The relevance of scientific research is due to the fact that the digitalization of the economy today, as well as in the Soviet period, is the key to a country’s competitiveness at the international level. The analysis of the historical experience of production automation in the USSR allowed us to assess the potential, features, and vulnerabilities of this process, and to draw historical parallels with the present day. This article provides an example of the creation of an automatic control system at the Saratovneftegaz Association, highlighting the advantages, disadvantages, and prospects of the system.
Academia and industry each possess distinct advantages in advancing technological progress. Academia's core mission is to promote open dissemination of research results and drive disciplinary progress. The industry values knowledge appropriability and core competitiveness, yet actively engages in open practices like academic conferences and platform sharing, creating a knowledge strategy paradox. Highly novel and publicly accessible knowledge serves as the driving force behind technological advancement. However, it remains unclear whether industry or academia can produce more novel research outcomes. Some studies argue that academia tends to generate more novel ideas, while others suggest that industry researchers are more likely to drive breakthroughs. Previous studies have been limited by data sources and inconsistent measures of novelty. To address these gaps, this study conducts an analysis using four types of fine-grained knowledge entities (Method, Tool, Dataset, Metric), calculates semantic distances between entities within a unified semantic space to quantify novelty, and achieves comparability of novelty across different types of literature. Then, a regression model is constructed to analyze the differences in publication novelty between industry and academia. The results indicate that academia demonstrates higher novelty outputs, which is particularly evident in patents. At the entity level, both academia and industry emphasize method-driven advancements in papers, while industry holds a unique advantage in datasets. Additionally, academia-industry collaboration has a limited effect on enhancing the novelty of research papers, but it helps to enhance the novelty of patents. We release our data and associated codes at https://github.com/tinierZhao/entity_novelty.
Felister Dibia, Oghenovo Okpako, Jovana Radulovic
et al.
The increasing demand for hydrogen has made it a promising alternative for decarbonizing industries and reducing CO<sub>2</sub> emissions. Although mainly produced through the gray pathway, the integration of carbon capture and storage (CCS) reduces the CO<sub>2</sub> emissions. This study presents a sustainability method that uses flare gas for hydrogen production through steam methane reforming (SMR) with CCS, supported by a techno-economic analysis. Data Envelopment Analysis (DEA) was used to evaluate the oil company’s efficiency, and inverse DEA/sensitivity analysis identified maximum flare gas reduction, which was modeled in Aspen HYSYS V14. Subsequently, an economic evaluation was performed to determine the levelized cost of hydrogen (LCOH) and the cost–benefit ratio (CBR) for Nigeria. The CBR results were 2.15 (payback of 4.11 years with carbon credit) and 1.96 (payback of 4.55 years without carbon credit), indicating strong economic feasibility. These findings promote a practical approach for waste reduction, aiding Nigeria’s transition to a circular, low-carbon economy, and demonstrate a positive relationship between lean and green strategies in the petroleum sector.
Abstract Data reconciliation techniques have been the subject of many classic studies in the data conditioning process. By reconciling the measurements, accurate estimation of the system output and unmeasured variables is provided. However, accurately determining measurement noise and parameter uncertainty in real time remains a significant challenge. How to simultaneously estimate parameters in the system has been attracting considerable interest. So far, very little attention has been paid to time-varying parameter estimation in oil production systems. In particular, estimation of parameter dynamics and the corresponding uncertainty without prior knowledge remains challenging. This work extends a previous study on dynamic parameter estimation by considering scenarios where parameters change both gradually and abruptly. To address these dynamics, nonlinear filtering methods are employed and compared. A comparative analysis was conducted using both quantitative metrics and visualization plots to evaluate the performance of various approaches. Under the same abrupt parameter change scenario, nonlinear filter-based methods demonstrated superior performance in parameter estimation, achieving a root mean square error of $$6.56 \times 10^{-11}$$ , compared to $$7.84 \times 10^{-11}$$ for the MCMC-based method-even without the use of prior information. Additionally, nonlinear filters showed a significant advantage in simultaneous state estimation, with a root mean square error of $$1.94 \times 10^{4}$$ , markedly lower than the $$1.47 \times 10^{6}$$ observed with the MCMC-based approach. The effectiveness of nonlinear filtering methods was further validated in scenarios involving gradual parameter changes, again without relying on prior knowledge. This work provides an important opportunity to advance the understanding of dynamic parameter estimation in the gas and oil industry, and the improved model can possibly be applied to real-time optimization and model-based control. Graphical abstract
We report the formation of chiral quantum droplet in a spin-orbit coupled Bose gas, where the system turns to a self-bound droplet when moving towards a particular direction and remains gaseous otherwise. The chirality arises from the breaking of Galilean invariance by spin-orbit coupling, which enables the system to dynamically adjust its condensation momentum and spin polarization in response to its velocity. As a result, only towards a specific moving direction and beyond a critical velocity, the acquired spin polarization can trigger collective interactions sufficient for self-binding and drive a first-order transition from gas to droplet. We have mapped out a phase diagram of droplet, gas and their coexistence for realistic spin-orbit coupled 39K mixtures with tunable moving velocity and magnetic detuning. Our results have revealed the emergence of chirality in spin-orbit coupled quantum gases, which shed light on general chiral phenomena in moving systems with broken Galilean invariance.
