Developments In Field Research Articles

Increased cardiac macrophages in Sorbs2-deficient hearts: revealing a potential role for macrophage in responding to embryonic myocardial abnormalities

Macrophages are known to support cardiac development and homeostasis, contributing to tissue remodeling and repair in the adult heart. However, it remains unclear whether embryonic macrophages also respond to abnormalities in the developing heart. Previously, we reported that the structural protein Sorbs2 promotes the development of the second heart field, with its deficiency resulting in atrial septal defects (ASD). In analyzing RNA-seq data, we noted an upregulation of macrophage-related genes in Sorbs2−/− hearts. Immunostaining and lineage-tracing confirmed an increase in macrophage numbers, underscoring a macrophage response to myocardial abnormalities. Partial depletion of macrophages led to downregulation of genes involved in lipid metabolism, muscle development and organ regeneration, alongside upregulation of genes associated with DNA damage-induced senescence and cardiomyopathy. Additionally, a non-significant increase in septal defects in macrophage-depleted Sorbs2−/− hearts suggests a potential reparative function for macrophages in maintaining structural integrity. Valve formation, however, remained unaffected. Our findings suggest that embryonic macrophages might sense abnormalities in embryonic cardiomyocytes and could adaptively support cardiac structure and function development in response to myocardial abnormalities.

Graphene-Based, Flexible, Wearable Piezoresistive Sensors with High Sensitivity for Tiny Pressure Detection

Flexible, wearable, piezoresistive sensors have significant potential for applications in wearable electronics and electronic skin fields due to their simple structure and durability. Highly sensitive, flexible, piezoresistive sensors with the ability to monitor laryngeal articulatory vibration supply a new, more comfortable and versatile way to aid communication for people with speech disorders. Here, we present a piezoresistive sensor with a novel microstructure that combines insulating and conductive properties. The microstructure has insulating polystyrene (PS) microspheres sandwiched between a graphene oxide (GO) film and a metallic nanocopper-graphene oxide (n-Cu/GO) film. The piezoresistive performance of the sensor can be modulated by controlling the size of the PS microspheres and doping degree of the copper nanoparticles. The sensor demonstrates a high sensitivity of 232.5 kPa−1 in a low-pressure range of 0 to 0.2 kPa, with a fast response of 45 ms and a recovery time of 36 ms, while also exhibiting excellent stability. The piezoresistive performance converts subtle laryngeal articulatory vibration into a stable, regular electrical signal; in addition, there is excellent real-time monitoring capability of human joint movements. This work provides a new idea for the development of wearable electronic devices, healthcare, and other fields.

Assessment of Anthropogenic Disturbances of Landscapes: West Kazakhstan Region

The study analyzes anthropogenic disturbances of landscapes in Western Kazakhstan, which occupies 27% of the country’s territory. The main focus is on the impact of industry and agriculture, especially pasture use and the development of oil and gas fields. The application of remote sensing data and field surveys allowed us to identify the degree of landscape disturbance and to propose their classification into five levels of disturbance, from virtually undisturbed to severely disturbed. Cartographic analysis revealed that pastures occupy 53.83% of the territory, while industrial-technogenic impact accounts for 23.12%. This indicates a significant level of landscape transformation. The findings of this study can serve as a foundation for environmental monitoring and the formulation of recommendations aimed at reducing anthropogenic impacts. The study underscores the necessity for the sustainable management of natural resources in the context of industrial development in the region and provides crucial insights for maintaining ecological balance.

Slax: a composable JAX library for rapid and flexible prototyping of spiking neural networks

Abstract Spiking Neural Networks (SNNs) offer rich temporal dynamics and unique capabilities, but their training presents challenges. While backpropagation through time (BPTT) with surrogate gradients is the defacto standard for training SNNs, it scales poorly with long time sequences. Alternative learning rules and algorithms could help further develop models and systems across the spectrum of performance, bio-plausibility, and complexity. However, implementing and evaluating these alternatives at scale is cumbersome and error-prone, requiring repeated reimplementations. To address this, we introduce Slax, a JAX-based library designed to accelerate SNN algorithm design and evaluation. Slax is compatible with the broader JAX and Flax ecosystem and provides optimized implementations of diverse training algorithms, enabling direct performance comparisons. Its toolkit includes methods to visualize and debug algorithms through loss landscapes, gradient similarities, and other metrics of model behavior during training. By streamlining the implementation and evaluation of novel SNN learning algorithms, Slax aims to facilitate research and development in this promising field.

