In view of the difficulties in the geological engineering dessert of coal-rock gas development in Jiaxian block of Ordos Basin, such as unclear fine evaluation, difficult fine control of drilling trajectory of long horizontal section horizontal well, small space for liquid control, efficiency improvement and cost reduction in large-scale fracturing transformation, and inaccurate control of efficient and economical drainage, the development concept of geological engineering integration is adhered to. Through the application practice of development pilot test, the key technology of integrated and efficient development of coal-rock gas with dessert evaluation, optimal and fast drilling, fracturing transformation and efficient drainage is formed. Based on the integrated dessert evaluation technology, a three-factor and 12-item evaluation index system of geological engineering economy in the development dessert area is established. Based on the mud logging data while drilling, the identification standard of black gold target is established to support the development of selected areas and the deployment and implementation of horizontal wells. By optimizing the wellbore structure, optimizing the high-efficiency speed-up tools, and adopting the integrated guidance technology of seismic geology and engineering, the optimal and fast drilling of large well cluster horizontal wells in factory is realized, and the drilling rate of coal rock is guaranteed to be more than 98 %. The integrated fracturing technology system of geological engineering with ‘high displacement + moderate scale + complex fracture network + multi-scale support’ as the core is formed, and the ultra-low pre-liquid controlled hydraulic fracturing technology and few cluster long fracture fracturing technology are explored, which further improves the pertinence and economy of coal rock gas fracturing transformation. A full-cycle integrated drainage technology system of ‘initial oil control pressure self-flowing, medium-term auxiliary bubble drainage + gas lift, and later artificial lifting’ has been formed to improve the drainage and gas recovery efficiency of coal-rock gas wells. Practice shows that the integrated and efficient development technology of coal, rock and gas has realized the deep integration of geological engineering, formed the closed-loop optimization of the whole development cycle, reduced the development cost and realized the scale benefit development of coal, rock and gas while ensuring the drilling effect and production effect of single well.
Petroleum refining. Petroleum products, Gas industry
Jana Budimir-Marjanovic, Sherwan Yassin Hammad, Shokhista Turdalieva
et al.
Modern agriculture faces increasing pressure to maintain productivity while reducing soil degradation, chemical inputs, and ecological footprint, making biologically based soil-improvement strategies highly relevant. This study examined whether microbial inoculation, combined with conservation tillage practices (loosening and no-tillage), can enhance soil physical quality during pea (<i>Pisum sativum</i>) cultivation in an agroecological market garden in Hungary. A 2 × 2 factorial field experiment was established, testing tillage (loosening vs. no-tillage) and microbial inoculation (with vs. without) in a randomized design with three replications per treatment (12 plots total). A single microbial application was performed prior to planting using a consortium of <i>Rhizobium</i> spp., <i>Ensifer</i> spp., <i>Pseudomonas</i> spp., and <i>Bacillus</i> spp. The research focused on (I) soil penetration resistance, (II) soil moisture dynamics, and (III) infiltration capacity, with most parameters measured before and after planting. Microbial inoculation significantly reduced penetration resistance under both tillage systems and influenced soil moisture behavior, indicating improved soil structure and water retention. Infiltration rate did not change significantly within the study period. Overall, the results demonstrate that microbial amendments can rapidly improve key soil physical properties, offering a practical, nature-based strategy for resilient, low-input farming systems.
Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks, such as financial operations, administrative functions, and beyond. While cryptocurrencies could serve as the foundation for monetizing value exchange in a collaboration and delegation dynamic among AI agents, a critical question remains: how can humans ensure meaningful oversight and control as a future economy of AI agents scales and evolves? In this philosophical exploration, we highlight emerging concepts in the industry to inform research and development efforts in anticipation of a future decentralized agentic economy.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpreting complex multi-view drawings with dense annotations remains challenging using manual methods, generic optical character recognition (OCR) systems, or traditional deep learning approaches, due to varied layouts, orientations, and mixed symbolic-textual content. To address these challenges, this paper proposes a three-stage hybrid framework for the automated interpretation of 2D multi-view engineering drawings using modern detection and vision language models (VLMs). In the first stage, YOLOv11-det performs layout segmentation to localize key regions such as views, title blocks, and notes. The second stage uses YOLOv11-obb for orientation-aware, fine-grained detection of annotations, including measures, GD&T symbols, and surface roughness indicators. The third stage employs two Donut-based, OCR-free VLMs for semantic content parsing: the Alphabetical VLM extracts textual and categorical information from title blocks and notes, while the Numerical VLM interprets quantitative data such as measures, GD&T frames, and surface roughness. Two specialized datasets were developed to ensure robustness and generalization: 1,000 drawings for layout detection and 1,406 for annotation-level training. The Alphabetical VLM achieved an overall F1 score of 0.672, while the Numerical VLM reached 0.963, demonstrating strong performance in textual and quantitative interpretation, respectively. The unified JSON output enables seamless integration with CAD and manufacturing databases, providing a scalable solution for intelligent engineering drawing analysis.
