Study region: The Mekong River Basin Study focus: This study reduces the multi-agent bargaining game to a one-to-one model by assuming downstream countries act as coalitions in water allocation scenarios. Each country’s risk level and perception inform its discount factor, which is then aggregated and converted into coalition discount factors through weighted averaging. Then, a Rubinstein bargaining water allocation model with multi-agent participation and multi-stage negotiation is constructed and applied to allocate water in the Mekong River Basin. New hydrological insights for the region: The proposed Multi-stage Rubinstein Bargaining Model produced allocations that were more stable than those generated by traditional bankruptcy rules such as Proportion, Adjusted Proportion, Constrained Equal Loss, Constrained Equal Award, and Shapley. Therefore, this allocation framework can serve as both a theoretical foundation and a practical tool for water allocation in transboundary river basins.
Panagiotis Regkouzas, Nancy Katie, Konstantinos Bontiotis
et al.
As climate change keeps progressing, the use of biochar and compost as nature-based materials emerges as sustainable alternative that can have a positive impact on agriculture. Such valuable organic materials produced from previously considered waste products are increasingly viewed as attractive nature-positive solutions to improve soil quality and fertility that could help in climate change mitigation and address sustainable development goals. In this study, compost and compost-derived biochar were produced from the organic fraction of municipal solid waste and tested as soil amendments for the cultivation of lettuce (Lactuca sativa L), irrigated with both tap water and secondary wastewater effluent. Physicochemical characterization of compost, biochar and soil showed that the investigated materials were not so rich, compared to biochars from different feedstocks but when applied to the soil for lettuce cultivation, they may provide a positive effect. The combined effect of the use of bio-based materials and irrigation with treated wastewater was found to lead to higher soil and crops quality. Longer crop growth cycles are necessary to clearly demonstrate the positive effect of biochar on crop yield. Such materials can effectively be used for sustainable agriculture, contributing to the promotion of nature-based solutions as useful tools that promote circular practices.
Scarcity of water is a universal problem affecting more than 2 billion individuals, with about 785 million having no access to basic drinking water. Spurred by the population growth, urbanization, and global climate change, meeting this challenge coheres with the UN's Sustainable Development Goal 6 to make water available by 2030. Desalination, which transforms saline water that constitutes approximately 97 % of the global resources of water into fresh water, is progressively critical, particularly in dry and coastal areas. Of the various desalination processes, Reverse Osmosis (RO) stands out due to its energy requirement of 3–10 kWh/m³ and quality of fresh water. Thermal technologies like Multi-Stage Flashing (MSF) and Multi-Effect Distillation (MED) are also utilized but are usually more energy-intensive. The integration of renewable energy sources, including solar, has improved sustainability, with hybrid solar desalination systems minimizing the energy intake by up to 30 %. Recent progress in solar still technologies, such as ceramic magnets and black powder-coated crushed granite for heat storage, has increased productivity by 28 %, attained energy efficiencies of 30.5 %, and reduced payback periods to less than four months. This article overviews solar desalination processes solar thermal, photovoltaic-driven RO (PV-RO), and CSP hybrid systems focusing on technological developments like nanomaterial and advanced membranes. Comparative evaluation of these technologies against traditional technologies weighs technical viability, cost-effectiveness, scalability, and environmental considerations. Case studies from water-scarce locations inform practical experience and issues. The article concludes by reviewing future opportunities, emphasizing innovative technologies and facilitating policy regimes to ensure sustainable, energy-efficient desalination solutions worldwide.
Benjamin Neale, Sakdirat Kaewunruen, Dan Li
et al.
