTs contribute to inclusive financial systems. The paper further discusses how emerging innovations—including blockchain, artificial intelligence, cloud computing, and biometric authentication—are improving the efficiency, security, and accessibility of digital payments. Together, these technologies are likely to accelerate the transition toward fully digital financial ecosystems and expand the potential for inclusive and sustainable growth.

Shenghui Li, Charles I. Addey, Raphaël Roman et al.

This paper highlights the urgent need to accelerate research and action on ocean carbon sinks through human intervention, known as the Global Ocean Negative Carbon Emissions (Global-ONCE) Programme, as a vital strategy in global efforts to mitigate climate change. Achieving “net zero” by 2050 cannot rely on emission reductions alone, emphasizing the necessity of complementary approaches. Global-ONCE’s mission extends beyond scientific exploration. It embodies a profound commitment to protecting and restoring blue carbon ecosystems, as well as implementing ocean-based solutions that are sustainable, equitable, and inclusive. Early career ocean professionals (ECOPs) are at the heart of these efforts, and their innovative approaches, technical expertise, and passion make them indispensable leaders in advancing ONCE initiatives. ECOPs bridge the gap between science and society, playing a relevant role in integrating cutting-edge research, technological advancements, and community-driven action to address climate threats. By bringing together diverse perspectives and leveraging their interdisciplinary expertise, ECOPs ensure that ONCE strategies are grounded in scientific rigor and practical feasibility. Through advocacy, education, and collaboration, ECOPs not only spearhead research and innovation but also inspire collective action to safeguard our oceans. This paper amplifies the critical role of ECOPs as agents of change and calls for a unified global commitment to harness the ocean’s potential for a climate-resilient future.

A. Iulianelli, E. Drioli

Abstract The current transformation of our linear and fossil fuels-based economy into a circular economy through the redefined paradigms of the sustainable development is requesting a major role of the renewable sources exploitation and the adoption of alternative technologies able to redesign a new industrial infrastructure not based on the fossil fuel utilization, but involving the concepts of wastes recycle and transformation into added value products. Membrane engineering moves under the principles of the Process Intensification Strategy applied to several industrial sectors, from the chemical and petrochemical industry to refinery, from water desalination and wastewater treatments to agro-food and gas separation etc. This review aims to analyze the impact of the membrane gas separation technology in petrochemical industry and refinery, highlighting the progress done on the membrane materials utilized in various industrial gas separation processes and discussing on the status of the implementation of membrane based operations in the various gas separation industrial fields and related markets. The benefits of their application in terms of improved process efficiency, reduced footprint, environmental protection and lower costs are also proposed. Furthermore, the importance of membrane reactors for fuel processing and membrane based pre-treatments and the integrated membrane gas separation systems is also discussed.

W. L. Lim, L. L. Liau, M. Ng et al.

M. Hauschild, S. Kara, I. Røpke

Abstract The global society faces huge challenges to meet the expanding needs of a growing population within the constraints posed by a climate crisis and a strongly accelerated loss of biodiversity. For sustainability, the total environmental impact of our activities must respect the planetary boundaries that define what is a safe operating space for our civilization. Engineering must change the current focus on eco-efficiency to a search for solutions that are effective in terms of operating within the share of the total pollution space that they can claim. Engineering for environmental sustainability must be life cycle engineering, and the paper positions it relative to the constraints given by the boundaries of the ecosystems, the targets of the United Nations’ sustainable development goals and the strategies for a circular economy. This top-down perspective is combined with a bottom-up perspective from the life cycle of the product and technology. For each stage of the life cycle, the contents of the toolbox for life cycle engineering are reviewed, and a perspective is given on how absolute environmental sustainability requirements can be incorporated in a target-driven life cycle engineering.

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi et al.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.

Christopher Garrett Lewis, Christopher Garrett Lewis, Muzan Williams Ijeoma et al.

