Future Field Research Articles

Vortex-induced vibration and energy harvesting of cylinder system with nonlinear springs

Vortex-induced vibration is a common fluid–structure coupling phenomenon in ocean engineering. As a nonlinear stiffness structure caused by anisotropic deformation and support gap, nonlinear spring is of great significance in the field of vortex-induced vibration. Based on the structural dynamics equations and the wake oscillator model, the vibration model of the 2-Degree of Freedom cylinder system with the nonlinear stiffness spring is established. The stiffness ratio, mass ratio, and damping ratio of the cylinder system are changed, and the amplitude and frequency response are analyzed to obtain the effect of nonlinear spring on vortex-induced vibration. The results show that the cylinder system with a nonlinear stiffness spring can maintain a high vibration amplitude in a large Reynolds number range, and the smaller the mass ratio, the more significant the effect is. When m*=1.5, the amplitude of the cylinder system in a cross flow is greater than 0.6 at Re = 180 000–480 000. The energy harvesting in the vortex-induced vibration of cylinder systems with nonlinear stiffness springs is also studied. The efficiency depends on the damping ratio and Reynolds number. When the damping ratio is constant, the energy harvesting efficiency increases first and then decreases with the Reynolds number increases. When m*=1.5, the energy harvesting efficiency of the cylinder system is 19.27% with Re= 35 000 and damping ratio ξ=0.155. As a kind of renewable energy, energy harvesting in vortex-induced vibration is a potential development direction in the future energy field.

Renewable Nanocellulose/rGO film with a dense brick-and-mortar structure for electromagnetic interference and energy storage

Considering the high requirements for robust and flexible power supplies, the green and sustainable fiber-based supercapacitor is suitable to accommodate the inevitable serious deformation and low radiation issues in wearable electronic energy storage devices due to its easy integration. Compatible natural nanocellulose (NFC) as a framework and the two-dimensional conductive graphene (GO) nanosheets constructed a dense brick-and-mortar structure, which the dispersing effect of NFC avoided GO aggregation. The utilization of the green-reducing agent and the obtained NFC/reduced graphene oxide (rGO) composite film endowed the composite film with a continuous conductive network by the self-assembly behavior of NFC and GO, that the lightweight flexible electrode NFC/50rGO with 13 μm thickness had high conductivity (883.70 S m−1), a total electromagnetic shielding efficiency of 27.1 dB at 8.2 GHz frequency, electrochemical behavior (mass-specific capacitance up to 170.6F g−1), and reversible charge–discharge behavior. Developing a green and portable small electronic device with such a flexible and high-strength electrode symbolizes the critical development direction in the future electronic equipment field.

Digital prosthodontics in near future: Awareness among the dental students on intraoral scanners and its probability of replacement over conventional method in the near future of prosthodontics: A cross-sectional survey

Aims: The aim of the study is to analyse the awareness and knowledge about IOSs among the dental students of Tamil Nadu. Settings and Design: A cross sectional survey – questionnaire study. Materials and Methods: A descriptive, cross-sectional survey was carried out to assess the knowledge, awareness, and practice of IOSs among dental students through a questionnaire of five domains, with five questions in each domain. This is sent to various dental students of Tamil Nadu through mail and othersocial media platforms. Statistical Analysis Used: It was performed using a statistical package for the social sciences’ [SPSS] software version 22. Descriptive statistics were performed for demographic variables. Chi square test was performed for all the qualitative data. Results: Most of the answers were statistically significant with P < 0.05 and few results were not significant indicating less knowledge and awareness about digital prosthodontics among the dental students. Conclusions: Thus, the addition of digital dentistry in the curriculum of dental students and giving them more knowledge helps in the flourishment of the future prosthodontic field with much accuracy of treatment and with less patient discomfort replacing the conventional methods.