A conceptual model for multivariate data integration in reservoir modeling: exploring the role of neural networks in petrophysical analysis

Reservoir modeling is crucial for optimizing oil and gas production, as it helps characterize subsurface properties like porosity, permeability, and fluid saturation. Integrating diverse data sources such as well logs, seismic data, and core analyses presents a significant challenge due to their differing scales, resolutions, and data types. Traditional methods often struggle to accurately integrate these data sources, resulting in suboptimal predictive accuracy. This review proposes a conceptual model that leverages neural networks to integrate multivariate data for enhanced reservoir characterization. Neural networks, with their ability to handle nonlinear relationships and large, complex datasets, offer a transformative approach to unify diverse data inputs, resulting in more accurate predictions of reservoir properties. The model focuses on using feedforward neural networks (FNNs), convolutional neural networks (CNNs), and autoencoders to merge well logs, seismic interpretations, and core sample data. This integration aims to improve the identification of productive zones, reservoir boundaries, and fluid distributions, thereby enhancing reservoir models. Additionally, the review explores how neural networks can manage data gaps, predict missing values, and quantify uncertainty, which are common challenges in reservoir studies. By automating data processing and facilitating real-time analysis, neural networks can accelerate decision-making in reservoir management and field development. This framework highlights the potential of AI-driven techniques to revolutionize reservoir modeling, offering a pathway to more efficient, accurate, and adaptive resource management. The conceptual model aims to advance predictive capabilities in reservoir analysis, addressing the complexities of multivariate data integration and opening doors for future innovations in the energy industry. Keywords: Conceptual Model, Multivariate Data, Reservoir Modeling, Petrophysical Analysis.

Exploring the Current State of Information Literacy Education: A Systematic Literature Review

Information Literacy Education is a skill taught to students of various educational levels, from elementary school to college and even postgraduate programs. This research aims to present the current state of Information Literacy Education, identify the dominant research themes, the methods of teaching and learning used, the context of learning in which the skill is taught, and recommends future research development in related fields. A systematic literature review was conducted using large electronic journal databases such as Taylor and Francis, and Science Direct, which obtained 37,633 articles. Furthermore, The PICOC and PRISMA protocols were applied to analyze 17 relevant articles and identify 14 themes in learning, 14 learning methods, 12 research methods used, and 7 scientific fields in the implementation of Information Literacy Learning from 2017 to 2021. These results provide direction for future research and contribute to enhanced information literacy curriculum development, effective integration of information literacy in specific disciplines, transitioning to online information literacy instruction, development of engaging and interactive information literacy modules, assessing learning outcomes and informing instructional strategies, collaboration and professional development in information literacy, diverse and effective teaching methods, and advancements in research methods for information literacy.

Facile Synthesis of Monodisperse Gold Nanorods, Gold Nanobipyramids and Gold Nanocups with Different Coatings and Evaluation of Their Cellular Cytotoxicity.

Assessing the cytotoxicity of gold nanoparticles (GNPs) has gained importance due to their development in the biomedical field. In this study, we systematically synthesized gold nanorods (GNRs), gold nanobipyramids (GNBPs), and gold nanocups (GNCs) using a seed-mediated method, with an average length of 32.53 ± 4.67 nm, 72.90 ± 7.54 nm and 118.01 ± 11.02 nm, respectively. Furthermore, using the cell counting kit-8 (CCK-8) assay, we assessed the cellular cytotoxicity of three different types of GNPs with various different surface coatings, such as organic cetyltrimethylammonium bromide (CTAB) and polyethylene glycol (PEG). The results showed that the cytotoxic behavior of GNPs was shape-dependent in the concentration range of 3.125 -100 μg/mL. The types of GNPs and their surface coating had a significant impact on how the GNPs behaved in cells. Compared to PEG-coated GNPs, which do not induce cell injury, CTAB-coated GNPs show more noticeable cytotoxicity. Furthermore, compared to GNCs, the toxicity of GNRs and GNBPs against GES-1 cells, RAW 264.7 cells and LX-2 cells was greater. Our research provides an important new understanding of the effects of surface modification on the biocompatibility and the shape of GNPs in the biomedical field.