This study aims to define specific transferable engineering capabilities needed for the implementation (design and practices) of circular economy (CE) within a smart city setting. We conducted a critical literature review of over 100 studies on the core values of CE and smart cities to investigate the knowledge gap in this topic and understand what specific skillset is employed by industry experts that can be harnessed on a wider scale, which can allow for the optimization of CE. There is a lack of research on the skillsets needed to implement a circular economy in any setting, and there are very few studies on circularity practices in a smart city setting. Primary data collection allows us to bridge this knowledge gap, yielding new findings that do not already exist concerning the skillset employed by experts in the field, which can positively impact the smart city settings in which a circular economy is implemented. We conducted a qualitative analysis based on expert interviews of 21 participants who have experience in the circular economy. This information will benefit the industry by informing businesses and councils about the key skillsets and capabilities to look out for when employing people to implement any aspect of circular practices in a smart city setting, with an emphasis on enhancing efficiency, achieving deliverables, and thinking systemically to address complex challenges they may face during the implementation. We also investigated the implementation of CE in smart cities to provide a well-rounded view of the different achievements and challenges faced during the process. This mainly focuses on the work of governance in smart circular cities, a factor that has many important implications and externalities in different sectors. This study describes the methodology adopted to formulate a detailed questionnaire for expert interviews with respect to the skill gap and capabilities necessary for working in the industry, the results of which aid discussions regarding the different challenges faced in CE implementation. Our findings reveal that background knowledge in engineering and sustainability is the most ‘highly critical’ hard skill according to the experts, while communication and stakeholder engagement are the essential soft skills required to ensure the success of a circular economy within smart city settings.
ABSTRACT In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pathways, and possess substantial commercial value. Research indicates that some synthetic promoters have higher transcriptional activity compared to strong natural promoters. However, with the exponential increase in complexity due to the 4n potential combinations in a promoter sequence of length n, identifying effective synthetic promoters remains a formidable challenge. Deep learning models, by adaptively learning from extensive data sets, have become instrumental in analyzing biological data. This study introduces a diffusion model-based approach for designing promoters viable in model bacteria such as Escherichia coli and cyanobacteria. This model proficiently assimilates and utilizes inherent biological features from natural promoter sequences to engineer synthetic variants. Additionally, we employed a transformer model to evaluate the efficacy of these synthetic promoters, aiming at screening those with high performance. The experimental findings suggest that the synthetic promoters by the diffusion model not only share key biological features with their natural counterparts but also demonstrate greater similarity to natural promoters than those generated by a variational autoencoder. In predicting promoter strength, the transformer model demonstrated improved performance over the convolutional neural network. Finally, we developed an integrated platform for generating promoters and predicting their strength.IMPORTANCEWe demonstrated that diffusion models are superior in accomplishing the promoter synthesis task compared to other state-of-the-art deep learning models. The effectiveness of our method was validated using data sets of Escherichia coli and cyanobacteria promoters, showing more stable and prompt convergence and more natural-like promoters than the variational autoencoder model. We extracted sequence information, dimer information, and position information from promoters and combined them with a transformer model to predict promoter strength. Our prediction results were more accurate than those obtained with a convolutional neural network model. Our in silico experiments systematically introduced mutations in promoter sequences and explored their contribution to promoter strength, highlighting the depth of learning in our model.
Este trabalho apresenta uma nova base de dados com estimativas de despesas em educação e alguns indicadores de viés elitista nas políticas educacionais no Brasil entre 1933 e 2010. As estimativas de despesa pública em educação como proporção do Produto Interno Bruto (PIB) (total, educação básica e ensino superior) são semelhantes às apresentadas por Maduro (2007). A novidade do presente trabalho são as séries de estimativas de despesas por ciclo de ensino (EF1, EF2 e EM). A partir destas estimativas, apresenta-se também um indicador de viés elitista: a razão de despesas por estudante no primário (EF1) em relação ao gasto por aluno no ensino superior. Essas razões foram historicamente baixas, mas houve uma elevação abrupta a partir do final da década de 1960. Este aumento se deu mais pela expansão da matrícula no ensino superior do que por uma elevação de gastos por estudante no primário.
Economic theory. Demography, Economic history and conditions
Deddy Eduar Eka Saputra, Dewi Susita, Anis Eliyana
et al.
Amidst a scarcity of literature on the adoption of information technology among Generation X employees within the banking sector, this study aims to delve into the factors influencing their intention to use Performance Management Systems (PMS). Drawing upon the Technology Acceptance Model, this study endeavors to analyze the impact of self-efficacy, perceived usefulness, and attitude towards PMS on the intention to use such systems among Generation X bank employees. Employing a quantitative methodology, the study engaged 158 participants from a regional bank in Indonesia, selected through purposive sampling. Data gathered via online questionnaires underwent analysis using Partial Least Square-Structural Equation Modeling via SmartPLS 4.0 to evaluate ten research hypotheses. The findings revealed that while self-efficacy in PMS did not directly influence the intention to use PMS, perceived usefulness of PMS and attitude to PMS acted as mediators, facilitating the effect of self-efficacy on intention. These results can be attributed to the inherent skepticism and pragmatism of Generation X individuals towards novel technologies. Consequently, this study yields significant insights for both the advancement of technology acceptance literature and the strategic preparedness of banking institutions in adopting new technologies.