The developing world continues to face substantial obstacles to achieving affordable and dependable electricity access. This issue is especially pertinent for Nigeria, where diesel generators are widely relied upon in urban and rural regions because of an underdeveloped and unreliable national grid. The lack of grid reliability is worsened in Northeastern Nigeria, an area plagued by conflict, extreme poverty, and grid infrastructure deterioration. This study investigates the feasibility of implementing community-scale microgrids in rural areas without grid connection access. It focuses on assessing the technical, economic, and environmental aspects of utilizing these microgrids to deliver inexpensive and dependable electricity to underserved populations to increase energy access. A case study was conducted in Kabuiri, a village with an estimated population of 2,300 residents and an estimated load demand of 610 kWh per day. A hybrid microgrid system was designed and optimized to meet the community’s load demand using HOMER software, sized to produce 610 kWh/day of electricity with a renewable penetration of 99%. The optimal solar PV/battery/generator system had a levelized cost of electricity (LCOE) of $ 0.093 per kWh, a net present cost (NPC) of $266,709, and an annual operating cost of $9,110. The system contributed 1,624 kg CO2 eq/year of global warming potential and 56.81 kg O3 eq/year of smog formation during operation. Sensitivity analysis showed that the system could effectively react to or adapt to substantial increases in diesel prices, requiring only marginal increases in photovoltaic capacity and reduced generator usage to maintain the most cost-efficient operation. Additionally, the system model can be adapted based on the population of the remote community without substantially impacting the LCOE, however, the NPC increases with increase in population size. This research will aid in increasing energy access in remote locations by providing insights to stakeholders and energy access project developers.

Weiwei Xie, Haifeng Wu, Boyu Liu et al.

The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening price from 2 January 2015 to 30 April 2022 were selected for an empirical demonstration of the model’s accuracy. The empirical results showed that the prediction of the exchange rate fluctuation with the EMD-LSTM-SVR model not only had higher accuracy, but also ensured that most of the predicted positions deviated less from the actual positions. The new model had a stronger generalization ability, a concise structure, and a high degree of ability to fit nonlinear features, and it prevented gradient vanishing and overfitting to achieve a higher degree of prediction accuracy.

Yanbo Liu, Junyi Wu, Haideng Li et al.

Background Daqu is an essential starter for baijiu brewing in China. However, the microbial enrichment and metabolic characteristics of Daqu formed at different fermentation temperatures are still unclear. Methods High-throughput sequencing technology and the non-targeted metabolomics were used to compare the microbial communities and metabolites of Taorong-type high-temperature Daqu and middle-temperature Daqu. In this study, the relationship between microorganisms and metabolites was established. Results The study found that the composition and metabolites of the microbial community differed due to the difference in Daqu-making temperature. The bacterial diversity of Taorong-type high-temperature Daqu was higher than that of middle-temperature Daqu, while the fungal community diversity of Taorong-type middle-temperature Daqu was higher than that of high temperature Daqu. A total of 1,034 differential metabolites were screened from the two types of Daqu, and 76 metabolites with significant differences were detected (P < 0.001 and variable importance in projection (VIP) > 1.15). Tetraacetylethylenediamine is the metabolite with the largest differential fold among the 76 differential metabolites, which can be used as a potential marker metabolite of high-temperature Daqu. Conclusion This study helps elucidate the microbial assembly mechanisms and functional expression under different processing conditions through a further understanding of the composition and metabolic profile differences of different types of Daqu microflora in Taorong-type baijiu.

Judith Verweijen, A. Dunlap

Abstract Ecological catastrophe and global inequality are pressing, yet socio-ecologically destructive natural resource extraction continues unabated. This special issue explores the strategies and tactics employed by large-scale mining and energy companies to render extraction socio-politically feasible in the face of multi-pronged opposition. Extraction, we contend, does not only need physical engineering, but requires social engineering as well. This entails shaping the behavior of people to ‘manage’ dissent and ‘manufacture’ consent. Situating the social engineering of extraction in key debates in the literature, this special issue introduction traces the evolution of its main technologies and techniques, related to colonialism, wars of decolonization, neoliberalism and the ‘green’ economy, respectively. We conclude by outlining a number of ways to advance research on the social engineering of extraction.

S. Joshi, SukhDev Mishra

Rising global energy demands and climate crisis has created an unprecedented need for the bio-based circular economy to ensure sustainable development with the minimized carbon footprint. Along with conventional biofuel such as ethanol, microbes can be used to produce advanced biofuels which are equivalent to traditional fuels in their energy efficiencies and are compatible with already established infrastructure and hence can be directly blended in higher proportions without overhauling of the pre-existing setup. Metabolic engineering is at the frontiers to develop microbial chassis for biofuel bio-foundries to meet the industrial needs for clean energy. This review does a thorough inquiry of recent developments in metabolic engineering for increasing titers, rates, and yields (TRY) of biofuel production by engineered microorganisms.

M. Cai, Peng Li, W. Tan et al.

Abstract The object of this article is to present the innovative technology system for the deep mining of metal mines in China. Based on the current situation and major problems of deep mining of metal mineral resources in China, the key engineering technology development strategies to solve the problems, such as the green intelligent mining modes, are proposed, which is conducive to ensuring the safety of mineral resources supply and the sustainable development of the national economy in China.