Isolation of New Strains of Lactic Acid Bacteria from the Vaginal Microbiome of Postmenopausal Women and their Probiotic Characteristics

Lactic acid bacteria (LAB), traditionally consumed as fermented foods, are now being applied to the medical field beyond health-functional food as probiotics. Therefore, it is necessary to continuously discover and evaluate new strains with suitable probiotic characteristics, mainly focusing on safety. In this study, we isolated eight new strains from postmenopausal vaginal fluid using culturomics approaches, an emerging area of interest. Data showed that most strains possessed significant cell surface hydrophobicity (≥ 76%), auto-aggregation capacity (17 to 61%), strong adhesion activity (8 to 34%), and excellent resistance to gastric acid, bile salt, and digestive enzyme, enhancing their survival in the gastrointestinal tract. Moreover, the strains exhibited functional characteristics, including substantial antibacterial activity with a minimal inhibitory concentration (MIC) ranging from 12.5 to 50%. They also harbored bacteriocins genes, produced short-chain fatty acids (acetate and propionate), exhibited significant phagocytic activity, possessed high antioxidative properties, rapidly depleted sodium nitrite, and exhibited proteolysis and β-glucosidase activity. In addition, heat-killed LAB strains significantly reduced the gene expressions of proinflammatory cytokines such as IL-β, IL-6, and iNOS in macrophages. Safety assessment revealed no cytotoxicity in macrophage cell lines. All strains tested negative for biogenic amine or H2O2 production, displayed no gelatinase or hemolytic activity, lacked virulence genes or detrimental enzymes, and displayed antibiotic susceptibility. In summary, these newly isolated strains demonstrate excellent probiotic functionality with a strong focus on safety, making them promising candidates for future drug development in the relevant fields.

A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation.

The accuracy and efficiency of a coarse-grained (CG) force field are pivotal for high-precision molecular simulations of large systems with complex molecules. We present an automated mapping and optimization framework for molecular simulation (AMOFMS), which is designed to streamline and improve the force field optimization process. It features a neural-network-based mapping function, DSGPM-TP (deep supervised graph partitioning model with type prediction). This model can accurately and efficiently convert atomistic structures to CG mappings, reducing the need for manual intervention. By integrating bottom-up and top-down methodologies, AMOFMS allows users to freely combine these approaches or use them independently as optimization targets. Moreover, users can select and combine different optimizers to meet their specific mission. With its parallel optimizer, AMOFMS significantly accelerates the optimization process, reducing the time required to achieve optimal results. Successful applications of AMOFMS include parameter optimizations for systems such as POPC and PEO, demonstrating its robustness and effectiveness. Overall, AMOFMS provides a general and flexible framework for the automated development of high-precision CG force fields.

A comparative evaluation of dark fermentative bioreactor configurations for enhanced hydrogen production.

Energy from renewable resources has been growing in popularity, which ultimately helps reduce emissions of greenhouse gases (GHGs) and contaminants. Since hydrogen (H2) has a higher combustion production of energy than hydrocarbon fuels, it has been identified as a clean, sustainable, and environmentally friendly energy source. There are several benefits to producing biohydrogen (bioH2) from renewable sources, including lower cost and increased sustainability. Among the bioH2 production processes, dark fermentation supports commercialization and scale-up for industrial applications. This paper considers the various bioreactors, such as anaerobic sequencing batch, continuous stirred, up-flow, fixed-bed, and membrane reactors, and their operational approaches for bioH2 production. This review paper also performs the bibliometric analysis method to identify historical and current developments in a particular field of reactor configuration studies. Furthermore, the main variables influencing reactor performance and methods for increasing process efficiency considering economic and environmental aspects are addressed. The results revealed that continuously stirred reactors are widely utilized for bioH2 production as a cost-effective reactor configuration. Moreover, the membrane bioreactors and fixed-bed reactors areyielded higher bioH2 performance than other configurations. Nevertheless, high energy consumption and costs have presented the need for further development of reactors. Consequently, future recommendations to solve the critical problems faced in reactor configurations, the gaps in the literature, and the points that need improvement were comprehensively reported.

Modern methods of strategic management in the oil and gas industry

The necessity to develop new methods of strategic management in the oil and gas industry is due to many factors, among which one can note the increase in the cost of material and technical components for equipment, high volatility of prices for oil and gas industry resources, lack of stability of financial markets, increased competition for the development of new fields, which requires the search for a new vector of successful development of oil and gas companies engaged in production, transportation and sale of resources.The article provides a comparative analysis of strategic management methods in the oil and gas industry and identifies the main trends in strategic management in accordance with new processes in the global and national economy.