Предметом дослідження є процеси автоматизованого прогнозування показників проєктів із розроблення програмних систем, що зазвичай підлягають оцінюванню, а також методи й моделі штучного інтелекту, які можуть бути застосовані для генерації базових шаблонів дорожніх карт і післяопераційних переліків робіт та альтернативних оцінок залежно від контексту. Мета роботи – дослідження можливості впровадження та ефективності методів штучного інтелекту у створенні системи для автоматизованого прогнозування альтернативних оцінок програмного продукту. У статті розв’язуються такі завдання: визначення етапів, пов’язаних з оцінюванням альтернатив у життєвому циклі проєкту з розроблення програмного продукту; вивчення проблем прогнозування та основних факторів, що впливають на кінцеві показники; дослідження методів прогнозування, що можуть бути впроваджені для реалізації багатоваріантного оцінювання проєкту з розроблення програмного продукту. Стаття присвячена визначенню концептуальних засад створення систем автоматизованого оцінювання та прогнозування на підставі аналізу ефективності обраних моделей машинного навчання. Застосовуються такі методи: оцінювання та прогнозування трудовитрат у проєктах із розроблення програмного забезпечення, машинного та глибокого навчання й оцінювання їх ефективності для вирішення проблеми прогнозування. Досягнуті результати: визначено концептуальні засади створення систем автоматизованого оцінювання та прогнозування на підставі аналізу ефективності обраних моделей машинного навчання, сфери застосування методів штучного інтелекту в процесі оцінювання показників проєктів із розроблення програмного забезпечення; оцінено показники продуктивності різних моделей машинного навчання за певними параметрами оцінки моделі, які характеризують точність прогнозів; запропоновано концептуальну архітектуру програмного засобу генерації дорожніх карт проєкту з використанням мовної моделі GPT. Висновки: використання методів машинного та глибокого навчання може підвищити точність прогнозів основних показників проєкту, забезпечити можливість гнучкої генерації різних альтернативних варіантів шаблонів дорожніх карт і післяопераційних переліків робіт, що зробить процес планування та управління більш ефективним і прозорим за умови високого рівня невизначеності вимог до проєкту.
Massimo Arnone, Alberto Costantiello, Angelo Leogrande
et al.
This study analyses the relationship between non-performing loans (NPLs) and innovation systems at a global level. The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. Since k-means is an unsupervised machine-learning algorithm, we compared the Silhouette coefficient with the Elbow method to find an optimization. The results show that the optimal number of clusters is three, as suggested using the Elbow Method. Furthermore, a panel data analysis was conducted. Results show that the level of NPLs is positively associated with cultural and creative services exports as a percentage of total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, ICT services imports as a percentage of total trade, and information and communication technologies.
IntroductionHeating is one of the main factors leading to high energy consumption and serious carbon emissions in buildings. The clean heating system formed by the coupling of phase change building maintenance structure and solar heating system can improve the thermal storage density of the building maintenance structure, while reducing energy consumption in winter while maintaining a comfortable room temperature through stable energy security.MethodsTherefore, a phase change radiation terminal heating (PCRTH) system with the phase change radiation module as the terminal and the solar energy and air energy as the clean heat source is established in this study. Nanjing, Tianjin and Shenyang in China were selected as the study zones which correspond to the hot summer and cold winter zone, the cold zone and the severe cold zone respectively. The operational effect of the PCRTH system in different climate zones was studied, and the parameters of the PCRTH system were optimized by the GenOpt program combined with Hooke-Jeeves optimization algorithm.ResultsThe analysis results show that the cascade phase change radiation terminals in the three zones reduced room temperature fluctuation, energy consumption and carbon dioxide emissions, but the heating cost was higher. After the Hooke-Jeeves optimization algorithm was used to optimize the PCRTH system parameters in three zones, the PCRTH system heating cost was reduced, and the PCRTH system energy consumption and PCRTH system carbon dioxide emissions were further reduced.DiscussionTherefore, the building heating system composed of PCM maintenance structure and renewable energy has great application advantages in maintaining a comfortable room temperature and improving heating system energy conservation and environmental protection.
