Developments In Field Research Articles

Application of Cold Atmospheric Pressure Plasma Jet Results in Achievement of Universal Antibacterial Properties on Various Plant Seeds

In view of a constant growth in the human population on Earth, the provision of a necessary amount of high-quality food looks challenging. As over 10% of the crop yields are annually lost due to the presence of phytopathogens, the development of novel, eco-friendly methods of pest eradication might contribute to avoiding nutritional shortages. Here, we propose a controlled application of cold atmospheric pressure plasma (CAPP) generated in the form of an atmospheric pressure plasma jet (APPJ), for which we conducted multivariate optimization of the working parameters with the use of the design of experiments (DoE) in addition to the response surface methodology (RSM). After estimating the optimal operating conditions of APPJ, we determined the inactivation rates caused by 2 min CAPP exposure towards bacterial phytopathogens from three species Dickeya solani, Pectobacterium atrosepticum and Pectobacterium carotovorum artificially inoculated on the surface of plant seeds from four species. Logarithmic reductions, as a key result of this work, were enclosed in the range of 1.61–4.95 in the case of Cucumis sativus, Pisum sativum, and Vigna radiata, while for the bacteria-inoculated Zea mays seeds, lower antibacterial properties of APPJ equaling 0.86–1.12 logs were noted. The herein applied exposure to APPJ did not reveal any statistically significant detrimental effects on the germination of plant seeds, seed coat integrity, or early plant growth. Even plant growth promotion by 20.96% was observed for the APPJ-exposed Zea mays seeds. By applying colorimetric assays and optical emission spectrometry (OES), we determined the oxidative potential in addition to identifying the reactive oxygen species (ROS) •OH, •HO2, •O2−, O3, and 1O2 and the reactive nitrogen species (RNS) N, NO2, and NO3 responsible for the antibacterial properties of APPJ. In summary, universal antiphytopathogenic properties of the APPJ treatment reached due to proper optimization of the working conditions were revealed against three bacterial strains from the family Pectobacteriaceae inoculated on the seeds from diverse plant species. The data presented herein may contribute to future development of the plasma agriculture field and provide alternatives to pesticides or the prevention-based control methods towards plant pathogenic bacteria.

The Top 100 Cited Articles in Male Aesthetic Surgery: A Bibliometric Analysis.

In recent years, male aesthetic surgery has significantly risen in popularity, attributable to surgical technique advancements and societal changes. Research in this subject area has increased accordingly, underscoring the need to identify the most influential articles. This study evaluates the bibliometric characteristics of the top 100 most cited male aesthetic surgery articles published in the last 50years. The 100 most cited articles in male aesthetic surgery from 1974 to 2024 were identified using Web of Science. We examined key trends in authorship contribution, institutional affiliation, country affiliation, citation count, journal impact factor, eigenfactor score, article influence score, and funding agency contribution. As of June 2024, the top 100 articles were cited a total of 7,341 times, with individual citation counts ranging from 18 to 243. The top cited article was "A Review of Psychosocial Outcomes for Patients Seeking Cosmetic Surgery," published in 2004. Of the 100 articles, the top contributing authors were Dr. Joel E. Pessa (5%) and Dr. Rod J. Rohrich (4%). The top contributing organization was the University of Texas System (8%). The peak year for publications was 2008, accounting for 10% of articles. The USA contributed to 60 of 100 articles. Plastic and Reconstructive Surgery was the most prolific journal, publishing 33% of articles. Through a historical analysis of the field's development up to its present state, our findings serve as a valuable resource for those interested in advancing their understanding of male aesthetic surgery literature. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Talent Management Effectiveness in Vietnamese State-Owned Banks

In the constantly changing digital context, state-owned commercial banks have implemented positive changes in their human resource development strategies. To meet the government's objectives for human resource development in the banking field, it is crucial to explore current talent management practices and assess their effectiveness in this industry. This study addresses the identified gap based on the objectives of reviewing the talent management practices employed by Vietnamese banks and evaluating the effectiveness of these activities. This research employed qualitative method as well as quantitative method to serve the research objectives. Twenty semi-structured indepth interviews with middle-level managers from different banks were conducted. Furthermore, 300 quantitative questionnaires containing measurement scales were sent to managers and staff at the banks to assess the efficiency of talent attraction, engagement, development, and retention. Findings reveal that state-owned commercial banks have developed an increasing level of commitment to talent management activities with obtained tangible results. However, there are still downsides related to talent training, engagement, and the measurement of talent management effectiveness for different reasons.