The fashion industry is an extremely profitable market that generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories. This systematic literature review (SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion industry. Thus, investigating the impact of integrating both technologies to enhance the fashion industry. This systematic review uses the Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, including three essential phases: identification, evaluation, and reporting. In the identification phase, the target search problems are determined by selecting appropriate keywords and alternative synonyms. After that 578 documents from 2014 to the end of 2023 are retrieved. The evaluation phase applies three screening steps to assess papers and choose 118 eligible papers for full-text reading. Finally, the reporting phase thoroughly examines and synthesizes the 118 eligible papers to identify key themes associated with GAI and Metaverse in the fashion industry. Based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) analyses performed for both GAI and metaverse for the fashion industry, it is concluded that the integration of GAI and the metaverse holds the capacity to profoundly revolutionize the fashion sector, presenting chances for improved manufacturing, design, sales, and client experiences. Accordingly, the research proposes a new framework to integrate GAI and metaverse to enhance the fashion industry. The framework presents different use cases to promote the fashion industry using the integration. Future research points for achieving a successful integration are demonstrated.
Safi Ullah, Zia Ur Rehman, Cedric Mankponse Antoine Assogba
et al.
Abstract Background Brassica napus (rapeseed) is a globally significant oilseed crop known for its rich polyunsaturated fatty acid (PUFA) content, which is essential for human health and industrial applications. This study provides a comprehensive molecular and biochemical characterization of 45 advanced B. napus lines, using 11 simple sequence repeat (SSR) markers to explore genetic diversity and gas chromatography–mass spectrometry (GC–MS) to analyze fatty acid profiles. Methods and results The seeds, derived from F6 generation crossbreeding of local and Canadian varieties with low erucic acid content, were grown under controlled conditions. Genomic DNA was extracted using a standard CTAB method and analyzed with SSR primers to detect polymorphisms associated with fatty acid synthesis. This study identified BRMS-287 as a novel, highly prevalent marker, detected in 97% of the lines, and highlighted its potential linkage to desirable oil quality traits. Conversely, BRMS-056 showed the lowest average frequency (75%). Fatty acid profiling revealed significant variation in oleic, linoleic, and linolenic acid content, with 74% of lines meeting industry standards for low saturated fatty acids (< 7%) and desirable erucic acid levels (< 5%). Conclusions This research provides new insights into the genetic basis of fatty acid composition in B. napus, highlighting the potential of BRMS-287 marker for breeding programs aimed at enhancing oil quality. The findings suggest a path forward for developing B. napus lines with improved PUFA content and reduced undesirable fatty acids, which could have significant implications for health and industry.
