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Numerous techniques and methods are used in School-Based Agricultural Education. These techniques are vital to the goal of educating a diverse and expansive population that exist within secondary agricultural science courses. Agricultural educators, both traditionally and alternatively certified, know and understand the value of using multiple techniques to differentiate one's instruction. It has also been previously identified that, while there are some similarities, these two certification groups do have unique professional development needs. This study sought to identify the techniques Alabama's agriculture instructors had been taught and are using. Through the identification of these techniques, key differences between traditionally and alternatively certified instructors can be identified. Based on the findings of this study, techniques that had a higher average of training received were more likely to be used by both certifications. Between traditionally and alternatively certified instructors, an increase in training received and decrease in utilization was seen across most of the specific techniques for alternatively certified instructors. Individual techniques also called to question the over-training or underutilization of that technique. While there are key differences across the certification types, the findings indicate more training may be needed in an effort to promote the use of different techniques in agricultural education. [Note: The issue number (1) shown at the top of the first page of the PDF is incorrect. The correct issue number is 3.]

The production of J/ψ mesons is studied in proton-lead collisions at the centre-of-mass energy per nucleon pair sNN=8.16 TeV with the LHCb detector at the LHC. The double differential cross-sections of prompt and nonprompt J/ψ production are measured as a function of the J/ψ transverse momentum and rapidity in the nucleon–nucleon centre-of-mass frame. Forward-to-backward ratios and nuclear modification factors are determined. The results are compared with theoretical calculations based on collinear factorisation using nuclear parton distribution functions, on the colour glass condensate or on coherent energy loss models.

Abstract Production of prompt D 0 mesons is studied in proton-lead and lead-proton collisions recorded at the LHCb detector at the LHC. The data sample corresponds to an integrated luminosity of 1.58±0.02 nb−1 recorded at a nucleon-nucleon centre-of-mass energy of s N N = 5 $$ \sqrt{s_{\mathrm{NN}}}=5 $$ TeV. Measurements of the differential cross-section, the forward-backward production ratio and the nuclear modification factor are reported using D 0 candidates with transverse momenta less than 10 GeV/c and rapidities in the ranges 1.5 < y ∗ < 4.0 and −5.0 < y ∗ < −2.5 in the nucleon-nucleon centre-of-mass system.

Researchers have reported that participation in agricultural education reinforces STEM concepts. The use of projects is common in agricultural education. However, the foundational understanding of certain tenets of project use is not clear. A quasi-experimental study was conducted to test the effects of project authenticity on learning. AFNR students (N = 219) were sampled and assigned as a cohort to one of four treatments. Treatments were project types which varied in their degree of project authenticity. During analysis, it was apparent that the intended project variable of authenticity had less effect on the outcomes than other factors. An analysis of covariance was used to test the effects of perceived novelty on change scores in a pre-post quasi-experimental design. A test of project type groups, which varied on novelty, yielded statistically significant results (p < 0.05) with small effect size ([omega superscript 2] = 0.04). A Pearson Chi Square was calculated to determine if a relationship between novelty and project type existed and determined to be significant, X[superscript 2] (12, N=152) = 22.35, p = 0.034. A pairwise comparison was calculated to compare levels of novelty to determine where the variation existed. Students with novelty level 4 had statistically different change scores than those at novelty level 2 (p = 0.006) and students with novelty level 4 were different from those with a novelty level 5 (p = 0.012). Students with less familiarity with the project (a more novel project) showed more improvement on the science exam.