Efficient ethidium bromide removal using sodium alginate/graphene oxide composite beads: Insights into adsorption mechanisms and performance

This study explores the efficacy of sodium alginate/graphene oxide (SA/GO) composite beads for ethidium bromide (EtBr) removal from aqueous solutions. GO was synthesized and crosslinked with SA to design composite beads, which were evaluated under various physicochemical conditions. Characterization techniques, including FESEM, TGA, XRD, and FTIR, were employed to analyze the beads before and after EtBr adsorption. Results demonstrated rapid EtBr adsorption onto SA/GO, following the Freundlich isotherm model with a maximum adsorption capacity of 2.97mmol g−1 (1171.05 mg g−1). The influence of solution pH and the negligible role of Br− ions suggested that electrostatic interaction was the primary mechanism for EtBr adsorption on SA/GO. XRD and FTIR analyses revealed that EtBr adsorption also occurred in the interlayer space of SA/GO beads, with the ethidium cations (Et+) entering this space in a horizontal or oblique (≥45°) orientation. The accumulation of Et+ within the interlayer space was estimated to form 1–6 basic inclined layers. SA/GO beads demonstrated promising characteristics for EtBr pollutant treatment, including low environmental impact, cost-effectiveness, rapid adsorption kinetics, and high removal efficiency. This study provides valuable insights into the adsorption mechanisms and performance of SA/GO beads, highlighting their potential for future field applications in the environmental remediation of EtBr contamination.

Effect of Al precursor’s properties on interactions with self-assembled monolayers for area selective deposition

Area selective deposition (ASD) is a high-precision atomic-level manufacturing technology that enables the development of bottom-up manufacturing methods in the future semiconductor field. The area selective deposition behavior of Al precursors on 1-octadecylthiol (ODT) passivated Cu/SiO2 surfaces was studied through experimental and theoretical analysis. The relationship between precursor steric hindrance, symmetry, penetration depth in ODT, and adsorption energy was elucidated. The loss of selectivity caused by different penetration depths of the precursor in ODT can be post-treated with acids or H2 plasma to remove the physisorption of precursor molecules between ODT chains, thereby improving the selectivity. Reliable ASD technology has been successfully applied to Cu/SiO2 patterns. Dimethylaluminum isopropoxide can selectively deposit about 10 nm of Al2O3 on SiO2 without detectable defects on the Cu area. This provides important insights into the choice of precursors in the ASD process and can extend its application to a wider range of device manufacturing schemes.

Simulation of Gas‐Particle Partitioning of Semi‐Volatile n‐Alkanes and PAHs in Nanjing, China, and Denver, United States: Effects of Vapor Pressure and Surface Adsorption Estimation

AbstractThe concentration data of semi‐volatile n‐alkanes and PAHs in the gas phase and in PM2.5 were obtained from Nanjing and Denver. First, the gas‐particle partitioning coefficients of the target compounds were calculated and compared with the predictions based on the equilibrium absorptive partitioning theory. Although the vapor pressure (poL) estimation method was selected to improve the agreement between measured and predicted partitioning coefficients, n‐alkanes in Denver and PAHs in both cities exhibited stronger sorption to PM2.5 than predicted. By including the adsorption mechanism, the average logarithms and temporal variations of the partitioning coefficients were well simulated in both Nanjing and Denver. The partitioning of n‐alkanes in Nanjing can be primarily explained by the absorption mechanism, while the adsorption mechanism dominates that for n‐alkanes in Denver and PAHs in both cities. To obtain an optimal simulation, the poL of n‐alkanes and PAHs was estimated using the SPARC and SIMPOL—two group contribution methods, respectively, and the difference between desorption and vaporization enthalpies was set between 2 and 3 kcal mol−1 for n‐alkanes in Denver and PAHs in both cities. Therefore, the estimation of poL and surface adsorption should be carefully considered when parameterizing the gas‐particle partitioning of non‐polar organic compounds in future field and modeling studies.