Objective Hydrogen energy has drawn significant attention as the strategy of energy transition pushing forward, making it essential to establish reliable hydrogen transmission systems. For the construction of hydrogen service pipelines, it is vital to evaluate the risk of material failure due to hydrogen embrittlement in pipes. Hydrogen embrittlement occurs when hydrogen comes into contact with pipeline steel through a process consisting of six steps, among which hydrogen generation and adsorption lack of well-developed theories, leading to disparities among scholars in their understanding of the hydrogen adsorption mechanism. Therefore, studying the dissociative adsorption mechanism of hydrogen on pipeline steel is particularly crucial. Methods Focusing on hydrogen generation and adsorption, this paper presents a systematic review of the dissociative adsorption mechanism of hydrogen on pipeline steel. Lennard-Jones potential curves are incorporated to illustrate the interaction process between hydrogen and the iron surface. The dissociative adsorption modes of hydrogen on the iron surface were simulated and calculated leveraging thermodynamics and density functional theory. By analyzing orbital bonding and charge transfer, the dissociative adsorption mechanism of hydrogen on the iron surface was identified. This paper summarizes three influencing factors in the dissociative adsorption of hydrogen: the environment, the surface, and the hydrogen itself, while proposing corresponding methods to inhibit the dissociative adsorption of hydrogen. Results Hydrogen was found to be adsorbed on the surface of pipeline steel through activated dissociation into hydrogen atoms, which then enter the pipes. This process follows the primary mechanism in which orbital hybridization between H2 and Fe leads to the rupture of the H-H bonds and the subsequent formation of H-Fe bonds. Several factors were observed to influence the dissociative adsorption of hydrogen to varying degrees, including hydrogen concentration, hydrogen flow state, gas impurities, temperature, and the condition of the iron surface. Based on these findings, three methods were proposed to enhance hydrogen resistance: coating, corrosion films, and protective gas. All these methods aim to prevent hydrogen from coming into contact with pipeline steel and causing embrittlement from the perspective of surface adsorption, with the protective gas method identified as the most economical and convenient option. Conclusion This research clarifies the specific process of H2 dissociative adsorption on the surface of pipeline steel. Future research is recommended to explore the dissociative adsorption of hydrogen under multi-factor coupling conditions, to identify economical and effective hydrogen resistance options. These outcomes will establish a foundation for the integrity management of hydrogen service pipelines and ensure the safety of pipes in contact with hydrogen.
Dario Genzardi, Estefanía Núñez Carmona, Elisabetta Poeta
et al.
Incorporating insect meals into poultry diets has emerged as a sustainable alternative to conventional feed sources, offering nutritional, welfare benefits, and environmental advantages. This study aims to monitor and compare volatile compounds emitted from raw poultry carcasses and subsequently from cooked chicken pieces from animals fed with different diets, including the utilization of insect-based feed ingredients. Alongside the use of traditional analytical techniques, like solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), to explore the changes in VOC emissions, we investigate the potential of S3+ technology. This small device, which uses an array of six metal oxide semiconductor gas sensors (MOXs), can differentiate poultry products based on their volatile profiles. By testing MOX sensors in this context, we can develop a portable, cheap, rapid, non-invasive, and non-destructive method for assessing food quality and safety. Indeed, understanding changes in volatile compounds is crucial to assessing control measures in poultry production along the entire supply chain, from the field to the fork. Linear discriminant analysis (LDA) was applied using MOX sensor readings as predictor variables and different gas classes as target variables, successfully discriminating the various samples based on their total volatile profiles. By optimizing feed composition and monitoring volatile compounds, poultry producers can enhance both the sustainability and safety of poultry production systems, contributing to a more efficient and environmentally friendly poultry industry.
The lithofacies and thermal maturity of the over-mature Lower Cambrian marine shale in the Northern Guizhou Region, and their impacts on reservoir properties in this shale were analyzed by combining geochemistry, mineralogy, and gas adsorption methods. Ten lithofacies were identified, and the dominant lithofacies in the studied shale are lean-total organic carbon (TOC) argillaceous-rich siliceous shale (LTAS), medium-TOC siliceous shale (MTSS), and rich-TOC siliceous shale (RTSS). Since the gas generation potential of organic matter was weak, meso- and macro-pores were compressed or filled during the thermal evolution stage with a vitrinite reflectance (RO) range of 3.0%–4.0%. The controlling factors for methane adsorption capacity in the shale samples are significantly influenced by TOC content rather than thermal maturity. Among the RTSS, MTSS, and LTAS samples, RTSS exhibits the highest favorability for preserving hydrocarbon gas, followed by MTSS. The shale types in this study play a significant role in determining the properties of shale reservoirs, serving as an effective parameter for evaluating shale gas development potential. The RTSS and MTSS with a RO range of 2.0%–3.0% stand out as the most favorable target shale types for shale gas exploration and development.
The study of the filling veins in deep reservoirs within the strike-slip fault zone in the north of Fuman Oilfield utilizes a range of methods including petrographic characteristics, analysis of rare earth elements andSr（strontium） isotopes, fluorescence spectra of oil inclusions, microscopic thermodynamics, and U-Pb isotopic dating of carbonate rocks. The findings reveal two stages of calcite vein formation in this area. These veins originate from the formation water of the middle and Lower Ordovician sources, with no evidence of oxidizing fluid intrusion, suggesting that the deep to ultra-deep oil and gas reserves have maintained good sealing properties in later stages. Furthermore, based on the burial history deduced from inclusions and low U-Pb isotope dates from carbonate rocks, it has been determined that there are three distinct stages of hydrocarbon charging in the deep Ordovician strata of the northern strike-slip fault zone in the Tarim Basin. These stages correspond to （459±7.2） Ma（middle Caledonian）, （348±18） Ma（early Hercynian）, and 268 Ma（late Hercynian）. It is noted that the early Hercynian period was the key phase for hydrocarbon accumulation in the deep and ultra-deep carbonate rocks in the north of Fuman Oilfield, with a significant correlation observed between oil and gas charging and fault activity.
