Hasil untuk "Engineering economy"

Klara Borowa, Andrzej Zalewski, Lech Madeyski

The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.

LIN Yongfeng, CHENG Zhen, LIU Wenmei, ZOU Zehua, LIU Hong, LIU Guangming, LIU Qingmei

In this study, the physicochemical properties of polysaccharides from Houttuynia cordata Thunb. fermented with Lactiplantibacillus plantarum HM6008 (FHCTP) were determined, and the antiallergic activity was evaluated using rat basophilic leukemia (RBL)-2H3 cells. The results showed that fermentation increased the ratio of mannose to sulfate in FHCTP. Compared with H. cordata Thunb. polysaccharides (HCTP), the particle size of FHCTP decreased by 26.67%, and its stability in aqueous solution increased. The inhibition rate of FHCTP on the degranulation of RBL-2H3 cells was significantly higher than that of HCTP, (82.79 ± 5.19)% versus (53.75 ± 1.95)%. After FHCTP intervention, the expression of fragment crystallizable epsilon receptor I (FcεRI) was significantly down-regulated, and the average fluorescence intensity decreased from 2 458.00 ± 7.50 to 1 495.00 ± 28.50. Both FHCTP and HCTP effectively inhibited the isomerization of cytoskeletal proteins and the increase of intracellular calcium ion concentration. In addition, in the mouse passive cutaneous anaphylaxis assay, FHCTP showed a more significant inhibitory effect on dye extravasation in mouse ears, indicating stronger antiallergic activity. In conclusion, FHCTP has better stabilizing effect on mast cells and effectively alleviates mast cell-mediated passive cutaneous anaphylaxis in mice. The results of this research are expected to promote the development and application of antiallergic products from edible and medicinal materials.

G. Hasibuan, M. T. Al Fath, N. Yusof et al.

Pattarawan Intasian, K. Prakinee, A. Phintha et al.

Since the industrial revolution, the rapid growth and development of global industries have depended largely upon the utilization of coal-derived chemicals, and more recently, the utilization of petroleum-based chemicals. These developments have followed a linear economy model (produce, consume, and dispose). As the world is facing a serious threat from the climate change crisis, a more sustainable solution for manufacturing, i.e., circular economy in which waste from the same or different industries can be used as feedstocks or resources for production offers an attractive industrial/business model. In nature, biological systems, i.e., microorganisms routinely use their enzymes and metabolic pathways to convert organic and inorganic wastes to synthesize biochemicals and energy required for their growth. Therefore, an understanding of how selected enzymes convert biobased feedstocks into special (bio)chemicals serves as an important basis from which to build on for applications in biocatalysis, metabolic engineering, and synthetic biology to enable biobased processes that are greener and cleaner for the environment. This review article highlights the current state of knowledge regarding the enzymatic reactions used in converting biobased wastes (lignocellulosic biomass, sugar, phenolic acid, triglyceride, fatty acid, and glycerol) and greenhouse gases (CO2 and CH4) into value-added products and discusses the current progress made in their metabolic engineering. The commercial aspects and life cycle assessment of products from enzymatic and metabolic engineering are also discussed. Continued development in the field of metabolic engineering would offer diversified solutions which are sustainable and renewable for manufacturing valuable chemicals.

Bruna Santos, Filomena Freitas, Abílio J. F. N. Sobral et al.

ABSTRACT The growing environmental crises demands an urgent transition from a linear to a circular economy. Microalgae are photosynthetic microorganisms that offer exceptional potential due to their rapid growth, high CO₂ fixation capacity, and ability to remove nutrients and pollutants from wastewater, producing both clean water and valuable biomass. Such characteristics have attracted interest in developing circular systems that transform wastes into resources such as biomaterials, biofertilisers, biofuels and bioactive compounds. However, various challenges hinder their industrial application, including technical, economic, environmental, commercial and political barriers. Technical limitations such as inefficient culture systems, low productivity and contamination risks, can be addressed by using genetic engineering tools to develop superior strains, and by developing bioreactors coupled with emerging technologies (AI, Digital Twin). Additionally, it was found that studies using wastewater for microalgae cultivation and a biorefinery approach to recover low and high value bioproducts were found to be energetically, environmentally and economically viable. Several projects and studies demonstrating microalgae-based circular economy models were highlighted. Finally, the implementation of clear regulations and guidelines for wastewater composition in microalgae systems is recommended to facilitate market acceptance and consumer trust in microalgae-derived products.

W. Zimmermann

Haoxuan Yu, I. Zahidi, C. Fai et al.

Muneera Bano, Hashini Gunatilake, Rashina Hoda

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

Rui Wang, Hongru Wang, Boyang Xue et al.

Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitioning from fast and intuitive thinking (System 1) to slow and deep reasoning (System 2). While System 2 reasoning improves task accuracy, it often incurs substantial computational costs due to its slow thinking nature and inefficient or unnecessary reasoning behaviors. In contrast, System 1 reasoning is computationally efficient but leads to suboptimal performance. Consequently, it is critical to balance the trade-off between performance (benefits) and computational costs (budgets), giving rise to the concept of reasoning economy. In this survey, we provide a comprehensive analysis of reasoning economy in both the post-training and test-time inference stages of LLMs, encompassing i) the cause of reasoning inefficiency, ii) behavior analysis of different reasoning patterns, and iii) potential solutions to achieve reasoning economy. By offering actionable insights and highlighting open challenges, we aim to shed light on strategies for improving the reasoning economy of LLMs, thereby serving as a valuable resource for advancing research in this evolving area. We also provide a public repository to continually track developments in this fast-evolving field.

