Hasil untuk "Engineering economy"

Imran Kadolkar, Sven Kepes, M. Subramony

Rapid growth in the gig economy has been facilitated by the increased use of algorithmic management (AM) in online platforms (OPs) coordinating gig work. There has been a concomitant increase in scholarship related to AM across scientific domains (e.g., computer science, engineering, operations management, management, sociology, and law). However, this literature is fragmented with scholars disagreeing on the conceptualization and measurement of AM, as well as a lack of consensus on the dimensions of AM influencing various gig worker‐related outcomes, the mechanisms through which these influences are exerted, and the relevant boundary conditions. To address these issues, we systematically reviewed the academic literature across scientific disciplines related to the AM of gig workers using natural language processing (NLP)‐based topic modeling. Our analysis yielded 12 topics, which we integrate using an input‐process‐output (IPO) framework to illustrate differing effects of AM on worker‐related outcomes. Based on our findings, we provide a comprehensive definition of AM, including its key dimensions, and highlight main mediating pathways through which the individual dimensions of AM impact various gig worker‐related outcomes. Finally, we provide a roadmap for future research on AM in the gig economy (GE) using an organizational behavior lens.

Dimitris Katikaridis, Lefteris Benos, Dimitrios Kateris et al.

Unmanned ground vehicles (UGVs) in agriculture face challenges in navigating complex environments due to the presence of dynamic obstacles. This causes several practical problems including mission delays, higher energy consumption, and potential safety risks. This study addresses the challenge by shifting path planning from reactive local avoidance to proactive global optimization. To that end, it integrates aerial imagery from an unmanned aerial vehicle (UAV) to identify dynamic obstacles using a low-latency YOLOv8 detection pipeline. These are translated into georeferenced exclusion zones for the UGV. The UGV follows the optimized path while relying on a LiDAR-based reactive protocol to autonomously detect and respond to any missed obstacles. A farm management information system is used as the central coordinator. The system was tested in 30 real-field trials in a walnut orchard for two distinct scenarios with varying worker and vehicle loads. The system achieved high mission success, with the UGV completing all tasks safely, with four partial successes caused by worker detection failures under afternoon shadows. UAV energy consumption remained stable, while UGV energy and mission time increased during reactive maneuvers. Communication latency was low and consistent. This enabled timely execution of both proactive and reactive navigation protocols. In conclusion, the present UAV–UGV system ensured efficient and safe navigation, demonstrating practical applicability in real orchard conditions.

Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo et al.

Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Yungpil Yoo, Sang-Yup Lee, Seok-Ho Seo et al.

The feasibility of integrating a CCU (Carbon Capture and Utilization) plant into a green methanol production system was studied through detailed energy, exergy, and thermoeconomic analyses from process, economic, and environmental perspectives. In this system, wood chips are combusted with oxygen generated by an electrolyzer in a circulating fluidized bed boiler. Carbon dioxide, a primary component of flue gas, reacts with hydrogen produced by the electrolyzer to synthesize green methanol. The chemical formula for wood chip combustion was modeled and used in this study. Our thermodynamic modeling allows us to calculate the oxy-fuel combustion, carbon capture, and water electrolysis processes to obtain the amounts of carbon dioxide and hydrogen required to produce methanol. When the unit costs of wood chips and electricity are $0.15/kg and $0.120/kWh, respectively, and the system’s initial investment cost is $117.9 million, the production cost of green methanol is calculated to be $1.393/kg. Additionally, the unit costs of hydrogen and oxygen produced by the electrolyzer were $4.75/kg and $0.30/kg, respectively. However, if the unit cost of electricity is reduced by 50 %, the production cost of green methanol decreases to $0.90/kg, which is close to the market price. If the carbon dioxide produced is sold as carbon credits at $0.50/kg, the unit price of methanol drops to $0.89/kg. Using wind, solar, or nuclear energy to electrolyze water, the carbon emissions of this methanol plant are estimated to be around 0.11–0.45 kgCO2/kgMeOH.

Max Neuwinger, Dirk Riehle

The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.

