Information Content Research Articles

Spectral resolution effects on the information content in solar spectra

When interpreting spectropolarimetric observations of the solar atmosphere, wavelength variations in the emergent intensity and polarization translate into information on the depth stratification of physical parameters such as the temperature, velocity, and magnetic field. Resolving the fine details in the shapes of the spectral lines and their polarization gives us the capability to resolve small-scale depth variations in these physical parameters. With the advent of large-aperture solar telescopes and the development of state-of-the-art instrumentation, the requirements on spectral resolution have become a prominent question. We aim to quantify how the information content contained in a representative set of polarized spectra of photospheric spectral lines depends on the spectral resolution and spectral sampling of that spectrum. We used a state-of-the-art numerical simulation of a sunspot and the neighboring quiet Sun photosphere to synthesize polarized spectra of magnetically sensitive neutral iron lines. We then applied various degrees of spectral degradation to the synthetic spectra and analyzed the impact on its dimensionality using principal component analysis and the wavelength power spectrum using wavelet decomposition. Finally, we applied the Stokes Inversion based on Response functions (SIR) code to the degraded synthetic data to assess the effect of spectral resolution on the inferred parameters. We find that the dimensionality of the Stokes spectra and the power contained in the small spectral scales significantly change with the spectral resolution. We find that regions with strong magnetic fields where convection is suppressed have more homogeneous atmospheres and produce less complex Stokes profiles. On the other hand, regions with strong gradients in the physical quantities give rise to more complex Stokes profiles that are more affected by spectral degradation. The degradation also makes the inversion problem more ill-defined, so inversion models with a larger number of free parameters overfit and give wrong estimates. The impact of spectral degradation in the interpretation of solar spectropolarimetric observations depends on multiple factors, including the spectral resolution, noise level, line spread function (LSF) shape, complexity of the solar atmosphere, and degrees of freedom in our inversion methods. To mitigate this impact, incorporating a good estimation of the LSF into the inversion process is recommended. Having a finely sampled spectrum may be more beneficial than achieving a higher signal-to-noise ratio per wavelength bin. Considering the inclusion of different spectral lines that can counter these effects, and calibrating the effective degrees of freedom in modeling strategies, are also important considerations. These strategies are crucial for the accurate interpretation of such observations and have the potential to offer more cost-effective solutions.

Development of Intragenic InDel Markers From RNA‐Seq Data for Map‐Based Cloning and Marker‐Assisted Selection in Maize (Zea mays L.)

ABSTRACTMaize is one of the most extensively grown and produced crops in the world. Functional molecular markers (FMM) are essential tools for enhancing the efficiency of both breeding and basic research. The transcriptome obtained from high‐throughput sequencing is a valuable resource for developing FMM. In this study, we precisely identified high‐density and highly polymorphic RNA‐derived InDel markers from 368 high‐quality, high read depth maize RNA‐seq datasets using stringent criteria. The nonredundant marker dataset comprised 13,904 InDel markers with a Polymorphism Information Content (PIC) ≥ 0.5 and a principal allelic difference (length difference between the most and second most frequent alleles) ≥ 3 bp, covering 7749 genes. Subsequently, the polymorphism of InDel markers was verified experimentally. Fifty InDel markers were randomly selected, and their average PIC values in 20 maize inbred lines selected from 368 RNA‐seq samples and 20 newly selected maize inbred lines were 0.53 and 0.54, with range intervals of 0.18 to 0.77 and 0.22 to 0.74, respectively. The high density and polymorphism of RNA‐derived InDel markers identified in this study make them effective tools for maize gene cloning, genetic diversity studies, genome‐wide association analysis, and marker‐assisted selection.

Genome-Wide In Silico Analysis of Microsatellite Loci in Rabbits

This study aimed to characterize microsatellites in the rabbit genome using an in silico approach and to develop and validate microsatellite markers. Blood samples were collected from 15 Baladi rabbits and 18 New Zealand White (NZW rabbits). The GMATA software was used to define SSRs in the extracted sequences. Twelve primer pairs were used to validate the loci identified and the primers developed. The total number of the detected microsatellite loci overall chromosomes was 1,136,253. The di-nucleotide microsatellite repeats dominated and exceeded 88% of the detected microsatellites in all chromosomes. There were no microsatellites detected in mitochondrial DNA. The highest relative microsatellite abundance was obtained for chromosome 19, followed by 13 and 6. The highest estimated SSR density was obtained for chromosome 14, and the lowest was for mitochondrial DNA, followed by chromosome 13. The polymorphism was 81.63% and 75.51% for Baladi and NZW rabbits, respectively. The number of detected alleles ranged between two and seven alleles/loci, and polymorphic information content was from 35% to 71%. The AMOVA analysis showed that the total variance of all levels of population structure was 15.734. The results definitely confirmed higher genetic diversity in Baladi compared with NZW rabbits.