Mohammed Saleh Alshaikh

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

Suman Lata Yadav

The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.

Fengyi Zhu, Ece Kendir Cakmak, Z. Cetecioglu

Marilene Pavan, Kristina Reinmets, Shivani Garg et al.

High levels of anthropogenic CO2 emissions are driving the warming of global climate. If this pattern of increasing emissions does not change, it will cause further climate change with severe consequences for the human population. On top of this, the increasing accumulation of solid waste within the linear economy model is threatening global biosustainability. The magnitude of these challenges requires several approaches to capture and utilize waste carbon and establish a circular economy. Microbial gas fermentation presents an exciting opportunity to capture carbon oxides from gaseous and solid waste streams with high feedstock flexibility and selectivity. Here we discuss available microbial systems and review in detail the metabolism of both anaerobic acetogens and aerobic hydrogenotrophs and their ability to utilize C1 waste feedstocks. More specifically, we provide an overview of the systems-level understanding of metabolism, key metabolic pathways, scale-up opportunities and commercial successes, and the most recent technological advances in strain and process engineering. Finally, we also discuss in detail the gaps and opportunities to advance the understanding of these autotrophic biocatalysts for the efficient and economically viable production of bioproducts from recycled carbon.

Marcus Kessel, Colin Atkinson

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.

Carlos Alberto Durigan Junior, Kumiko Oshio Kissimoto, Fernando Jose Barbin Laurindo

The fourth industrial revolution promotes the integration of Information Technology (IT) and strategic resources. New IT demands and uses have been leading to changes in business processes and corporate governance. Lately, the financial industry has adopted a new integrated banking model known as Open Banking (OB) and the advent of cryptocurrencies has led to the Digital Economy (DE) materialization. Considering these facts, this paper expects to point out through literature review some IT enabling factors that allow the conception of a new industry design (or governance) specifically in the financial industry illustrated by the cases of the Open Banking and Digital Economy. This paper is structured mostly on literature review, accompanied by results, discussions, and finally, conclusions are presented. It was found five potential enabling factors. Keywords: Digital Economy, Information Technology (IT), Open Banking.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

Valerio Terragni, Annie Vella, Partha Roop et al.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

Chuan Shen, Xia Li, Jianfeng Qin

Abstract Intercropping systems have garnered attention as a sustainable agricultural approach for efficient land use, increased ecological diversity in farmland, and enhanced crop yields. This study examined the effect of intercropping on the kiwifruit rhizosphere to gain a deeper understanding of the relationships between cover plants and kiwifruit in this sustainable agricultural system. Soil physicochemical properties and bacterial communities were analyzed using the Kiwifruit-Agaricus blazei intercropping System. Moreover, a combined analysis of 16S rRNA gene sequencing and metabolomic sequencing was used to identify differential microbes and metabolites in the rhizosphere. Intercropping led to an increase in soil physicochemical and enzyme activity, as well as re-shaping the bacterial community and increasing microbial diversity. Proteobacteria, Bacteroidota, Myxococcota, and Patescibacteria were the most abundant and diverse phyla in the intercropping system. Expression analysis further revealed that the bacterial genera BIrii41, Acidibacter, and Altererythrobacter were significantly upregulated in the intercropping system. Moreover, 358 differential metabolites (DMs) were identified between the monocropping and intercropping cultivation patterns, with fatty acyls, carboxylic acids and derivatives, and organooxygen compounds being significantly upregulated in the intercropping system. The KEGG metabolic pathways further revealed considerable enrichment of DMs in ABC transporters, histidine metabolism, and pyrimidine metabolism. This study identified a significant correlation between 95 bacterial genera and 79 soil metabolites, and an interactive network was constructed to explore the relationships between these differential microbes and metabolites in the rhizosphere. This study demonstrated that Kiwifruit-Agaricus blazei intercropping can be an effective, labor-saving, economic, and sustainable practice for reshaping bacterial communities and promoting the accumulation and metabolism of beneficial microorganisms in the rhizosphere.

Silas M. Mbeche, Paul M. Wambua, David N. Githinji

Human hair (HH) is considered a waste material generated in salons and barbershops in most societies, especially highly populated cities, where it is produced in large quantities, thus rekindling the interests of academics. Several studies are ongoing on the possibility of utilizing it as a reinforcement in polymer composites, either in its raw form or as extracted keratin nanoparticles, due to its unique features and the current global emphasis on circular economy. The present review seeks to provide a synopsis of recent developments in the utilization of HH and keratin in polymer composites. Composites from different HH loading, length, and chemical treatments were made using hand lay-up and hot compression molding methods. HH has been investigated in diverse composite systems, encompassing HH/natural fiber composites, HH/synthetic fiber composites, and keratin-reinforced composites. Our study revealed that these innovative materials exhibit enhanced energy absorption capacity, mechanical strength, hardness, and thermal properties, positioning them as promising choices for a wide range of engineering applications. The review further revealed that keratin nano-particles can be extracted from waste HH using various methods such as reduction alkaline hydrolysis and can be used as reinforcement in polymer composites.

LIN Yan, JIN Tingyu, YANG Yuchao

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.