The world is confronting numerous challenges, including global warming, health epidemics, and population growth, each presenting significant threats to the stability and sustainability of our planet’s ecosystems. Such issues have collectively contributed to a reduction in agricultural productivity, corresponding with an increase in demand and costs of essential commodities. This critical situation requires more sustainable environmental, social, and technological solutions. Molecular hydrogen (H2) has been suggested as a “green” solution for our energy needs and many health, agricultural, and food applications. H2 supplementation in agriculture may represent a novel and low-carbon biotechnological strategy applicable to the abundant production of crops, vegetables, and fruits in agri-food chains. H2 is a potential green alternative to conventional chemical fertilizers. The use of a hydrogen-rich water irrigation system may also provide other health-related advantages, i.e., decreasing the heavy metal accumulation in crops. By adopting a H2 strategy, crop producers, food processors, and decision-makers can contribute to sustainable solutions in the face of global challenges such as climate change, communicable disease epidemics, and a growing population. The versatile applications of H₂ in agriculture and the wider food industry position it as a uniquely suitable approach to address today’s significant challenges, potentially fostering better crop production and positively impacting the agri-food chain. The present review is timely in combining the latest knowledge about the potential applications of H2 in the agriculture and food industry, from farm to fork.

Abstract Social media impacts people’s wellbeing in different ways, but relatively little is known about why this is the case. Here we introduce the construct of “social media sensitivity” to understand how social media and wellbeing associations differ across people and the contexts in which these platforms are used. In a month-long large-scale intensive longitudinal study (total n = 1632; total number of observations = 120,599), we examined for whom and under which circumstances social media was associated with positive and negative changes in social and affective wellbeing. Applying a combination of frequentist and Bayesian multilevel models, we found a small negative average association between social media use AND subsequent wellbeing, but the associations were heterogenous across people. People with psychologically vulnerable dispositions (e.g., those who were depressed, lonely, not satisfied with life) tended to experience heightened negative social media sensitivity in comparison to people who were not psychologically vulnerable. People also experienced heightened negative social media sensitivity when in certain types of places (e.g., in social places, in nature) and while around certain types of people (e.g., around family members, close ties), as compared to using social media in other contexts. Our results suggest that an understanding of the effects of social media on wellbeing should account for the psychological dispositions of social media users, and the physical and social contexts surrounding their use. We discuss theoretical and practical implications of social media sensitivity for scholars, policymakers, and those in the technology industry.

Oxygen (O2) was discovered approximately 250 years ago (Contribution 1), a breakthrough accredited to at least three people: Antoine-Laurent de Lavoisier in France (Antoine Lavoisier), Carl Wilhelm Scheele in Sweden, and Joseph Priestley in England [...]

Abstract In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the performance of fraud detection models. This study presents a comparison in model performance using the most important features selected by SHAP (SHapley Additive exPlanations) values and the model’s built-in feature importance list. Both methods rank features and choose the most significant ones for model assessment. To evaluate the effectiveness of these feature selection techniques, classification models are built using five classifiers: XGBoost, Decision Tree, CatBoost, Extremely Randomized Trees, and Random Forest. The Area under the Precision-Recall Curve (AUPRC) serves as the evaluation metric. All experiments are executed on the Kaggle Credit Card Fraud Detection Dataset. The experimental outcomes and statistical tests indicate that feature selection methods based on importance values outperform those based on SHAP values across classifiers and various feature subset sizes. For models trained on larger datasets, it is recommended to use the model’s built-in feature importance list as the primary feature selection method over SHAP. This suggestion is based on the rationale that computing SHAP feature importance is a distinct activity, while models naturally provide built-in feature importance as part of the training process, requiring no additional effort. Consequently, opting for the model’s built-in feature importance list can offer a more efficient and practical approach for larger datasets and more intricate models.

Social media platforms are too often understood as monoliths with clear priorities. Instead, we analyze them as complex organizations torn between starkly different justifications of their missions. Focusing on the case of Meta, we inductively analyze the company’s public materials and identify three evaluative logics that shape the platform’s decisions: an engagement logic, a public debate logic, and a wellbeing logic. There are clear trade-offs between these logics, which often result in internal conflicts between teams and departments in charge of these different priorities. We examine recent examples showing how Meta rotates between logics in its decision-making, though the goal of engagement dominates in internal negotiations. We outline how this framework can be applied to other social media platforms such as TikTok, Reddit, and X. We discuss the ramifications of our findings for the study of online harms, exclusion, and extraction.