Prediction of Turbulent Boundary Layer Flow Dynamics with Transformers

Time-marching of turbulent flow fields is computationally expensive using traditional Computational Fluid Dynamics (CFD) solvers. Machine Learning (ML) techniques can be used as an acceleration strategy to offload a few time-marching steps of a CFD solver. In this study, the Transformer (TR) architecture, which has been widely used in the Natural Language Processing (NLP) community for prediction and generative tasks, is utilized to predict future velocity flow fields in an actuated Turbulent Boundary Layer (TBL) flow. A unique data pre-processing step is proposed to reduce the dimensionality of the velocity fields, allowing the processing of full velocity fields of the actuated TBL flow while taking advantage of distributed training in a High Performance Computing (HPC) environment. The trained model is tested at various prediction times using the Dynamic Mode Decomposition (DMD) method. It is found that under five future prediction time steps with the TR, the model is able to achieve a relative Frobenius norm error of less than 5%, compared to fields predicted with a Large Eddy Simulation (LES). Finally, a computational study shows that the TR achieves a significant speed-up, offering computational savings approximately 53 times greater than those of the baseline LES solver. This study demonstrates one of the first applications of TRs on actuated TBL flow intended towards reducing the computational effort of time-marching. The application of this model is envisioned in a coupled manner with the LES solver to provide few time-marching steps, which will accelerate the overall computational process.

The Impact of Social Entrepreneurship on Market Mavericks: A Meta-Analysis

Numerous studies on the topic of social entrepreneurship and businesses have yielded a wide range of findings and publications. The purpose of this research is to provide a synopsis of previous work on this topic and to identify where the field is now and where it is heading. Consequently, the research on social entrepreneurship's potential to transform businesses was the subject of a bibliometric analysis. Several methods were employed in our search of the SCOPUS database, such as authors, study kinds, and keywords. Articles from different authors, institutions, and even nations are all part of this study's scope. There are several analysis applications used in bibliometric research, including VOS viewer (for data visualization) and Bibilomagika 2.2 (for frequency). Using the terms "social entrepreneurship" and "business" as search terms, 1272 publications were located between 2020 and 2024. The time frame of this research is five years. Most of the articles exhibit a pattern with both a growth and a fall, according to the analysis. But there will be a rise of 359 in 2023. With a total of 12 articles (0.94%) authored by Ratten, Vanessa was the most productive author in this study. The US, India, UK, Spain, and China are the most common bases for publications. Between the two journals, Sustainability (Switzerland) has published 93 papers and Emerald Emerging Markets Case Studies has published 48. Research, national contributions, famous writers, and the most active journals on the subject can be better understood with the use of this study. The data collected can serve as a foundation for academics to fill knowledge gaps and chart a course for the field's future.

A Comprehensive Review of Group-III Nitride Light-Emitting Diodes: From Millimeter to Micro-Nanometer Scales.

This review describes the development history of group-III nitride light-emitting diodes (LEDs) for over 30 years, which has achieved brilliant achievements and changed people's lifestyles. The development process of group-III nitride LEDs is the sum of challenges and solutions constantly encountered with shrinking size. Therefore, this paper uses these challenges and solutions as clues for review. It begins with reviewing the development of group-III nitride materials and substrates. On this basis, some key technological breakthroughs in the development of group-III nitride LEDs are reviewed, mainly including substrate pretreatment and p-type doping in material growth, the proposal of new device structures such as nano-LED and quantum dot (QD) LED, and the improvement in luminous efficiency, from the initial challenge of high-efficiency blue luminescence to current challenge of high-efficiency ultraviolet (UV) and red luminescence. Then, the development of micro-LEDs based on group-III nitride LEDs is reviewed in detail. As a new type of display device, micro-LED has drawn a great deal of attention and has become a research hotspot in the current international display area. Finally, based on micro-LEDs, the development trend of nano-LEDs is proposed, which is greener and energy-saving and is expected to become a new star in the future display field.

Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning

ABSTRACT Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustainable development of marine resources. Offshore wind power has developed rapidly in China, but the spatiotemporal distribution characteristics of offshore wind turbines and farms remain poorly understood. Extracting nearshore small targets from Sentinel-1 synthetic aperture radar images remain challenging due to noise from the rough sea surface background and the diverse substrate types in offshore wind turbine (OWT) distributions. Here, to address these issues, we first created a dataset of China’s offshore wind turbines (COWTs) that contained typical substrate types (e.g. seawater and tidal flats). Then, a deep learning model integrating an attention mechanism and receptive fields was used to accurately detect COWTs. The well-trained model was used to detect the changes in COWTs from 2015 to 2022. Our model detected an increase in the number of offshore wind farms in China from 7 to 114 (clusters counted as an offshore wind farm), while COWTs increased from 305 to 6451 from 2015 to 2022. The Taiwan Strait exhibited the highest wind speeds, yet the Jiangsu and Guangdong regions had the most installed turbines. Over 90% of COWTs were located in areas with offshore wind energy resources less than 500 W/m3, indicating a mismatch between COWT installation and wind resource distribution. This study demonstrated temporal and geographical generalizability, which is promising for global wind power detection. Our analysis of spatiotemporal variation in COWTs facilitates informed decision-making for future field establishment and sustainable marine planning.