Sports training injuries and prevention measures using big data analysis

This work explores the application of big data technology in monitoring sports training injuries, emphasizing the biomechanical principles underlying injury mechanisms to enhance the accuracy of injury prediction and provide scientific prevention measures. It collects training data from professional sports teams using big data technology and constructs a Bi-directional Long Short-Term Memory (BiLSTM)—Residual Network (ResNet) model through deep learning techniques. In this model, the BiLSTM module captures the temporal sequence features of sports data, while the ResNet module improves the model’s expressiveness and stability through residual learning. To establish a clearer connection with mechanobiology, the study discusses the mechanical forces involved in sports injuries, including impact forces, torsional stresses, and their effects on tissues at the cellular level. By integrating biomechanical insights with big data analytics, the research aims to provide a comprehensive understanding of how mechanical stressors contribute to injury risk. The performance of the proposed model in predicting sports injury risks is evaluated, showing an accuracy of 95.72%, a precision of 91.59%, a recall of 85.40%, and an F1 score of 88.56%, significantly outperforming existing traditional models and other comparison algorithmsTherefore, the proposed model demonstrates exceptional performance in improving the accuracy of sports injury prediction and providing personalized prevention measures, offering experimental references for the intelligent development of the sports field by bridging sports science and biomechanics.

Improvement of technology for pressing process of stainless steel pipes.

The method of producing pipes on pressing units is most effective in the production of pipes from low-plasticity steel grades. These include, in particular, steel grades with increased corrosion resistance and cold resistance, which are currently in demand for the development of oil and gas fields in the Far North. But even the use of a pressing process with a favorable stress state scheme, as experience has shown, does not completely eliminate problems with metal destruction. When hot pressing pipes from complex-alloy steel grades, one of the problems is the poor quality of the outer surface, caused by localized destruction of the metal in the form of surface cracks. To predict the probability of occurrence of ruptures on the outer surface of pipes and to search for technical solutions to prevent them, the phenomenological theory of destruction of metals and alloys during pressure treatment is taken as a basis, the foundations of which are laid in the works of V. L. Kolmogorov and A. A. Bogatov. At the same time, the influence of the main process factors on the quality of the surface of hot-pressed pipes is considered. In particular, an analysis of the influence of the granulometric composition and viscosity of the lubricants used is performed. Permissible temperature ranges for deformation of complex-alloy steel grades are considered. The nature of the change along the deformation center of the main characteristics of the stress-strain state is analyzed. Particular attention is paid to the study of the influence of the plasticity of the press tool shape on the degree of resource exhaustion. The studies were carried out using computer modeling using the finite element method and experimental studies on modern testing equipment. As a result of such a comprehensive analysis, the main factors influencing the occurrence of ruptures of the outer surface of pipes during hot pressing were identified. An assessment of the degree of influence of each of them was made and technical proposals were developed to minimize the likelihood of ruptures.

Integrated logistics and election performance: a systematic literature review

ABSTRACT Elections are typically seen through the lens of political frameworks, overlooking the profound impact of logistics on election performance. Through a systematic literature review, this study explores the integral role of logistics in election performance. It highlights the links between logistics functionality performance and perceptions of electoral integrity. The findings reveal that as prerequisites, elections require specialised logistics systems integrated with inclusive I.T. and the human side to ensure dependability, increased visibility and security during the transportation and warehousing of sensitive electoral materials throughout election cycles. The findings identify distinct challenges faced by developed and developing countries, particularly concerning reverse logistics, warehousing, transport networks, distribution planning and technological infrastructure, suggesting the need for tailored logistics strategies based on national development status. Finally, the study emphasises the imperative involvement of logistics experts to ensure the effective integration of logistics functions within elections, oversight and the strategic development of an election logistics field.