Producing high‐purity hydrogen from water electrocatalysis is essential for the flourishing hydrogen energy economy. It is of critical importance to develop low‐cost yet efficient electrocatalysts to overcome the high activation barriers during water electrocatalysis. Among the various approaches of catalyst preparation, corrosion engineering that employs the autogenous corrosion reactions to achieve electrocatalysts has emerged as a burgeoning strategy over the past few years. Benefiting from the advantages of simple synthesis, effective regulation, easy scale‐up production, and extremely low cost, corrosion engineering converts the harmful corrosion process into the useful catalyst preparation, achieving the goal of “transforming damage into benefit.” Herein, the concept of corrosion engineering, fundamental reaction mechanisms, and affecting factors are firstly introduced. Then, recent progresses on corrosion engineering for fabricating electrocatalysts toward water splitting are summarized and discussed. Specific attentions are devoted to the formation mechanisms, catalytic performances, and structure–activity relations of these catalysts as well as the approaches employed for performance improvements. At last, the current challenges and future exploiting directions are proposed for achieving highly active and durable electrocatalysts. It is envisioned to shed light on the multidisciplinary corrosion engineering that is closely associated with corrosion and material science for energy and environmental applications.
Diego Vergara, Pablo Fernández-Arias, J. Extremera
et al.
The rapid advance of Information and Communication Technology (ICT) in recent times and the current pandemic caused by COVID-19 have profoundly transformed society and the economy in most of the world. The education sector has benefited from this ICT-driven revolution, which has provided and expanded multiple new tools and teaching methods that did not exist just a few decades ago. In light of this technological change, virtual laboratories (VLs) based on the use of virtual reality (VR) have emerged, which are increasingly used to facilitate the teaching–learning process in a wide range of training activities, both academic and professional types. The set of advantages offered by this type of VL, the main of which are listed in this article, has made its use increasingly common as support for engineering classes at universities. This paper presents a study involving 420 engineering students from Spanish and Portuguese universities and associated analyses on the assessment of different parameters in various VLs designed by the authors. The results obtained indicate that, in general, VR-based VLs are widely accepted and demanded by students, who likewise consider real laboratories (RLs) necessary in face-to-face teaching. In the current post-COVID-19 educational scenario, VLs and RLs will coexist within the new hybrid models that combine face-to-face and online teaching and learning.
Alexander Einhaus, T. Baier, Marian Rosenstengel
et al.
Microalgal biotechnology promises sustainable light-driven production of valuable bioproducts and addresses urgent demands to attain a sustainable economy. However, to unfold its full potential as a platform for biotechnology, new and powerful tools for nuclear engineering need to be established. Chlamydomonas reinhardtii, the model for microalgal synthetic biology and genetic engineering has already been used to produce various bioproducts. Nevertheless, low transgene titers, the lack of potent expression elements, and sparse comparative evaluation prevents further development of C. reinhardtii as a biotechnological host. By systematically evaluating existing expression elements combined with rational promoter engineering, we established novel synthetic expression elements, improved the standardized application of synthetic biology tools, and unveiled an existing synergism between the PSAD 5' UTR and its corresponding chloroplast targeting peptide. Promoter engineering strategies, implemented in a newly designed synthetic algal promoter, increased the production of the sesquiterpene (E)-α-bisabolene by 18-fold compared to its native version and 4-fold to commonly used expression elements. Our results improve the application of synthetic biology in microalgae and display a significant step toward establishing C. reinhardtii as a sustainable green cell-factory.
The purpose of this article is to contribute to the research literature on entrepreneurial intention (EI) of engineering students in an emerging economy. Through the lens of Ajzen’s theory of planned behavior (TPB), we investigate the influence of academic curriculum, institutional support, family, and peer effects in fostering an intent to be self-employed among Indian engineering students. Data collected from 210 final-year engineering students were tested using partial least squares structural equation modeling method. The antecedents of the TPB, namely, attitude toward entrepreneurship (ATE), subjective norms (SN), and perceived behavioral control (PBC), demonstrated a favorable influence on EI. Whereas the regular academic curriculum seemed to have a negative influence on ATE, institutional support showed a positive effect. Both curriculum and academic support were found to have no significant impact on either PBC or SN. However, both family and peer influence revealed a strong positive relationship with all the antecedents of EI.