Perovskite quantum dots/WSe2 mixed-dimensional van der Waals heterostructure for photoelectric enhancement and polarization sensitivity

The performance of some heterostructures based devices is limited by strong interlayer coupling and limited electronic structure modulation. Meanwhile, the detection of polarized light holds great significance in future photoelectric fields such as cryptography. In this work, zero-dimensional/two-dimensional (0D/2D) mixed-dimensional van der Waals heterostructure (MvdWH) polarization-sensitive photodetector composed of solution-synthesized CH3NH3PbI3(MAPbI3) perovskite quantum dots (QDs) and chemical vapor deposition (CVD)-grown WSe2 was designed, forming a type II band structure. The MvdWH effectively enhance the carrier transmission rate, and improve the efficiency of charge separation, realizing an ultra-high responsiveness of 217.8 A/W and an ultrasensitive photodetectivity of 2.56 × 1011 Jones under 633 nm illumination. The device shows ultra-fast response times of 4.5/4.9 μs, which is due to the introduction of 0D QDs will shorten the electron transport path and reduces trap-mediated non-radiative recombination. Further, the in-plane anisotropy of MAPbI3 QDs was identified by angle-resolved absorption spectroscopy and angle-resolved polarization Raman spectroscopy, respectively. The MAPbI3/WSe2 device exhibits polarization-sensitive optoelectronic detection with an anisotropy ratio of 1.71, and powerful polarization-sensitive imaging ability, implying that MAPbI3 QDs endows the device with polarization detection and imaging capabilities. These results indicate that the construction of 0D/2D MvdWH opens a new avenue for ultrasensitive and polarization-sensitive photodetection, which depicts opportunities for designing multifunctional, high-performance imaging optoelectronic devices.

Phase transitions in a heterogeneous lattice hydrodynamic model involving both communication distance and memory time duration differences

An in-depth analysis of vehicle heterogeneity characteristics in intelligent heterogeneous traffic flows, involving connected autonomous vehicles and human-driven vehicles, is essential to reveal the evolutionary mechanism of congestion and to understand its interaction among traffic factors. It has significant theoretical value in guiding the future field deployment and testing of large-scale connected autonomous vehicles through the systematic analysis of the complex dynamic characteristics of intelligent heterogeneous traffic flows from a traffic engineering perspective, as well as traffic management and control in intelligent heterogeneous traffic flow environments. In this study, the lattice hydrodynamics model is applied to construct a corresponding traffic flow model for the differences of communication capability and historical information memory capability for heterogeneous vehicles. The roles of these heterogeneous properties and their interactions are investigated. Through linear stability analysis and nonlinear analysis, we in this study explore the influence of key traffic factors involving the differences of communication capability and historical information memory capability contributing to ameliorating intelligent heterogeneous traffic dynamics, and numerical simulations are carried out to verify the accuracy and reliability of theoretical analysis. An interesting discovery is that longer memory duration gets better, but the optimizing impact of memory duration on the system being marginally decreasing.

Data-driven multiscale geomechanical modeling of unconventional shale gas reservoirs: a case study of Duvernay Formation, Alberta, West Canadian Basin