Electrocatalytic and Photocatalytic N2 Fixation Using Carbon Catalysts.

Carbon catalysts have shown promise as an alternative to the currently available energy-intensive approaches for nitrogen fixation (NF) to urea, NH3, or related nitrogenous compounds. The primary challenges for NF are the natural inertia of nitrogenous molecules and the competitive hydrogen evolution reaction (HER). Recently, carbon-based materials have made significant progress due to their tunable electronic structure and ease of defect formation. These properties significantly enhance electrocatalytic and photocatalytic nitrogen reduction reaction (NRR) activity. While transition metal-based catalysts have solved the kinetic constraints to activate nitrogen bonds via the donation-back-π approach, there is a problem: the d-orbital electrons of these transition metal atoms tend to generate H-metal bonds, inadvertently amplifying unwanted HER. Because of this, a timely review of defective carbon-based electrocatalysts for NF is imperative. Such a review will succinctly capture recent developments in both experimental and theoretical fields. It will delve into multiple defective engineering approaches to advance the development of ideal carbon-based electrocatalysts and photocatalysts. Furthermore, this review will carefully explore the natural correlation between the structure of these defective carbon-based electrocatalysts and photocatalysts and their NF activity. Finally, novel carbon-based catalysts are introduced to obtain more efficient performance of NF, paving the way for a sustainable future.

Development of an Oil Field’s Conceptual Model

Development of an Oil Field’s Conceptual Model

Lions, Game Players and Niche Providers: Universities' Positioning in Funding Competitions in Germany

ABSTRACTThis research addresses two important developments in the academic field: First, universities are transforming into organisational actors, gaining more responsibilities and capacities for strategic action, and second, competition has reached a new level. An important driver of competition and universities' development towards strategic actorhood is funding contests in which actors compete with proposals over a limited timeframe with clear evaluation criteria. In Germany, funding contests are central to the crystallisation of a broader competitive positioning and universities develop different strategic approaches in relation to them. We focus on two large‐scale funding contests to analyse universities' heterogeneous positioning strategies: The German Excellence Initiative (EI) and the Quality Pact for Teaching (QPL). We empirically develop three ideal types of universities in terms of strategic positioning (lions, niche providers and game players) and examine different strategic approaches taken by universities when relating to internal and external reference groups.

Intellectual capital meets Industry 4.0: transforming logistics through bibliometric insights

This study conducts a comprehensive bibliometric analysis to explore the evolving relationship between intellectual capital and Industry 4.0 within the logistics sector. The research identifies trends, key contributors, and thematic developments in this interdisciplinary field using data from two leading scientific databases, Web of Science and Scopus. The primary objective is to understand the critical areas of intellectual capital—human, structural, and relational—and their application in Logistics 4.0, driven by technological innovations such as cyber-physical systems and digital transformation. The methodology employs established bibliometric techniques, including co-occurrence and thematic mapping, to evaluate research questions addressing publication trends, country contributions, source relevance, and author influence. Data from 2001–2024 for Web of Science and Scopus datasets reveal key insights: consistent publication growth, leading contributions by countries like China and the USA, and the emergence of themes such as sustainability, innovation, and digital transformation. Comparative analysis highlights differences in keyword trends and collaborative networks between databases, with Scopus displaying a notable time lag in topic evolution. Findings emphasise the pivotal role of intellectual capital in driving logistics innovation, underscoring its integration with Industry 4.0 technologies. Limitations include potential database biases and a data lag for 2024. This study is a foundational reference for scholars and practitioners who leverage intellectual capital for competitive advantage in the digitised logistics era.

미래 수중독립도시의 연구 동향 분석 - CiteSpace 문헌계량학 분석 방법을 기반으로

Against the backdrop of global climate change and rising sea levels, underwater independent cities are widely attracting attention as potential solutions to cope with urbanization and ecological crises. In this study, the literature on underwater independent cities from 2000 to 2023 was systematically organized and analyzed using the CiteSpace literature measurement method. The core topics of this field, research focus, and future development directions were presented through literature inquiry trends, keyword presentation analysis, and literature official analysis. According to the results of the study, future underwater independent cities have important potential in coping with the challenges of climate change and urbanization, and future studies should focus on the synergy effect of technological innovation and policy support. Despite significant progress, existing research focuses primarily on the technical level and lacks in-depth exploration of the role of design in driving the development of independent cities on water. This study clarifies the core value and practical significance of design in the future water independent urban development, and provides theoretical basis and direction for promoting sustainable, ecological and intelligent urban design.