Research on the Green Path of Building Electrical Engineering in the Context of Digitalization

In today's digital era, the greening of building electrical systems has become a crucial path for the sustainable development of the construction industry. Judging from the current research status at home and abroad, although certain achievements have been made, there are still many areas that need further exploration. Digital technologies have brought transformative opportunities to building electrical systems. In the field of intelligent lighting, they can achieve precise dimming and scene adaptive control; in the application of high-efficiency and energy-saving equipment, their performance can be optimized with the help of digital monitoring and regulation; after the digital upgrade of energy management systems, refined energy consumption analysis and intelligent dispatching can be accomplished; in the process of integrating renewable energy with building electrical systems, digital technologies can help solve the problems of intermittency and stability. However, this process is faced with challenges such as technology integration, cost control, standards and regulations, and user awareness. Looking ahead, with technological innovation and policy promotion, the greening of building electrical systems will thrive in the directions of intelligence, high efficiency, and synergy, laying a solid theoretical and practical foundation for the sustainable development in the construction field.

How faculty with critical care specialties learn in a university hospital: a qualitative phenomenological study

ObjectivesThe study aims to explore the workplace learning experiences of medical faculty in critical care specialties at a university hospital, focusing on how they develop their professional identity and construct...

New opportunities in mechanistic and functional microbiome studies.

The field of microbiome research has experienced remarkable growth, leading to unprecedented discoveries of the molecular mechanisms that dictate host-microbiota interactions and their crucial roles in human health. In this "chemical biology of the microbiome" focus issue from Cell Chemical Biology, this Voices piece asks researchers from a range of backgrounds to share their insights on the most exciting recent developments in the microbiome field.

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.

Numerical simulation and parametric optimization of harmonic pulse water flooding technology

Pulse water injection technology is an emerging enhanced oil recovery method that improves oil-water interface and flow characteristics by periodically varying the injection rate. This study, using the TOUGH2 simulator, examines the impact of different pulse injection waveforms (sine, triangle, and square) on the recovery factor in medium to low-permeability carbonate reservoirs. It highlights that pulse water injection outperforms constant rate injection, with the triangle waveform yielding the best recovery rates. Through controlled simulations, findings indicate that increasing pulse injection time and amplitude enhances recovery and water flow. However, frequency variations in ultra-low frequency pulses have minimal effects on recovery. Additionally, the efficiency of pulse water injection is inversely related to reservoir porosity and permeability, while higher oil viscosity significantly boosts recovery efficiency. Conversely, increasing water temperature reduces dynamic viscosity, affecting the oil-water interface lag and decreasing overall displacement efficiency. The study also notes significant differences between heterogeneous and homogeneous reservoir models, emphasizing the need for future large-scale numerical modeling to consider reservoir heterogeneity. These findings provide valuable insights for optimizing pulse water injection in medium to low-permeability reservoirs, supporting practical oil field development strategies.

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.

A bibliometric and visualized analysis of heartland virus.

Heartland virus (HRTV) is an emerging tick-borne bunyavirus first detected in 2009. The purpose of this study was to utilize bibliometric analysis to assess the research trends, key foci, and progress of HRTV. This analysis aims to provide valuable references and insights for future basic research and prevention and control of HRTV to promote the progress and development of related fields. The Web of Science Core Collection (WOSCC) was used to extract global publications on the HRTV from 2013 to 2024. VOSviewer, CiteSpace, Scimago Graphica, and Bibliometrix were used to process the data and visualize the results. A stable trend in publication numbers was observed, with 82 articles from 17 countries. The United States led in publications, with significant contributions from the Centers for Disease Control and Prevention-USA. Keywords indicated research emphasis on "Heartland virus" and "severe fever." HRTV research is in a phase of continuous and progressive growth, with a steady literature output over the past decade, indicating this field's wide interest and importance in the research community. Currently, researchers are focusing on pathogenesis, immune response, vector relationships, and epidemiology, providing valuable insights for future studies.

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.