Quality and Readability of Online Information on Percutaneous Needle Electrolysis

Objective PNE (Percutaneous needle electrolysis) is a relatively new and effective method used to treat musculoskeletal injuries. The aim of this study was to evaluate PNE-related websites in terms of information quality, readability and content. In addition, this study aimed to identify typologies of websites that provide highly reliable information about PNE. Methods On April 7, 2024, the term “percutaneous needle electrolysis” was searched on the Google search engine. The categories, quality, content, readability, The Journal of American Medical Association (JAMA) score were used for quality assessment, and the Flesch–Kincaid grade (FKG) and the Simple Measure of Gobbledygook (SMOG) were used for readability assessment. Results Of the 153 websites, 107 had high-quality content. The quality of professional websites and scientific publishing sites appeared to be high. There was no significant difference in quality between the initial 10 websites and the remaining websites (p = .317). The average readability scores of the websites were not at the recommended level. Conclusion Most PNE-related websites are of high quality, but do not meet the recommended readability levels. It is important to review existing online content on PNE and create new high quality content that meets the readability recommendations.

Is YouTube an Adequate Source of Learning About 3D Printing in Orthodontics?

We aimed to assess the content, quality, and reliability of information provided by YouTube videos regarding 3D printing in orthodontics. YouTube was searched using the term ‘3D printing in orthodontics’. 91 videos that met the inclusion criteria were selected. Videos were rated independently by two assessors using reliability of information index, GQS and HONcode. General parameters, video purpose, source and topic were recorded. Videos uploaded by doctors had significantly higher GQS and reliability of information scores compared to videos from companies. Moderate/high information content videos had significantly higher GQS compared to low information content videos. There is a need for better quality videos about 3D printing in orthodontics.

Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models

Abstract. Distributed energy and mass balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well-observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet-snow maps could potentially help refine the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction (SCF) pertains predominantly to the ablation season. In this work, wet-snow maps derived from Sentinel-1 and SCF retrieval from Sentinel-2 are compared against model results from a fully distributed energy balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet-snow line (WSL) to compare Sentinel-1 wet-snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals a delayed snowmelt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed for the achievement of earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow-line (SL) observations were also substantially reduced. These results suggest that wet-snow maps contain valuable real-time information for snowpack models, complementing flat-field snow depth observations well, particularly in complex terrain and at higher elevations. The persisting correlation between wet-snow-line and snow-line biases provides insights into refined development, tuning, and data assimilation methodologies for operational snow-hydrological modelling.

The Impact of the Relationship between Company Characteristics and the Audit Committee on the Quality of Financial Reports (A Field Study on a Sample of Iraqi Private Companies)

The current study primarily aimed to determine the impact of the relationship between company characteristics and the audit committee on the quality of financial reports in its various dimensions (formal, temporal, and informational content). The study also sought to identify the influence of company characteristics on the quality of financial reports across these dimensions. The audit Committee role in improving financial report quality was the first area that the survey examined; this includes looking not just where audits. The study employed a descriptive and then analytical approach to achieve the goal of answering its research question. The study population included all senior management personnel at Iraqi private businesses, responsible auditors and members of audit commissions. From a variety of industries five companies were chosen, yielding a sample of 500 possible contacts (average 100 staff / company). A random sample of 219 participants was selected using the formula that Richard Geiger came up with. Questionnaires were sent to the participants, and all the responses accepted. After analyzing the data and testing the hypotheses using SPSS software, several key findings were revealed, the most significant being: • Company characteristics significantly affect the quality of financial reports. These characteristics provide an accurate and objective depiction of the company's financial position, aiding investors in making informed investment decisions. • Iraqi companies' unique characteristics notably affect the formal dimension of financial report information, as the company's reputation and business relationships are reflected in the reports' format, influencing investors' valuation of shares. • The timeliness of financial report information is vital in attracting investors. It reflects the company's growth and continuity in the market, significantly affecting its valuation. • Company characteristics influence the content of financial reports, including financial data and accompanying disclosures, thereby providing precise information for investors. • Audit committees play a pivotal role in ensuring the integrity and transparency of financial information, enhancing trust, and reducing errors in financial reports. They also contribute to improving the organizational structure of financial information, facilitating data comparability across different periods and entities. • The results indicate that audit committees improve the accuracy of the temporal information presented, boosting the reliability of information provided to the public. Additionally, audit committees assure investors that financial data are free from material errors, positively affecting investment decisions. • The research has shown that the relationship between company characteristics and audit committees enhances the verifiability of financial reports and also builds investor confidence. However, this research finds that if committee independence and effectiveness are not sufficiently so a very close relationship between enterprises (or groups of companies) and audit committees may have undesirable effects on quickness in presenting accountings. • The findings underscore that the unique characteristics of a company directly influence the content of its financial reports, highlighting the importance of having influential audit committees to ensure the credibility of the presented information.