Produced by photosynthesis, oxygen (O2) is a fundamentally important gas in biological systems, playing roles as a terminal electron receptor in respiration and in host defence through the creation of reactive oxygen species (ROS). Hydrogen (H2) plays a role in metabolism for some organisms, such as at thermal vents and in the gut environment, but has a role in controlling growth and development, and in disease states, both in plants and animals. It has been suggested as a medical therapy and for enhancing agriculture. However, the exact mode of action of H2 in biological systems is not fully established. Furthermore, there is an interrelationship between O2 and H2 in organisms. These gases may influence each other’s presence in solution, and may both interact with the same cellular components, such as haem prosthetic groups. It has also been suggested that H2 may affect the structures of some proteins, such as globins, with possible effects on O2 movement in organisms. Lastly, therapies may be based on supplying O2 and H2 together, such as with oxyhydrogen. Therefore, the relationship regarding how biological systems perceive and respond to both O2 and H2, and the interrelationship seen are worth considering, and will be discussed here.

Abstract In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge. This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), and a novel ensemble supervised feature selection method. The techniques are applied to optimize Machine Learning models for fraud identification in the classification of highly imbalanced Big Medicare Data. Utilizing two datasets from The Centers for Medicare & Medicaid Services (CMS) labeled by the List of Excluded Individuals/Entities (LEIE), our principal contribution lies in empirically demonstrating that data reduction techniques applied to these datasets significantly improves classification performance. The study employs a systematic experimental design to investigate various scenarios, ranging from using each technique in isolation to employing them in combination. The results indicate that a synergistic application of both techniques outperforms models that utilize all available features and data. Moreover, reduction in the number of features leads to more explainable models. Given the enormous financial implications of Medicare fraud, our findings not only offer computational advantages but also significantly enhance the effectiveness of fraud detection systems, thereby having the potential to improve healthcare services.

Abstract As a means of building explainable machine learning models for Big Data, we apply a novel ensemble supervised feature selection technique. The technique is applied to publicly available insurance claims data from the United States public health insurance program, Medicare. We approach Medicare insurance fraud detection as a supervised machine learning task of anomaly detection through the classification of highly imbalanced Big Data. Our objectives for feature selection are to increase efficiency in model training, and to develop more explainable machine learning models for fraud detection. Using two Big Data datasets derived from two different sources of insurance claims data, we demonstrate how our feature selection technique reduces the dimensionality of the datasets by approximately 87.5% without compromising performance. Moreover, the reduction in dimensionality results in machine learning models that are easier to explain, and less prone to overfitting. Therefore, our primary contribution of the exposition of our novel feature selection technique leads to a further contribution to the application domain of automated Medicare insurance fraud detection. We utilize our feature selection technique to provide an explanation of our fraud detection models in terms of the definitions of the selected features. The ensemble supervised feature selection technique we present is flexible in that any collection of machine learning algorithms that maintain a list of feature importance values may be used. Therefore, researchers may easily employ variations of the technique we present.

Cancer is a leading cause of mortality worldwide. B-cells are a keystone of the adaptive immune response and are essential for the presentation of tumor-associated antigens to various types of T-cells. Approximately 1.5% of global cancer cases, including breast and gastric carcinomas and both Hodgkin’s and non-Hodgkin’s lymphomas, are linked with prior Epstein–Barr Virus (EBV) infection. Such properties make EBV-infected lymphocytes ideal models for understanding the effect of oxyhydrogen gas on dysfunctional cell cycling. The aim of this study is to assess the effects of the direct infusion of oxyhydrogen gas on the replicative capacity of EBV-immortalised B-lymphocytes. Oxyhydrogen gas was directly infused into cell culture media. Cells were incubated in 95% air and 5% CO2 for up to 72 h. Cell enumeration was assessed with and without the addition of mitogenic growth stimuli, and subsequent cell-cycle analysis was performed. Cell enumeration: An initial trend of replicative inhibition of TK6 cells is noted with a single oxyhydrogen treatment at the 24 and 48 h time points. The daily addition of oxyhydrogen-infused media showed statistically relevant data at 24 and 48 h but not at 72 h. In mitogen-stimulated cells, a non-statistical trend of inhibition was observed at 24, 48 and 72 h. Analysis details a significant increase in DNA in the Sub G1 phase, indicating increased apoptosis.