The Duvernay Formation in Canada is one of the major oil and gas source formations in the Western Canadian Sedimentary Basin, located at its deepest point. While it demonstrates promising development potential, challenges arise in the urgent need for integration of geology and engineering models, as well as in optimizing sweet spots, particularly as infill wells and pads become central operational objectives for the shale gas field. A lack of the geomechanical understanding of shale gas reservoirs presents a significant obstacle in addressing these challenges. To overcome this, we implemented data acquisition and prepared historical models and profiles, resulting in an extended high-resolution geological and reservoir property model with a fine grid system. Subsequently, a 3D full-field multi-scale geomechanical model was constructed for the main district by integrating seismic data (100 m), geological structures (km), routine logs (m), core data (cm), and borehole imaging (0.25 m), following a well-designed workflow. The predicted fracturability index (brittleness) ranges from 0.6 to 0.78, and a lower horizontal stress difference (STDIFF) is anticipated in the target formation, Upper Duvernay_D, making it a favorable candidate for hydraulic fracturing treatment. Post-analysis of the multi-disciplinary models and various data types provides guidelines for establishing a specific big database, which serves as the foundation for production performance analysis and aggregate sweet spot analysis. Fourteen geological and geomechanical candidate parameters are selected for the subsequent sweet spot analysis. This study highlights the effectiveness of multi-scale geomechanical modeling as a tool for the integration of multi-disciplinary data sources, providing a bridge between geological understanding and future field development decisions. The workflows also offer a data-driven framework for selecting parameters for sweet spot analysis and production dynamic analysis.

Understanding Silent Data Corruption in Processors for Mitigating its Effects

Silent Data Corruption (SDC) in processors can lead to various application-level issues, such as incorrect calculations and even data loss. Since traditional techniques are not effective in detecting these errors, it is very hard to address problems caused by SDCs in processors. For the same reason, knowledge about these SDCs in the wild is limited. In this paper, we conduct an extensive study on CPU SDCs in a large production CPU population, encompassing over one million processors. In addition to collecting overall statistics, we perform a detailed study to understand 1) whether certain processor features are particularly vulnerable and their potential impacts on applications; 2) the reproducibility of CPU SDCs and the triggering conditions (e.g., temperature) of those less reproducible SDCs; and 3) the challenges to mitigate and handle CPU SDCs. We further investigate the implications which our observations obtained from the above researches have, on the SDC fault models, SDC mitigation strategies and the future research fields. In addition, we design an efficient SDC mitigation approach called Farron, which uses prioritized testing to detect highly reproducible SDCs and temperature control to mitigate less reproducible SDCs. Our experimental results indicate that Farron can achieve better coverage of CPU SDCs with lower overall overhead, compared to the baseline used in Alibaba Cloud. This demonstrates that our observations are able to assist in SDC mitigation.

Nitrogen credits after peanut (Arachis hypogaea L.)

AbstractState‐level cooperative extension services provide fertilizer recommendations for row crops in the United States. Of these, nitrogen (N) recommendations are arguably the most important because N is the most common yield‐limiting nutrient in nonlegume crop production systems. Throughout the peanut (Arachis hypogaea L.) growing region of the United States, Cooperative Extension Services generally recommends 22–67 kg N/ha credit to crops following peanut, likely due to the assumption that peanut, being a legume, contributes N to the following crop. The body of peer‐reviewed literature indicates that N credits from peanut to the subsequent crop are negligible. Recent literature indicates that apparent differences in yield following peanut compared to a nonlegume are a result of nonlegume crop residue favoring N immobilization rather than N mineralization from peanut residue. Taken together, recent research corroborates the few previous scientific publications addressing the issue, namely, that cooperative extension service recommendations to reduce N fertilization to crops after peanut are not supported by the peer‐reviewed literature. Future field research should include summer fallows to determine if yield differences between legumes and nonlegumes are due to N credits by the legume or N immobilization by nonlegumes. Data on N loss pathways following peanut are needed to identify management strategies that can mitigate N losses after peanut harvest. In conclusion, the preponderance of peer‐reviewed science does not support current Extension recommendations regarding peanut N credits to the following crop.

Photodynamic inactivation of edible photosensitizers for fresh food preservation: Comprehensive mechanism of action and enhancement strategies.