Marvin Wyrich, Lloyd Montgomery

A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.

Manish Kumar Sah, Biraj Shah Thakuri, Jyoti Pant et al.

The current economic development paradigm, which is based on steadily rising resource consumption and pollution emissions, is no longer viable in a world with limited resources and ecological capacity. The “green economy” idea has presented this context with a chance to alter how society handles the interplay between the environmental and economic spheres. The related concept of “green nanotechnology” aims to use nano-innovations within the fields of materials science and engineering to generate products and processes that are economically and ecologically sustainable, enabling society to establish and preserve a green economy. Many different economic sectors are anticipated to be impacted by these applications, including those related to corrosion inhibitor nanofertilizers, nanoremediation, biodegradation, heavy metal detection, biofuel, insecticides and pesticides, and catalytic CO₂ reduction. These innovations might make it possible to use non-traditional water sources safely and to create construction materials that are enabled by nanotechnology, improving living and ecological conditions. Therefore, our aim is to highlight how nanotechnology is being used in the green economy and to present promises for nano-applications in this domain. In the end, it emphasizes how critical it is to attain a truly sustainable advancement in nanotechnology.

Yuchen Wu, Yan Gao, Chao Tian et al.

Tick-borne diseases are of substantial concern worldwide for both humans and animals, and dogs are frequently exposed to tick infestation. The aim of this study was to examine tick infestation of pet and stray dogs in Guangzhou, Southern China, as well as tick-borne pathogens of randomly sampled ticks. Ticks were found on 118 (14.1% [95% confidence interval; CI: 13.6–14.6]) out of 836 animals in four veterinary clinics and two shelters. A total of 280 individual ticks were collected from tick-infested dogs. Rhipicephalus sanguineus sensu lato (s.l.) was identified. PCR tests were conducted to detect six tick-borne pathogens in 280 ticks. The prevalence of tick-borne pathogens was 5.3% for Babesia spp., including Babesia vogeli (5.0% [95% CI: 4.3–6.3]) and Babesia gibsoni (0.3% [95% CI: 4.3–6.3]). These results indicated that R. sanguineus s.l. was the tick species found in dogs in Guangzhou city, and that dogs were at risk to exposure to babesiosis, implying the need for surveillance for this disease in dogs in this region.

Tat-Dat Bui, Hien Minh Ha, Thi Phuong Thuy Tran et al.

This study is to build a causality model to implement energy security strategies (ESSs) in approaching a world-regions comparison. This study contributes to ESSs by indicating a set of valid attributes and those attributes are interrelationships in nature. There is major global interest in ESSs due to the pressure to ensure sustainable energy supply sources. An adequate energy source is decisive for ensuring stable economic growth, enhancing social development, and protecting the environment. Nonetheless, in reviewing the energy literature, generating strategic attributes is still lacking, which leads to difficulties for policymakers in building, executing, and assessing energy policies. This study utilizes a hybrid method: text mining, cluster analysis, fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, and entropy weight method. As a result, five aspects and 22 criteria from the data pool are validated. The causal model shows that the energy control system, strategic collaboration and technological capability are the priority. In practice, the effect aspects are waste-to-energy and energy resilience. Although the research trends on ESSs in different regions are quite similar, each continent still has unique concerns such as European countries with distributed energy resources, Asia and Oceania with decarbonization, African countries with new technologies, and Americas with energy planning.

Funmilayo Ebun Rotimi, Marcela Brauner, Megan Burfoot et al.

Diversity and inclusion of the construction workforce are considered fundamental to disrupting the perception of the male-dominated construction industry. Despite efforts to increase diversity and inclusion, the construction industry continues to record a slow increase in women’s representation, resulting in the industry missing significant potential talent. Therefore, identifying women's work environment challenges is vital for promoting construction careers. This study examines three categories of challenges: benevolent sexism, hostile sexism, and job conditions and the influences of demographic factors. The study adopted a quantitative research method, with 65 structured questionnaires completed by women working in the industry. The study found that benevolent sexism challenges, such as stereotyping and pressure to prove oneself and hostile sexism challenges, like masculine culture, sexual harassment, and lack of respect, are significant for women in construction. A lack of female role models and work overload are two job condition-related challenges that affect women in the industry. The findings from this study are an important contribution to the existing literature, as they highlight the need to consider demographic factors when creating initiatives to address the challenges faced by women in the construction industry.