Johan Cederbladh, Loek Cleophas, Eduard Kamburjan
et al.
With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.
This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.
Bryce Morsky, Tyler Meadows, Felicia Magpantay
et al.
The gig economy has grown significantly in recent years, driven by the emergence of various facilitating platforms. Triggering substantial shifts to labour markets across the world, the COVID-19 pandemic has accelerated this growth. To understand the crucial role of such an epidemic on the dynamics of labour markets of both formal and gig economies, we develop and investigate a model that couples disease transmission and a search and match framework of unemployment. We find that epidemics increase gig economy employment at the expense of formal economy employment, and can increase the total long term unemployment. In the short run, large sharp fluctuations in labour market tightness and unemployment can occur, while in the long run, employment is reduced under an endemic disease equilibrium. We analyze a public policies that increase unemployment benefits or provide benefits to gig workers to mitigate these effects, and evaluate their trade-offs in mitigating disease burden and labour market disruptions.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is slow and labor-intensive, while traditional Optical Character Recognition (OCR) techniques often struggle with complex layouts and overlapping symbols, resulting in unstructured outputs. To address these challenges, this paper proposes a novel hybrid deep learning framework for structured information extraction by integrating an Oriented Bounding Box (OBB) detection model with a transformer-based document parsing model (Donut). An in-house annotated dataset is used to train YOLOv11 for detecting nine key categories: Geometric Dimensioning and Tolerancing (GD&T), General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. Detected OBBs are cropped into images and labeled to fine-tune Donut for structured JSON output. Fine-tuning strategies include a single model trained across all categories and category-specific models. Results show that the single model consistently outperforms category-specific ones across all evaluation metrics, achieving higher precision (94.77% for GD&T), recall (100% for most categories), and F1 score (97.3%), while reducing hallucinations (5.23%). The proposed framework improves accuracy, reduces manual effort, and supports scalable deployment in precision-driven industries.
Psidium guajava L. leaves contain various bioactive components have been utilized in traditional medicine for the treatment of diseases. A novel acidic polysaccharide (GLP) was extracted and purified from Psidium guajava L. leaves, an underutilized by-product of guava processing. GLP. The molecular weight of GLP was found to be 1.723 × 103 kDa and mainly consists of arabinose, galactose, galacturonic acid and rhamnose using high performance liquid chromatography, ion chromatography, methylation analysis and nuclear magnetic resonance techniques. Key glycosidic linkages such as →3)-β-D-Galp-(1→, →3,4)-β-D-GalpA-(1→, and →3)-α-L-Araf-(1 → . were identified. In a retinoic acid-induced mouse model of osteoporosis, GLP administration significantly improved trabecular bone microarchitecture by increasing bone volume fraction, trabecular number, and thickness, while decreasing trabecular separation. Additionally, GLP markedly reduced serum levels of bone turnover markers (ALP, OC, TRACP) and pro-inflammatory cytokines (IL-6, IL-2, TNF-α). Furthermore, GLP modulated gut microbiota composition, enhancing the relative abundance of beneficial genera such as Lachnospira, Oscillospiraceae, and Dubosiella. These findings suggest that GLP not only modulates bone metabolism and inflammation but also may serve as an innovative natural therapeutic agent or functional food ingredient for the prevention and management of osteoporosis, highlighting the potential of valorizing guava leaf by-products in industrial applications.