Assessment of Quality and Reliability of YouTube Videos for Student Learning on Class II Resin Composite Restorations.

YouTube has emerged as a popular platform for accessing educational content. However, its effectiveness has been a topic of debate in dental education. This study aimed to analyze the content and quality of YouTube videos focusing on Class II resin composite restorations. The first 100 videos of Class II resin composite information on YouTube were evaluated. The overall quality of the videos was assessed using the video information and quality index (VIQI) and Content Score based on Class II resin composite criteria. Videos with a score of less than the mean were recognized as low-content. No significant differences were observed between high- and low-content videos when the number of views, likes, duration, days since upload, viewing rate, and interaction index were investigated (p>0.05). The number of subscribers revealed a marginally significant difference (p=0.053). The high-content videos demonstrated higher mean values compared to the low-content videos in flow (4.6 vs. 3.8; p=0.0004), accuracy (4.3 vs. 3.3; p<0.0001), value (3.7 vs. 2.9; p=0.002), and precision (4.8 vs. 4.2; p=0.0002). The overall VIQI score was significantly higher (p<0.0001) in high-content videos (Mean 17.4; SD 1.5) compared to the low-content videos (Mean 14.2; SD 2.2). When the Content Score was assessed, high-content videos (Mean 9.9; SD 1.3) revealed a higher score (p<0.0001) than low-content-videos (Mean 4.2; SD 2.3). Most YouTube videos showcasing Class II resin composites serve as effective teaching tools. However, a significant number of videos with low content exist. Therefore, dental students should exercise caution when utilizing YouTube videos for learning purposes.

Spillover Effects of Paid Functions on Physicians' Unpaid Knowledge Activities: Quasi-Experimental Approach.

To promote sustained contributions by physicians to online health care communities, these platforms have introduced a content payment model that offers economic incentives for physicians' online knowledge activities. However, the impact of these paid features on unpaid knowledge activities remains unexplored. This study investigated how the introduction of economic incentives in online medical communities affects physicians' unpaid knowledge activities in the community. The data for this study were obtained from the Haodf Online platform in China, which has implemented paid scenarios for its science popularization function, providing economic benefits to physicians. The dataset, which comprises panel data, includes 7453 physicians who participated in both paid and unpaid knowledge contributions on the website. This study examined the impact of paid knowledge activities on physicians' free knowledge contributions, focusing on dimensions including knowledge quantity, quality, and diversity. To address the timing discrepancies in physicians' participation in paid activities, we used a quasi-experimental design that combined the approach of propensity score matching and multi-period difference in differences. In the balance test results of the propensity score matching, the absolute values of the SDs of all matching variables were mostly <5% after matching, ensuring the accuracy of the results obtained from the difference in differences method. This study found that participation in paid knowledge activities had a positive spillover effect on physicians' free knowledge contributions, which manifested in the increase in post quantity (473.1%; P<.001), article length (108%; P=.009), function word frequency (0.6%; P=.001), causal word frequency (0.2%; P<.001), and content information entropy (6.6%; P=.006). The paid function led to a decrease in the consistency between titles and content (-115.5%; P<.001). The findings of this study contribute to the existing literature on the impact of economic incentives in the medical context. For the platform, providing economic incentives to physicians can have positive significance in promoting the development of the platform's knowledge ecosystem and can effectively encourage physicians to contribute to both paid and free knowledge activities. This study provides a valuable reference for the platform to introduce a paid knowledge model, which is beneficial to the sustainable development of the platform.