Otilia Manta, Maria Palazzo

The existing global multi-crises have generated significant transformations in the architecture of financial systems, impacting local communities. Furthermore, the digital era has created a conducive environment for the development of financial innovations that can generate financial instruments supporting financial inclusion. Our research aims to identify and develop innovative financial instruments that foster closer collaboration within communities and promote a more equitable distribution of wealth and resources, directly impacting financial inclusion and well-being. The methodology used in our study is based on existing empirical research in the specialized scientific literature, as well as on identifying variables within existing models. Additionally, the use of bibliometric analyses and research tools based on artificial intelligence allows us to structure the innovative financial instruments found in the scientific databases. Building on the existence of innovative financial instruments, our paper specifically explores the concept of time banking as an innovative financial instrument, offering a new approach to economic exchange and the construction of financial mechanisms at the local community level. By using technology, especially in digital and ecological eras, time banks can be efficiently managed through online platforms where individuals can register their contributed hours and access the services they need. This study’s conclusions emphasize that time banks have the potential to serve as innovative financial instruments. Furthermore, through the analysis conducted in this study and the identified models, this study contributes to redefining the concept of time banking as an innovative financial instrument. Time banks focus on the productivity and efficiency of local community activities, with direct implications for reducing dependence on traditional currency and promoting an equitable distribution of labor. This innovative approach is promising, especially in an increasingly digitized financial landscape. Our paper seeks to capture this transformative potential and highlight our personal contributions to redefining the time bank as an innovative financial instrument.

Lianbaichao Liu, Zhanping Song, Ping Zhou et al.

Abstract In geological engineering and related fields, accurately and quickly identifying lithology and assessing rock strength are crucial for ensuring structural safety and optimizing design. Traditional rock strength assessment methods mainly rely on field sampling and laboratory tests, such as uniaxial compressive strength (UCS) tests and velocity tests. Although these methods provide relatively accurate rock strength data, they are complex, time-consuming, and unable to reflect real-time changes in field conditions. Therefore, this study proposes a new method based on artificial intelligence and neural networks to improve the efficiency and accuracy of rock strength assessments. This research utilizes a Transformer + UNet hybrid model for lithology identification and an optimized ResNet-18 model for determining rock weathering degrees, thereby correcting the strength of the tunnel face surrounding rock. Experimental results show that the Transformer + UNet hybrid model achieves an accuracy of 95.57% in lithology identification tasks, while the optimized ResNet model achieves an accuracy of 96.13% in rock weathering degree determination. Additionally, the average relative error in tunnel face strength detection results is only 9.33%, validating the feasibility and effectiveness of this method in practical engineering applications. The multi-model neural network system developed in this study significantly enhances prediction accuracy and efficiency, providing robust scientific decision support for tunnel construction, thereby improving construction safety and economy.

Aydan Garrod, Shanza Neda Hussain, Meet Hemantbhai Intwala et al.

Bifacial photovoltaics (bPV) provide an advantage over their traditional monofacial counterparts as they can utilise solar radiation incident on both the front and rear side of the module, allowing for increased energy production. While this is an advantage on the side of bPV, the amount of energy produced by the rear side of the bPV panel can vary greatly depending on the inclination and azimuth of the panel as well as external climatic and ground albedo conditions. This paper aims to analyse the electrical and thermal performance of bPV under varying albedo conditions, using two different materials: grass, and a reflective material. It also contains an example of bifaciality testing under real-world conditions. Several experiments were conducted to evaluate the electrical performance and the temperature distribution of the bPV panel under different weather conditions at the UK location and then the results were compared with the single diode model of bPV. Furthermore, a bPV panel temperature model has been developed for the temperate climate condition of UK. The results show that the open circuit voltage of the system increases with irradiance up to around 800 W/m2, at which point it decreased due to the increased PV system temperature. The normalised efficiency for the PV system under different conditions were also evaluated, which showed that the bPV module was most efficient under diffuse irradiance conditions, encouraging the use of the bPV technology in the UK.