Agriculture (General), Nutrition. Foods and food supply
The development of the Metaverse will depend on the rapid development of the exabyte economy and a new technical and technological impetus to improve the tools for the functioning of the gig economy. The article substantiates the existing products, services and national projects of the Metaverse in the context of the accelerated formation of the exabyte economy. It is noted that the exabyte economy, by improving its throughput, lays an innovative and digital basis for the development of the Metaverse ecosystem and the optimization of business processes through the digitalization of business models of entrepreneurship. The connection of the Metaverse with the exabyte economy and the gig economy is analyzed through the prism of digitalization and breakthrough innovations. The article reveals that the technologies that are actively developing the exabyte economy are AI, IoT, Big Data, and blockchain technology. The structural components of the Metaverse engineering in the context of the digital transformation of the global world-system are presented. It’s indicated that the institutional, technical and technological components and the crypto ecosystem are key drivers of the progressive development of the Metaverse using virtual, augmented and augmented reality, the application of smart contracts, and patentability for virtual goods. It’s noted that a global, institutionalized, interacting network of 3D virtual worlds is demonstrated in real time. An attempt has been made to reveal the characteristic features, types, and models of the Metaverse in the conditions of the development of the gig economy, including the currently existing ones: 2 types of Metaverses; 3 models of ownership of the Metaverse. The goods produced by digitized businesses in physical space have every chance of becoming tools for the progressive development of the virtual environment of the Metaverse. Scientists are of the opinion that it was the technological wave of the last 5 years that gave rise to a number of breakthrough innovations and high technologies, which allowed forming the basis of the exabyte economy and the gig economy.
Andrea Molina-Cortés, Fabian Tobar-Tosse, Mauricio Quimbaya
et al.
Abstract A crucial step in the engineering of bioactive materials from sugarcane by-products is understanding their physical, chemical, and biological characteristics, particularly their molecular composition and biological activities. This study aimed to characterize the physicochemical properties of methanolic and aqueous extracts from sugarcane molasses and vinasses, determine their antioxidant capacity, and identify key compounds of biological interest; specifically phenolic compounds (PCs) and heat-induced compounds (HICs). Through non-targeted analytical approaches, we identified a diverse range of PCs and HICs in the extracts. In vitro tests revealed significant antioxidant effects in both aqueous and methanolic fractions, with the methanolic extracts showing superior free radical scavenging capacity. This bioactivity was linked to PCs such as p-coumaric acid, 4-hydroxybenzoic acid, 4-hydroxybenzaldehyde, chlorogenic acid, and schaftoside, as well as HICs like 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP); 4-hydroxy-2,5-dimethyl-3(2H)-furanone (HDMF); 2,6-dimethoxyphenol; and 1,6-anhydro-β-D-glucopyranose. These findings underscore the potential of sugarcane molasses and vinasses as sources of bioactive compounds, which can be engineered into new materials with promising biological properties for health, pharmacological, and food industry applications. Furthermore, our research highlights the integration of bioengineering, material science, and sustainable practices within the sugarcane industry by promoting the valorization of by-products, contributing to resource efficiency and industrial innovation under circular economy principles.
Barbara Uliasz-Misiak, Jacek Misiak, Radosław Tarkowski
This article presents the findings of a bibliometric analysis of scientific publications in journals and materials indexed in the SCOPUS and Web of Science databases, covering the broad topic of underground hydrogen storage (UHS). The use of VOSviewer software for keyword analysis enabled the identification of four key research areas related to UHS. These areas include hydrogen and hydrocarbon reservoir engineering; hydrogen economy and energy transformation; processes in hydrogen storage sites, including lessons from CO<sub>2</sub> sequestration; and the geology, engineering, and geomechanics of underground gas storage. The interdisciplinary nature of UHS research emphasises the synergy of research across diverse fields. A bibliographic analysis allowed for the identification of areas of intensive research and new directions of work related to UHS, key research centres, and the dynamics of the development of research topics related to UHS. This study revealed the chronological dispersion of the research results, their geographical and institutional variability, and the varying contributions of major publishing journals. The research methodology used can serve as an inspiration for the work of other researchers.
Modern systems are increasingly connected and more integrated with other existing systems, giving rise to \textit{systems-of-systems} (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within Software Engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The \textit{International Workshop on Software Engineering for Systems-of-Systems} (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in Software Engineering for SoS.
Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier
et al.
Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.