Efficient value synthesis in the orbitofrontal cortex explains how loss aversion adapts to the ranges of gain and loss prospects.

Is irrational behavior the incidental outcome of biological constraints imposed on neural information processing? In this work, we consider the paradigmatic case of gamble decisions, where gamble values integrate prospective gains and losses. Under the assumption that neurons have a limited firing response range, we show that mitigating the ensuing information loss within artificial neural networks that synthetize value involves a specific form of self-organized plasticity. We demonstrate that the ensuing efficient value synthesis mechanism induces value range adaptation. We also reveal how the ranges of prospective gains and/or losses eventually determine both the behavioral sensitivity to gains and losses and the information content of the network. We test these predictions on two fMRI datasets from the OpenNeuro.org initiative that probe gamble decision-making but differ in terms of the range of gain prospects. First, we show that peoples' loss aversion eventually adapts to the range of gain prospects they are exposed to. Second, we show that the strength with which the orbitofrontal cortex (in particular: Brodmann area 11) encodes gains and expected value also depends upon the range of gain prospects. Third, we show that, when fitted to participant's gambling choices, self-organizing artificial neural networks generalize across gain range contexts and predict the geometry of information content within the orbitofrontal cortex. Our results demonstrate how self-organizing plasticity aiming at mitigating information loss induced by neurons' limited response range may result in value range adaptation, eventually yielding irrational behavior.

Elite Cooperation and Affective Polarization: Evidence From German Coalitions

Affective polarization is a central characteristic of political competition, but high levels are seen as potentially harmful. In this article, we link the study of affective polarization to that of coalition politics, expecting that by signalling the willingness to cooperate in a coalition, political elites can reduce mutual dislike between political camps. We argue, first, that the impact of coalition formation should depend on the information content (‘surprisingness’) of this signal, and, second, that its effect should spill over to parties outside of the coalition. Combining 20 years of monthly voter-level data from Germany with data on national and regional coalitions, we show that coalitions are most likely to reduce affective distance when participating parties are ideologically distant and when the signals are still recent. Moreover, coalitions have a system-wide impact beyond the specific parties involved. We discuss the implications for the role of political elites in shaping affective polarization.

Information provision on local industries by Japanese public libraries: a survey on cultural industry-focused corners

PurposeThe purpose of this study was to examine how Japanese public libraries position and implement their services to provide information on cultural industries as local industries. Furthermore, the design characteristics of corners providing information on local industries and the potential and issues of services for contributing to the development of local industries were discussed.Design/methodology/approachThrough qualitative content analysis of library and municipal publications and websites, the following perspectives were examined: description, background of collecting materials and relationship with the local government policies. Through direct observations of library corners, the following perspectives were examined: location of the library, location of the corner, library information resource arrangement and content and content without library information resources. The targets were the following three corners related to the cultural industry: animation corner, film materials room and Ningyo (Japanese doll) corner.FindingsRegarding the design characteristics of corners, an intent to disseminate the appeal of local industries was particularly revealed. Regarding potential, cultural industries examined in this study are present in various and supporting cultural industries can promote development in these communities, even though traditional BI services may not be effective in some communities. Regarding issues, helping revitalize users and local businesses and collaboration with other organizations was inadequate.Originality/valueThis study provides suggestions for libraries to support local industries, expanding the concept of BI services, and has the potential to support communities that traditional BI services cannot cover.

Reliable and Effective Stereo Matching for Underwater Scenes

Stereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective stereo matching. This study develops two specialized stereo matching networks: UWNet and its lightweight counterpart, Fast-UWNet. UWNet utilizes self- and cross-attention mechanisms alongside an adaptive 1D-2D cross-search to enhance cost volume representation and refine disparity estimation through a cascaded update module, effectively addressing underwater imaging challenges. Due to the need for timely responses in underwater operations by robots and other devices, real-time processing speed is critical for task completion. Fast-UWNet addresses this challenge by prioritizing efficiency, eliminating the reliance on the time-consuming recurrent updates commonly used in traditional methods. Instead, it directly converts the cost volume into a set of disparity candidates and their associated confidence scores. Adaptive interpolation, guided by content and confidence information, refines the cost volume to produce the final accurate disparity. This streamlined approach achieves an impressive inference speed of 0.02 s per image. Comprehensive tests conducted in diverse underwater settings demonstrate the effectiveness of both networks, showcasing their ability to achieve reliable depth perception.

Microsatellite markers development and molecular fingerprinting of cashew cultivars.