Foodborne harmful bacteria not only cause waste of fresh food, but also pose a major threat to human health. Among many new sterilization and preservation technologies, photodynamic inactivation (PDI) has the advantages of low-cost, broad-spectrum, energy-saving, nontoxic, and high efficiency. In particular, PDI based on edible photosensitizers (PSs) has a broader application prospect due to edible, accessible, and renewable features, it also can maximize the retention of the nutritional characteristics and sensory quality of the food. Therefore, it is meaningful and necessary to review edible PSs and edible PSs-mediated PDI, which can help to arouse interest and concern and promote the further development of edible PSs-mediated PDI in the future field of nonthermally sterilized food preservation. Herein, the classification and modification of edible PSs, PS-mediated in vivo and PS-mediated in vitro mechanism of PDI, strengthening strategy to improve PDI efficiency by the structure change synergistic and multitechnical means, as well as the application in fresh food preservation were reviewed systematically. Finally, the deficiency and possible future perspectives of edible PSs-mediated PDI were articulated. This review aimed to provide new perspective for the future food preservation and microbial control.

Unveiling the Opportunities of Unexplored Use of Cover Crop in Mediterranean Agriculture through Systematic Review and Meta-Analysis

Cover crops are multifunctional, and contribute to improving soil properties and reducing environmental impact compared to no-cover crops, thus could provide multiple soil, agricultural, and environmental benefits, and they are recognized as a valid strategy for the achievement of sustainable agriculture. However, cover crops’ impacts on subsequent cash crops and soil characteristics are dependent on several factors, such as pedoclimatic conditions, cover crop species, agricultural practices, method of termination, and irrigation management. The fact that cover crops are never applied as a single practice in the real agricultural sector, but are instead combined with other factors or agricultural practices, deeply affects their performance, but the scientific literature nevertheless keeps considering the use of cover crops alone. Moreover, the potential outcomes of cover crops that encompass other factors or agricultural practices affecting soil quality, weed control, and cash crops are still unclear. Additionally, cover crops are still poorly use in the Mediterranean type of climate. Therefore, this study reviewed the scientific literature to identify the most relevant factors or agricultural practices driving cover crops’ performance, and to outline future fields of investigation looking towards promising sustainable agriculture in Mediterranean areas with a view to minimizing the competition for soil water with the cropping systems and to reduce soil degradation. Furthermore, the investigation includes multiple factors or agricultural practices that better represent the real farming system, contributing to a comprehensive understanding of their interactions with cover crops, and suggests alternative strategies for reducing yield gap while seeking to achieve agricultural sustainability.

A Deep Federated Learning-Based User Credit Evaluation Model Under Financial Internet of Things Scenarios

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy.

Structural health monitoring on seismic loading in buildings

Structural Health Monitoring (SHM) is a process of using various sensors and technologies to assess the condition of a structure continuously or periodically. In the context of seismic loading on buildings, SHM becomes particularly crucial for ensuring the structural integrity and safety of the building during and after seismic events. This bibliometric review's goal is to provide a thorough analysis of the (SHM) unveiling its development, present patterns, and potential future paths. The approach entails a meticulous bibliometric examination of research productivity from the establishment of the subject in 1968. Highlighting significant milestones and charting trends in research. Research indicates a steady increase in SHM under seismic load on over 112 yearly papers demonstrate construction research, with main contributions from the US and Italy. The report also shows how SHM is being used more in construction, supported by sensor research.This review's originality lies in its thorough overview of SHM research, revealing historical and current trends and suggesting future study fields. This provides a solid platform for future research, promoting expansion in this promising sector.

Boundary Work in the Nordic Media Model: Metajournalistic Discourse on Alternative Media in Denmark, Norway, and Sweden

ABSTRACT The increasing prominence of alternative news media has been identified as a particular challenge to contemporary established journalism, next to collapsing financial models, platform dependency, falling levels of trust and increased competition. Against this background, this study examines how news editors from 23 established news organizations in Sweden, Norway, and Denmark view the rise and role of alternative news media and thereby the boundaries of the journalistic field. Combining strategic action field theory, boundary work and the concept of metajournalistic discourse, it identifies three metajournalistic discourses—We are the (real) journalists, We are in control (for now), and We are under pressure. Together, these metajournalistic discourses portray alternative news media as a challenge that largely has been overcome, and as currently insignificant actors that clearly do not represent “real journalism”. At the same time, the discourses also display concerns that alternative news media might represent potential future field ruptures via their association with broader potentially detrimental media developments, such as genre-confused media audiences or harassment of journalists.