Johan Cederbladh, Antonio Cicchetti

In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.

Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach et al.

Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.

Styliani Avraamidou, S. Baratsas, Yuhe Tian et al.

Abstract Rising populations put huge stresses on natural resources. Extraction and depletion of raw materials and waste created throughout the supply chain of products have enormous environmental and socioeconomic impacts. One way to reduce these impacts is through the move towards the circular economy (CE). CE aims to solve resource, waste, and emission challenges confronting society by creating a production-to-consumption total supply chain that is restorative, regenerative, and environmentally benign. This article highlights research challenges and identifies process systems engineering (PSE) research opportunities to assist in the understanding, analysis and optimization of CE supply chains. A motivating example on the supply chain of coffee is introduced to illustrate the challenges of the transition towards a CE and to propose PSE research opportunities.

Xiaoming LUO, Zihan JIA, Hongyang ZHANG

Hydrogen, as an efficient energy carrier and clean fuel, has a demand for large-scale storage. At present, the underground hydrogen storage (UHS), in types of salt caverns, depleted oil and gas reservoirs and aquifers, is the most feasible solution balancing the storage security and economy. Specifically, UHS in the depleted oil and gas reservoirs is most promising among the three types. Herein, the particularity of UHS was discussed by comparing the difference of physical properties among H2, CH4 and CO2. In particular, the technical challenges and coping strategies for UHS in depleted oil and gas reservoirs were overviewed: (1) For the gas leakage caused by unstable displacement, seepage and diffusion, efforts should be made to control the gas injection rate, optimize the scheme of injection-production and cushion gas arrangement, and study the caprock breakthrough pressure, the surface interface characteristics and the flow and mass transfer mechanism. (2) For the hydrogen-consuming geochemical reaction and microbial catalysis, the strata with highly hydrogen-sensitive minerals, ions and microorganisms should be excluded from UHS potential sites to prevent unacceptable H2 consumption. (3) To cope with the integrity failures of traps and artificial materials, the risk of formation damage and gas leakage should be evaluated considering the macroscopic and microscopic deformation and fracture evolution characteristics of the formation. Besides, appropriate materials should be selected to enhance the resistance of artificial facilities to corrosion and hydrogen embrittlement. Finally, the directions of research on UHS technology were pointed out, including the research on H2 loss mechanism under multi-scale and multi-field coupling, the numerical simulation study on-site scale of UHS, and the gas migration and leakage monitoring technology in UHS. Generally, this research could provide a reference for promoting the engineering practice of UHS.

Hanna Hrinchenko, Viktor Koval, Nadiia Shmygol et al.

The study considers ways to ensure the quality of the functioning of power equipment as one of the directions for ensuring the safety of energy complexes in the period of “green transformation”. Based on the analysis, it is established that for an effective “green” transformation, it is necessary to ensure the energy safety of equipment operation, which is possible by developing effective science-based approaches to technical diagnostics of the state of power systems and individual equipment. The main objective of the study is to develop a Safety Management Model for Life Term Operation of power equipment, which takes into account changes in its condition at different stages of operation and allows for the prediction of further safe operation. The paper proposes an approach to researching the technical condition of power equipment, taking into account the deformations that occur during operation. The results of the calculation of stress intensity coefficients for longitudinal and transverse-postulated cracks in different modes are presented. Based on the calculated and experimentally obtained values, an approach to predicting the operating life of power equipment with regard to changes in technical characteristics is proposed. The results of the calculations showed that by changing the allowable load on the material of the energy equipment to 35 kgf/mm², the estimated operational lifespan could be extended until 2035, ensuring all necessary safety conditions. It has been proven that with effective management and continuous safety diagnostics, nuclear power plants have the potential to operate beyond their standard design life of up to 30–40 years. This approach allows for the quality and safe operation of power equipment in the context of the transition to sustainable energy.

Haris Alibašić

The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.