Cashew (Anacardium occidentale L.) is a widely cultivated tree with great economic significance. In cashew, several elite cultivars have been developed for commercial cultivation, which form the underpinning for the cashew-based industries and the several billion-dollar world trade. However, frequently the genetic purity of the planting material is not maintained, resulting in great economic losses. Therefore, there is a need to develop a reliable method for the identification of cultivars to avoid economic losses to the cultivators and the protection of cultivars by the breeders. In this study, 35 new microsatellite/simple sequence repeat (SSR) markers were developed, and a set of 20 highly polymorphic and reproducible markers were used for DNA fingerprinting and genetic diversity analysis in 32 cashew cultivars. The polymorphic information content (PIC) of newly developed markers varied from 0.19 to 0.67, with an average of 0.44. The probability of identifying any two genotypes with identical fingerprints using the 20 SSR markers used for fingerprinting here in cashew cultivars was less than 2.8 × 10-11. Of the set of 20 markers, eight were sufficient for uniquely fingerprinting all the cultivars. Genetic diversity analysis by the neighbor-joining (NJ) method grouped 32 cultivars into three main clusters, and the grouping had no relationship to the geographic regions or the pedigree. The findings of this study are useful for the conservation and protection of cultivars under the PVP Act for ensuring the trading of quality planting material and are also useful for cashew breeding programs.

Learning Many-to-Many Mapping for Unpaired Real-World Image Super-Resolution and Downscaling.

Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a two-stage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional many-to-many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.

Sufferer Odysseus in Video Games of the 21st century

In recent decades, the active development of the video game market, as well as the rapid growth in the number of game content consumers, have caused an urgent need for scientifi c research of these products. In foreign game studies, there are two approaches: narratology and ludology. The narratological approach, which is based on the analysis of the plot of the game scenario, has a long history and is closer to the humanities, while ludology includes the digital aspects of video games. Based on the fi rst approach, which allows us to consider in detail various aspects of the psycho-emotional impact on the educational level of the game user, we can offer some recommendations for video content creators, as well as for the bodies that control the creation of such products. The article analyzes three computer games that have the name Odysseus in their titles and that use episodes and details from Homer’s heroic epic The Odyssey in their plots: the 2000 Odyssey quest from the French company Cryo Interactive (The Odyssey: The Search for Ulysses), the 2011 game by the Russian company Nevosoft Odysseus. The Long Way Home, and the adventure action game with numerous quests from Ubisoft Assassin’s Creed, which released the fi rst part of the game Assassin’s Creed: Odyssey in 2018. Using comparative methods, the narratives of the listed games are compared with the narrative in the original version f Homer’s epic poem The Odyssey, and elements of the Gothic tradition and associated popular culture in the gameplay scenario are noted. As a result of the study, it became obvious that the postmodernist general cultural context of modern civilization and game designers, who turned to ancient archetypal models for advertising purposes, chose the most destructive, barbaric type of using ancient names and narratives. In classical plots, there are no moral guidelines left, laid down there by classical authors. An important issue, which the conducted analysis leads to, is the problem of responsibility of video game developers for the educational, upbringing and informational content of the content they offer.

Analysis of scale-dependent spatial correlations of actual evapotranspiration measured by lysimeters

Accurate determination of actual evapotranspiration (ETa) is important in various research fields like hydrology, meteorology, ecology and agriculture. In situ ETa can be determined using weighing lysimeters and eddy covariance. However, despite being regarded as the most precise in situ method for measuring ETa, the information content of lysimeter measurements remains poorly understood. Here we examined the spatial correlations between ETa measured at different locations by lysimeter (ET-LYS) and at different locations by eddy covariance (ET-EC). This was done for the period 2015 - 2020 and the analysis was made for different spatial (range: 0 to 500 km) and temporal scales (range: 1 day to 1 year) using 23 lysimeters and 4 eddy covariance towers. We found that: (a) Same lysimeters at the plot scale show very high correlations of ET-LYS; (b) The Pearson correlation of daily standardized anomalies of ET-LYS between sites exhibit moderate to high correlations and were similar to that of ET-EC, indicating that lysimeter is generally as representative as EC regarding ETa, and can provide certain information at the landscape and larger regional scale. During winter, the spatial correlations for ET-LYS were smaller; (c) Wavelet analysis indicated that temporal correlations in ETa were strongest for distances in time around 12 months (yearly cycle) and less than three months. Spatial correlations were smaller under drought conditions (in the year 2018). Furthermore, combination of multiple ET-LYS from different sites improved the predictability of ET-LYS for another site, suggesting that ET-LYS can be predicted well using ET-LYS from different neighboring sites. Overall, lysimeter measurements can provide information at much larger scales compared to their small measurement area.