The central role of technologies like social media and smartphones in children's lives means that learning to navigate the digital world is now a core developmental task for elementary school students. While access to the Internet affords children the potential to find connections at scale, it can also expose them to potential threats like privacy violations and online aggression. In this study, we report on the results of an educational intervention that aimed to improve students’ ability to respond adaptively to these challenges - a concept we discuss as digital resilience. The intervention was co-designed with the non-profit organization My Digital Tat2 and administered to 566 elementary school students. Results showed that the intervention improved students’ digital skills, self-efficacy with technology, intentions of being an upstander to support peers online, and willingness to seek help for difficult situations from trusted adults. Compared to their baseline, students were not only better prepared to evaluate digital information and stand up for themselves online, but also more willing to give and receive support for digital challenges. Our results speak to the potential for research-practice partnerships to create developmentally appropriate interventions that equip students with the skills, self-efficacy, and support structures needed to harness the benefits of technologies, while being informed and resilient to its dangers.

Abstract Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide important information in experimental results. However, we find that analysis of metrics for performance evaluation and what they can hide or reveal is rarely covered in related works. Therefore, we address that gap by analyzing multiple popular performance metrics on three Big Data classification tasks. To the best of our knowledge, we are the first to utilize three new Medicare insurance claims datasets which became publicly available in 2021. These datasets are all highly imbalanced. Furthermore, the datasets are comprised of completely different data. We evaluate the performance of five ensemble learners in the Machine Learning task of Medicare fraud detection. Random Undersampling (RUS) is applied to induce five class ratios. The classifiers are evaluated with both the Area Under the Receiver Operating Characteristic Curve (AUC), and Area Under the Precision Recall Curve (AUPRC) metrics. We show that AUPRC provides a better insight into classification performance. Our findings reveal that the AUC metric hides the performance impact of RUS. However, classification results in terms of AUPRC show RUS has a detrimental effect. We show that, for highly imbalanced Big Data, the AUC metric fails to capture information about precision scores and false positive counts that the AUPRC metric reveals. Our contribution is to show AUPRC is a more effective metric for evaluating the performance of classifiers when working with highly imbalanced Big Data.

Over 2000 publications including more than 100 human studies seem to indicate that humans have only recently benefited from or known about the medical effects of H2 within the past 15 years. However, we have unknowingly benefited from H2 since the dawn of time, from H2-producing bacteria to the use of naturally occurring hydrogen-rich waters. Moreover, the first writings on the therapeutic effects of H2 date to around 1793. Since then, papers appeared sporadically in the literature every few decades but never exploded until Ohsawa et al. again demonstrated hydrogen’s therapeutic effects in 2007. This landmark paper appears to have been the spark that ignited the medical interest in hydrogen. Although H2 was used in the 1880s to locate intestinal perforations, in the 1940s in deep sea diving, and in the 1960s to measure blood flow, H2 was largely viewed as biologically inert. This review highlights the history of hydrogen in the genesis/evolution of life and its medicinal and non-medicinal use in humans. Although hydrogen medicine has a long and erratic history, perhaps future history will show that, this time around, these 15 years of ignited interest resulted in a self-sustaining explosion of its unique medical effects.