Assessing the potential of geothermal energy in Cambrian complexes for renewable energy transition in Lithuania

Several depleted Cambrian oil fields in Lithuania, with water-cuts reaching 99 %, present promising opportunities for geothermal energy utilization. This study focused on Lithuania’s Cambrian reservoir complex, which has the highest water production rates. The study aimed to explore the advantages of horizontal wells, a technology not previously employed for geothermal production in Lithuania, over traditional vertical wells. To optimize field development, various scenarios were evaluated using mechanistic models, with insights subsequently applied to real field conditions. The results from the mechanistic model demonstrated that horizontal wells outperform vertical wells in both water production and power generation. Furthermore, increasing fracture intensity was shown to enhance water output and power generation by operating the wells at pressures above fracture propagation, facilitating re-injection. However, the study also emphasized the importance of thorough reservoir characterization and modeling to account for geological complexities and improve production outcomes. In conclusion, the research underscores that horizontal wells offer several advantages over vertical wells for geothermal energy production. These include increased water production, higher power generation, reduced drilling costs, and enhanced operational efficiency. These benefits align with technological advancements observed in the hydrocarbon industry, making horizontal wells a viable solution for maximizing geothermal energy potential in Lithuania’s depleted oil and gas fields.

Animation in Audiovisual Art: Socio-Cultural Aspects

The purpose of the research is to determine the place of animation in the social and geopolitical context. To consider modern animation technologies and analyse possible methods of their use. To investigate the relevance of animation in modern life and the peculiarities of its use and development in various fields. The research methodology involves the use of the generalisation method, which made it possible to identify specific socio-cultural aspects of animation; the theoretical method, which was used to outline the impact of animated films on the viewer (the primary source of information was contemporary publications on the areas of influence of animation in various fields). A literature analysis of research and publications on the topic was also carried out. Scientific novelty. For the first time, the author analyses the areas of possible use of animation in Ukraine during the war and post-war reconstruction, taking into account its socio-cultural potential. It has been determined that within the transformation from classic cartoons to modern CGI masterpieces, animation has become a complex art form with its own set of unique characteristics, among which the main ones are movement and mobility, visual appeal, emotional manifestations, suspension of reality, flexibility and adaptability, technical skills and innovation, transformation and metamorphosis, exaggeration and caricature, symbolism and metaphors. Conclusions. The article shows that animated films are in great demand worldwide. It is proved that there are several animation studios in Ukraine that are actively engaged in the creation of animated films and series, as well as working on projects for international markets. The study identifies that animation as an art form has numerous socio-cultural aspects that influence animation works’ perception, creation, and distribution.

Influence of Soil Temperature on Root Development and Microbial Diversity in Paddy Fields: A Comprehensive Review

Influence of Soil Temperature on Root Development and Microbial Diversity in Paddy Fields: A Comprehensive Review

Control of the productivity and the reservoir energy state by processing and analysis of permanent downhole gauges data using machine learning

The issues of high-quality hydrodynamic studies of wells are relevant at any stage of the development of an oil field. The reliability and reliability of the results depend on the significant technological indicators of the development of deposits, deposits and the parameters of the operation of individual wells. This paper presents a new approach to the analysis of hydrodynamic studies of wells by the method of steady-state sampling, based on cluster analysis. The steady-state sampling method is a significant and most effective tool in analyzing measurements of continuous monitoring systems, which allows you to obtain more reliable data for further use in solving various development tasks. The new clustering-based method improves the quality of analysis and reduces the negative impact of the human factor on the success of data operations. This is achieved by automatically dividing the data into groups or clusters, which allows you to more accurately determine the characteristics of steady-state modes. The article provides a detailed description of the new method, its advantages and possibilities of application in the practice of analyzing hydrodynamic studies of wells based on data from continuous monitoring systems. The obtained results of testing the presented approach to the analysis of large amounts of information in a single research plane allow us to say about the high level of its relevance and the possibility of further improvement of algorithms, which will reduce the level of uncertainty in the implementation of digital diagnostics of well operation. Keywords: well testing; permanent downhole gauges (PDG); telemetry systems; inflow performance relationship (IPR); reservoir pressure; clustering; machine learning methods; multilayer systems.