Effect of Gamification on the Development of Digital Competencies of Regular Basic Education Teachers

In the digital age, teachers’ digital competencies are important for effective education, yet many educators, particularly in Latin America, lack these essential skills. While gamification has shown promise in various education contexts, its effectiveness for developing teachers’ digital competencies remains understudied, especially in the Latin American setting. This study aimed to examine the effect of gamification on the development of digital competencies of basic education teachers in Trujillo, Peru. A quasi-experimental design was used, and 60 primary-level teachers were selected through convenience sampling; they were randomly assigned to either an experimental (n = 30) or control (n = 30) group. The experimental group participated in a two?month program consisting of 15 gamified sessions, whereas the control group received no intervention. Data were collected via an adapted version of the Teacher Digital Competence Questionnaire in pretest and posttest phases. Descriptive statistics, Mann-Whitney U tests, and Kruskal-Wallis tests were employed for data analysis. The results reveal significant improvements in the experimental group’s digital competencies across all dimensions (information literacy, communication, content creation, safety, and problem-solving), with 93.3% achieving the “achieved” level postintervention, compared with 30% of the control group. Inferential analysis confirms significant differences between groups in the posttest phase (Mann-Whitney U = 96.5, p &lt; .05). These findings suggest that gamification is an effective strategy for developing teachers’ digital competencies, and can contribute valuable empirical evidence to the field of education technology and holds important implications for teacher training and education policies in Latin America.

Artificial intelligence in vegetable growing in Russia: problems and prospects

Relevance. Using artificial intelligence (AI) systems is of particular importance in the transformation of modern Russian vegetable growing.Methods. Using the monographic method, the world and Russian practice of developing and using artificial intelligence systems is considered using the example of: CropX, John Deere, IBM Watson, AgEagle Aerial Systems, Blue River Technology, Farmwise, Taranis, Naiad Irrigation, Sustainable Agriculture Technology (SAT), Leader Technology, AgroCalypso, AgroVzglyad group of companies, Russian technologies and systems, Rostec Artificial Intelligence Competence Center, AgroBot, Kaluga Astra, Agrosystems, RosAgro, SAFMAR, AgriCo management company, Sadko agrofirm, AgroEco, AgroInvest and others.Results, A list of problems with the use of AI in vegetable growing is identified, such as: high initial costs of implementing AI; lack of qualified personnel with the necessary knowledge in the field of IT and agronomy; high probability of technical failures, leading to losses in productivity and increased costs; difficulty of integration with existing automation systems; information security of databases; difficulty of data preparation; legal and ethical risks; lack of necessary infrastructure; unfavorable climatic conditions for the operation of AI; resistance to innovations on the part of personnel. Despite the identified problems, the prospects for using AI in vegetable growing in Russia will allow: optimizing agronomic processes; improving the quality of forecasting and monitoring; increasing the level of automation; improving the quality of data processing; improving resource manageability; increasing the level of adaptation of production to market needs; increasing adaptation to storage conditions and supply chain logistics, increasing the level of information content of technological processes.Conclusion. Expanding the practice of using AI will increase the efficiency and sustainability of vegetable growing in the strategic perspective.

‘But I don’t watch television!’ Keeping late-night comedy and political satire relevant for the audience

What happens when members of the audience no longer sit in front of the television to be entertained by a regular late-night comedy and political satire broadcasts, choosing instead to access information and entertainment content via social media? What must late-night comedy and political satire do to adapt and remain relevant for younger audiences who simply ‘don’t watch television’ anymore? The article begins by engaging with how members of Generation Z consume entertainment content, reflecting on findings from survey data and learnings from teaching undergraduates enrolled in entertainment, media, and politics classes during each new election cycle. A discussion of late-night comedy and political satire shows’ impact on the media narrative and agenda is privileged, along with a review of current adaptations late-night comedy shows are making to increase audience engagement. The manuscript concludes by offering a detailed list of suggested strategies to help late-night comedy and political satire remain relevant for members of Generation Z who possess far different media habits than their parents and grandparents. Overall, the research offers a path forward for traditional network late-night comedy and political satire programming so that it can stay relevant and engaging well into the twenty-first century.