In 2007 a paper in Nature Medicine sparked a new wave of interest in the use of molecular hydrogen (H2) for medical treatments. Since then there has been a flurry of papers looking at a range of medical aspects, from neurodegenerative disease to sports injuries. Several methods of application have been developed, including breathing the gas, or making a hydrogen-rich solution that can be ingested, or administered as saline. H2 treatments are deemed to be safe and can be used in agricultural practice as well as in the biomedical arena. However, the first studies to investigate the use of H2 in medicine were carried out by those interested in the use of a range of gases and how this may affect respiration in humans. Beddoes was published in 1793 and Cavallo in 1798, with Davy following shortly after in 1800. With so many papers now appearing on H2 in biological systems, it seems timely, and interesting, to revisit the early papers, to humbly remind us of what these pioneers of respiratory research did.

Molecular hydrogen (H2) is a low-molecular-weight, non-polar and electrochemically neutral substance that acts as an effective antioxidant and cytoprotective agent, with research into the effects of H2 incorporation into the food chain, at various stages, rapidly gaining momentum. H2 can be delivered throughout the food growth, production, delivery and storage systems in numerous ways, including as a gas, as hydrogen-rich water (HRW), or with hydrogen-donating food supplements such as calcium (Ca) or magnesium (Mg). In plants, H2 can be exploited as a seed-priming agent, during seed germination and planting, during the latter stages of plant development and reproduction, as a post-harvest treatment and as a food additive. Adding H2 during plant growth and developmental stages is noted to improve the yield and quality of plant produce, through modulating antioxidant pathways and stimulating tolerance to such environmental stress factors as drought stress, enhanced tolerance to herbicides (paraquat), and increased salinity and metal toxicity. The benefits of pre- and post-harvest application of H2 include reductions in natural senescence and microbial spoilage, which contribute to extending the shelf-life of animal products, fruits, grains and vegetables. This review collates empirical findings pertaining to the use of H2 in the agri-food industry and evaluates the potential impact of this emerging technology.

Hydrogen gas (molecular hydrogen, H2) has significant effects in a range of organisms, from plants to humans. Many inert gases have been reported to have similar effects, and such responses may be most pronounced when cells are stressed. Xenon (Xe), for example, is a well-known anesthetic. The direct targets of these gases, in most cases, remain elusive. Myoglobin and hemoglobin are known for their roles in the transport of gases through coordinate interactions with metals (O2, NO, CO) and covalent modifications of thiols (NO, H2S) and amines (CO2). These are well exemplified in biotrophic reactions of NO with heme iron (to form iron nitrosyl heme) and cysteine (to form bioactive S-nitrosothiols) essential for tissue oxygenation. Here, we consider an alternative “third mode” of gas transport in what have been dubbed “Xenon pockets”, whereby inert gases may have functional effects. Many proteins have similar cavities, and possible effects include alterations in allosteric properties of proteins (potentially altering protein hydration). Here, it is suggested that similar to other inert gases, H2 also has biological effects by utilizing these protein structures. This ought to be investigated further, in a range of species, to determine if this is the mode of action of H2.

In this paper, we develop the concept of agentic social media use: a way of engaging with social media that emphasizes having the beliefs, knowledge, and practices to use it intentionally. In comparison to instances of “mindless” social media use, people who use social media agentically do so with a purpose in mind: they leverage the affordances of social media to do things that are meaningful, useful, or satisfying for them. For example, people can use social media to intentionally build or manage their relationships, to seek out and learn new information about their interests, or to craft a positive image of themselves through the content they post. Crucially, however, there are many other valuable uses of social media that may not be considered conventionally productive but are nonetheless deliberate and useful, such as using social media intentionally to relax, unwind, and entertain themselves in an effort to modulate their emotions. To use social media agentically means to (1) hold an agentic mindset about one's relationship with social media, (2) have the knowledge and literacy to understand how to navigate social media effectively, and (3) enact practices that assert control over specific elements of social media use, such as curating content and refining algorithmic recommendation. Approaching social media use from the perspective of agency and intentionality allows us to better understand heterogeneous social media effects and to identify new ways of helping people benefit from these technologies.