As atmospheric greenhouse gas concentrations continue to rise and the consequences of climate change become increasingly destructive, engineers, scientists, and the broader population are now aware that climate change is a threat we can no longer avoid. Addressing climate change requires facing two interconnected challenges. The first is to rapidly transition to carbon neutral—or even carbon negative—energy systems while reducing greenhouse gas emissions across all sectors of the economy. The second challenge is to minimize the impacts of climate change, particularly for the most vulnerable populations, by building sustainable and resilient systems. Addressing these challenges will require a fundamental reshaping of engineering education. The next generation of engineers and current practitioners will need new skills to serve society in this environment of uncertainty and rapid change. Empowering engineers with the skills to address the challenges created by climate change requires adapting both the technological and philosophical frameworks used in engineering education. In particular, engineers will need to (1) understand how climate and sustainability are linked to engineering design; (2) incorporate a wide range of disciplines into engineering solutions; (3) understand the ethics and justice dimensions of engineering; and (4) listen to and collaborate with diverse communities. Developing these skills requires reimagining engineering education and continuing professional development from student to senior practitioner.
Cell factories converting bio-based precursors to chemicals present an attractive avenue to a sustainable economy, yet screening of genetically diverse strain libraries to identify the best-performing whole-cell biocatalysts is a low-throughput endeavor. For this reason, transcriptional biosensors attract attention as they allow the screening of vast libraries when used in combination with fluorescence-activated cell sorting (FACS). However, broad ligand specificity of transcriptional regulators (TRs) often prohibits the development of such ultra-high-throughput screens. Here, we solve the structure of the TR LysG of Corynebacterium glutamicum, which detects all three basic amino acids. Based on this information, we follow a semi-rational engineering approach using a FACS-based screening/counterscreening strategy to generate an l-lysine insensitive LysG-based biosensor. This biosensor can be used to isolate l-histidine-producing strains by FACS, showing that TR engineering towards a more focused ligand spectrum can expand the scope of application of such metabolite sensors. Transcriptional biosensors represent powerful tools for the screening of vast strain libraries, but the broad ligand specificity of some transcriptional regulators (TRs) can prohibit such applications. Here authors present the engineering of a LysG-based biosensor with a focused ligand specificity to isolate L-histidine-producing strains.
Circular Economy (CE) has proved its contribution to addressing environmental impacts in the Architecture, Engineering, and Construction (AEC) industries. Building Circularity (BC) assessment methods have been developed to measure the circularity of building projects. However, there still exists ambiguity and inconsistency in these methods. Based on the reviewed literature, this study proposes a new framework for BC assessment, including a material flow model, a Material Passport (MP), and a BC calculation method. The material flow model redefines the concept of BC assessment, containing three circularity cycles and five indicators. The BC MP defines the data needed for the assessment, and the BC calculation method provides the equations for building circularity scoring. The proposed framework offers a comprehensive basis to support a coherent and consistent implementation of CE in the AEC industry.
Abstract The German Rhineland is home to the world's largest opencast lignite coal mine and human-made hole – the Hambach mine. Over the last seven years, RWE, the mine operator, has faced an increase in militant resistance, culminating in the occupation of the Hambacher Forest and acts of civil disobedience and sabotage. The mine provides a European case study to examine the repressive techniques deployed by RWE to legitimise coal mining in the face of a determined opposition. Drawing on political ecology literature and work on corporate counter-movements, this paper peers into extractive industries and their corporate social responsibility (CSR) engagements through the lens of corporate counterinsurgency. We first provide some background to the mine and RWE's unique position in the German political economy. After explaining the rise of resistance, the paper then discusses counterinsurgency in relation to CSR by outlining the different techniques used to win the ‘hearts’ and ‘minds’ of people around the mine. This includes securing the support of political leaders, lobbying, involvement in social events, infrastructure projects, astroturf groups and ecological restoration/offsetting work, which combine with overtly repressive techniques by public and private security forces that together attempt to legitimise the mine and stigmatise, intimidate and criminalise activists. This paper contents that counterinsurgency techniques are becoming normalised into the everyday operations of RWE, naturalising its image as ‘good corporate citizen’ and legitimising and invisibilising the violence towards (non)human nature inherent in the corporate-state-mining-complex, as mining is becoming part of the ‘green economy’ and made ‘sustainable’.