Petroleum refining. Petroleum products, Gas industry
K. V. Nagaraja, Sumanta Shagolshem, Bhavesh Kanabar
et al.
Abstract The present analysis examines the effect of thermal radiation on the stream of Maxwell liquid past a stretchy surface in the presence of a zero-mass flux condition. The effect of waste discharge concentration, thermophoresis-Brownian motion, and porous media on the fluid motion is also considered. Investigation of the liquid flow with waste discharge concentration in stretched sheets may lead to the growth of effective methods for disposing of waste and preventing pollution. Due to their extensive applicability, the mechanisms of flow and heat transmission in the context of thermal radiation play a crucial role in research and industry. This phenomenon often occurs in spacecraft, nuclear reactor cooling, power production, aerospace technologies, combustion applications, gas turbines, hypersonic flights, and high-temperature processes. The modelled governing partial differential equations (PDEs) are converted into dimensionless ordinary differential equations (ODEs) utilizing appropriate similarity variables. Further, the Keller–Box approach is utilized to solve the resultant ODEs numerically. The influence of several parameters on the temperature, concentration and velocity profiles is shown via graphic representations. The intensification in Deborah’s number and porous parameter values declines the velocity profile. As the values of the radiation, thermophoresis, and Brownian motion parameters increase, the thermal profile increases. The concentration profile increases as the pollutant external source parameter value upsurges.
In recent years, Shale gas has been the fastest-growing energy source in the world. In USA, shale gas now contributes to more than 60 % of natural gas supply. In China, annual shale gas production climbed to 800 Bcf (billion cubic feet) in 2021. However, drilling in shale has been a major challenge since the dawn of petroleum industry due to the reactive clay minerals.This paper surveys the field cases of drilling fluids in major shale plays. OBM (oil based mud), formulated with diesel and low fraction of water phase, provides effective shale stability, excellent lubricity, and high rate of penetration (ROP). As a result, more than 70 % of shale gas wells have been drilled with OBM with very few reported cases of wellbore instability. WBM (water-based mud) is made of water and necessary chemical additives. WBM is less costly and more environment-friendly than OBM, however some shale wells drilled with WBM reported severe instability issues. Nevertheless, recent innovations in WBM lead to successes in drilling major shale plays. WBM has great potential in shale drilling and deserves more research and improvements.
In the last century the automotive industry has arguably transformed society, being one of the most complex, sophisticated and technologically advanced industries, with innovations ranging from hybrid, electric and self-driving smart cars to the development of IoT-connected cars. Due to its complexity, it requires the involvement of many Industry 4.0 technologies, like robotics, advanced manufacturing systems, cyber-physical systems or augmented reality. One of the latest technologies that can benefit the automotive industry is blockchain, which can enhance its data security, privacy, anonymity, traceability, accountability, integrity, robustness, transparency, trustworthiness and authentication, as well as provide long-term sustainability and a higher operational efficiency to the whole industry. This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. Thus, the applicability of blockchain is evaluated after examining the state-of-the-art and devising the main stakeholders' current challenges. Furthermore, the article describes the most relevant use cases, since the broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications that can create new business models and even disrupt the car-sharing economy as we know it. Finally, after a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments.
Thermodynamic functions of the ideal Fermi gas at arbitrary temperatures are calculated using the standard Fermi-Stoner functions. The properties of the Fermi-Stoner functions are analyzed. The limiting cases of low-temperature and classical limits with taking into account quantum corrections and the special case of zero chemical potential are considered.