The rapid development of the digital economy has had a profound impact on the implementation of the rural revitalization strategy. Based on this, this study takes Hainan Province as the research object to deeply explore the impact of digital economic development on rural revitalization. The study collected panel data from 2003 to 2022 to construct an evaluation index system for the digital economy and rural revitalization and used panel regression analysis and other methods to explore the promotion effect of the digital economy on rural revitalization. Research results show that the digital economy has a significant positive impact on rural revitalization, and this impact increases as the level of fiscal expenditure increases. The issuance of digital RMB has further exerted a regulatory effect and promoted the development of the digital economy and the process of rural revitalization. At the same time, the establishment of the Hainan Free Trade Port has also played a positive role in promoting the development of the digital economy and rural revitalization. In the prediction of the optimal strategy for rural revitalization based on the development levels of the primary, secondary, and tertiary industries (Rate1, Rate2, and Rate3), it was found that rate1 can encourage Hainan Province to implement digital economic innovation, encourage rate3 to implement promotion behaviors, and increase rate2 can At the level of sustainable development when rate3 promotes rate2's digital economic innovation behavior, it can standardize rate2's production behavior to the greatest extent, accelerate the faster application of the digital economy to the rural revitalization industry, and promote the technological advancement of enterprises.
Fareena Naz, Muhammad Fahim, Adnan Ahmad Cheema
et al.
Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: 1) root mean square error (RMSE), 2) mean absolute error (MAE), and 3) R-squared (R2). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R2) and the lowest error scores for both RMSE and MAE.
Customer satisfaction (CST) is a critical focus for both public and private organizations due to its significant impact on business success. In this context, ethical sales behavior, perceived trust, customer loyalty, and green experience are identified as key factors influencing CST. This study aims to examine the relationships among these variables within public sector organizations in the UAE. Primary data were collected from 224 respondents using a structured questionnaire. The data analysis was conducted utilizing descriptive statistics, multivariate assumptions, and a two-step approach that included both measurement and structural models. Structural equation modeling techniques were employed to test the relationships between the variables, grounded in the Resource-Based View (RBV) and expectation confirmation theory. The findings reveal that perceived trust, customer loyalty, and green experience significantly and positively impact customer satisfaction among public firms in the UAE. However, ethical sales behavior did not demonstrate a significant direct effect on customer satisfaction. These results provide valuable insights for policymakers and administrative representatives in the UAE public sector. To enhance customer satisfaction, it is recommended that public sector organizations prioritize building trust and loyalty while integrating green practices into their operations. Although ethical sales behavior does not directly affect customer satisfaction, it remains crucial for influencing trust and loyalty. The study underscores the importance of these factors in driving customer satisfaction and offers practical recommendations for public sector organizations aiming to improve their service quality and customer relations.
Background: Sustainable software engineering (SSE) means creating software in a way that meets present needs without undermining our collective capacity to meet our future needs. It is typically conceptualized as several intersecting dimensions or ``pillars'' -- environmental, social, economic, technical and individual. However; these pillars are theoretically underdeveloped and require refinement. Objectives: The objective of this paper is to generate a better theory of SSE. Method: First, a scoping review was conducted to understand the state of research on SSE and identify existing models thereof. Next, a meta-synthesis of qualitative research on SSE was conducted to critique and improve the existing models identified. Results: 961 potentially relevant articles were extracted from five article databases. These articles were de-duplicated and then screened independently by two screeners, leaving 243 articles to examine. Of these, 109 were non-empirical, the most common empirical method was systematic review, and no randomized controlled experiments were found. Most papers focus on ecological sustainability (158) and the sustainability of software products (148) rather than processes. A meta-synthesis of 36 qualitative studies produced several key propositions, most notably, that sustainability is stratified (has different meanings at different levels of abstraction) and multisystemic (emerges from interactions among multiple social, technical, and sociotechnical systems). Conclusion: The academic literature on SSE is surprisingly non-empirical. More empirical evaluations of specific sustainability interventions are needed. The sustainability of software development products and processes should be conceptualized as multisystemic and stratified, and assessed accordingly.
Does the national innovation city and smart city pilot policy, as an important institutional design to promote the transformation of old and new dynamics, have an important impact on the digital economy? What are the intrinsic mechanisms? Based on the theoretical analysis of whether smart city and national innovation city policies promote urban digital economy, this paper constructs a multi-temporal double difference model based on a quasi-natural experiment with urban dual pilot policies and systematically investigates the impact of dual pilot policies on the development of digital economy. It is found that both smart cities and national innovation cities can promote the development of digital economy, while there is a synergistic effect between the policies. The mechanism test shows that the smart city construction and national innovation city construction mainly affect the digital economy through talent agglomeration effect, technology agglomeration effect and financial agglomeration effect.