Background: Polymer flooding is a well-known enhanced oil recovery technique which can increase recovery factors in mature oilfields above 10% of the oil originally in place. Despite a lengthy history and many published field cases, the speed of deployment is still rather slow. With the need to boost energy production while minimizing energy wastes and carbon emissions, considering this technique known to reduce water usage and accelerate oil recovery should be a must. Aim: This short publication aims at providing guidelines to accelerate deployment of polymer injection in various oilfields and a couple of pragmatic approaches recognizing the need for field data instead of poorly constrained simulations or incomplete laboratory studies. Materials and methods: After a brief review of the technique and current implementation workflows, we will discuss new approaches to foster the deployment of injection pilots by showing how polymer injection can reduce emissions and energy wastes while accelerating oil production. Results: We provide a different perspective on polymer injection with pragmatic tools and ideas showing that going to the field fast provides more information than any laboratory study. Conclusion: Given the current need for mitigating oil production declines, polymer flooding is a technique of choice which can be deployed fast if basic criteria explained in this paper are met.
A large amount of fracturing fluid in hydraulic fracturing is imbibed into the shale fracture/matrix, which leads to significant uncertainty in gas recovery evaluation. The mechanism of imbibition impact on the gas–water flow is not well understood. In this study, systematic comparative experiments are carried out to simulate imbibition in fractured shale samples obtained from the Wufeng-Longmaxi shale reservoirs in China, and the imbibition effect in the fracture–matrix system is qualitatively and quantitatively investigated. Nine cores are collected to measure their porosity and permeability using a helium porosimeter and nitrogen pulse–decay tests. Gas/liquid single-phase flow experiments are then carried out using methane and KCl solution, respectively. Subsequently, dynamic imbibition experiments are carried out on three samples in a visual cell. The gas–water interfacial tension, water imbibition amount, and displacement velocity are recorded. A single-phase gas/liquid flow test shows a high linear correlation between the fluid displacement velocity and pressure gradient in the fractured samples as the fracture is the main flow channel, dominantly determining the flow behavior. Moreover, the capillary force was introduced in the cross-flow term of the triple-medium model to characterize the imbibition effect, and a two-phase flow simulation model considering the fracturing fluid imbibition retention was developed, and the two-phase flow behavior by considering the imbibition effect of the fracturing fluid retention in the shale gas reservoir was analyzed. Valuable experiment data in this work are provided, which can be used to validate analytical equations for gas/water flow in the shale fracture–matrix system.
Risk assessment across industries is paramount for ensuring a robust and sustainable economy. While previous studies have relied heavily on official statistics for their accuracy, they often lag behind real-time developments. Addressing this gap, our research endeavors to integrate market microstructure theory with AI technologies to refine industry risk predictions. This paper presents an approach to analyzing industry trends leveraging real-time stock market data and generative small language models (SLMs). By enhancing the timeliness of risk assessments and delving into the influence of non-traditional factors such as market sentiment and investor behavior, we strive to develop a more holistic and dynamic risk assessment model. One of the key challenges lies in the inherent noise in raw data, which can compromise the precision of statistical analyses. Moreover, textual data about industry analysis necessitates a deeper understanding facilitated by pre-trained language models. To tackle these issues, we propose a dual-pronged approach to industry trend analysis: explicit and implicit analysis. For explicit analysis, we employ a hierarchical data analysis methodology that spans the industry and individual listed company levels. This strategic breakdown helps mitigate the impact of data noise, ensuring a more accurate portrayal of industry dynamics. In parallel, we introduce implicit analysis, where we pre-train an SML to interpret industry trends within the context of current news events. This approach leverages the extensive knowledge embedded in the pre-training corpus, enabling a nuanced understanding of industry trends and their underlying drivers. Experimental results based on our proposed methodology demonstrate its effectiveness in delivering robust industry trend analyses, underscoring its potential to revolutionize risk assessment practices across industries.
We calculate the superfluid fraction of an interacting Fermi gas, in the presence of a one-dimensional periodic potential of strength $V_0$ and wave-vector $q$. Special focus is given to the unitary Fermi gas, characterized by the divergent behavior of the s-wave scattering length. Comparison with the Leggett's upper bound $(\langle n_{1D}\rangle <1/n_{1D}>)^{-1}$, with $n_{1D}$ the 1D column density, explicitly shows that, differently from the case of a dilute interacting Bose gas, the bound significantly overestimates the value of the superfluid fraction, except in the phonon regime of small $q$. Sum rule arguments show that the combined knowledge of the Leggett bound and of the actual value of the superfluid fraction allows for the determination of curvature effects providing the deviation of the dispersion of the Anderson-Bogoliubov mode from the linear phonon dependence. The comparison with the predictions of the weakly interacting BCS Fermi gas points out the crucial role of two-body interactions. The implications of our predictions on the anisotropic behavior of the sound velocity are also discussed.