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Bullshit, as defined by Frankfurt (2005, p. 10), is language that is “disconnected from a concern for the truth.” Much scholarship shows that bullshit is a prominent feature in organizations that is difficult, if not impossible, to get rid of (e.g., McCarthy et al., 2020; Penny, 2010). Bullshit, by definition and by cultural practice, seems antithetical to business writing orthodoxy. As Thill and Bovée (2020) suggest in a representative textbook, communication should be clear and ethical. However, Spicer (2020) codifies bullshit as a social practice whose outcomes are not always dire. Well-crafted bullshit benefits its users, allowing them to “fit into a speech community, get things done in day-to-day interaction and bolster their image and identity” (Spicer, 2020, p. 20). Contrasting with business writing’s abstinence-only bullshit stance, this suggests that successful writers must adapt to their organization’s speech act practices. In this article, we argue that students must be taught about bullshit. After describing bullshit and its role in organizations, we show how business writing could incorporate a critically informed approach to bullshit in undergraduate courses, internship preparation courses, and other curricular instances in which students work directly with organizations. While bullshitting should not be outright encouraged, continued ignorance will do nothing to solve its associated problems. Promoting bullshit literacy, however, could both minimize bullshit’s harms and maximize its benefits. We close by describing how this approach could foster critical thinking skills, promote more seamless adaptation to organizational cultures and communication practices, and perhaps even improve mental health outcomes.

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic’s short-term example.

Moderate-intensity physical activity is recommended for inactive adults with overweight/obesity (OW/OB). The objective of this study is to determine if differences exist in the selection of moderate intensity between inactive adults with juvenile-onset (JO) and adult-onset (AO) OW/OB. Participants (JO = 18, AO = 20) were stratified by onset and completed two separate 20-minute moderate-intensity exercise sessions on a treadmill and cycle ergometer (randomized order). Multiple linear regression was used to determine whether exercise intensity (average METS, % age-predicted HRmax), self-reported pleasure or exertion differed by onset, controlling for age and gender. On the treadmill, JO and AO participants selected an average intensity of (mean [SD]) 3.5 (0.9) and 3.7 (0.9) METS, and 64.0 (7.7) and 64.9 (7.5) % of age-predicted HRmax, respectively. On the cycle, JO and AO participants selected an average intensity of 3.3 (0.9) and 3.3 (1.0) METS, and 65.2 (8.8) and 60.7 (7.2) % of age-predicted HRmax. After adjustment, participant intensity selection did not significantly differ by obesity onset when walking or cycling. There were no significant differences in pleasure or perceived exertion by onset, however, perception of exertion was on the high-end of moderate for both the cycle (13.0, 12.5) and treadmill (12.0, 12.1), in JO and AO participants, respectively. Perception of moderate intensity did not differ by obesity onset. Self-selected intensity was at the low end of moderate for walking and cycling.

This work explores using Probabilistic Context Free Grammars and Artificial Neural Networks as possible machine learning models for classifying introns into major and minor introns. It presents an intron classification framework that combines probabilistic context free grammars and support vector machines. It also assesses the computational prediction power of these two models in comparison to the Position Weight Matrices technique, which is currently the exclusively used model for intron classification. The comparison is done through experimental analysis, and it shows promising results for Probabilistic Context Free Grammars and Artificial Neural Networks. © 2022 IEEE.

Purpose: This study aimed to examine the ways in which physical education teacher education (PETE) prepares preservice physical education teachers (PPETs) to select and implement appropriate assessments.Methods: PPETs (N = 14) enrolled in the secondary teaching methods course at two US universities participated in the study. Semi-structured interviews were completed to collect data concerning how assessment knowledge and skills were taught and learned. Constant content comparison method was used to analyze the data.Results: Two major themes with varying sub-themes emerged from the data: ‘Scratching the surface of assessment with unclear learning objectives’, and ‘Perceiving the importance of assessment, but still not integrate it into instruction’ Overall, assessment was not found to conjunctionally taught with instruction. School-based field experiences pertaining to assessment content and pedagogical knowledge were also weak.Conclusions: Minimum assessment knowledge and skills were taught in secondary methods courses with little field experience pertaining to assessment. Future research is needed on examining PETE program content and pedagogy courses to highlight the need for assessment instruction and transform our approaches to preparing PPETs.

We show that overconfident individuals are likely to be arrested for public intoxication by using arrest records from a university town police log. This relationship is robust to various control variables such as risk aversion, time discounting, present bias, self-control, selfishness, loss aversion, and socializing with peers arrested for public intoxication. However, this relationship is no longer significant using only self-reported arrest data. We hypothesize that overconfident individuals are likely to underreport their arrests. This result has important implications for the use of self-reported data on public intoxication arrests rather than actual arrest records.

In collaboration with members of the transgender and gender diverse (TGD) community, we created a didactic resource about the unique needs of TGD youth.

Organizations strive to motivate employees to thrive at work. However, employees’ motivation is likely to vary over a short period (e.g., a few months) to cope with the routine dynamics of organizations’ activities. These motivation dynamics covary with employees’ affective, cognitive, and behavioral outcomes in the workplace. Moreover, employees’ psychological health, a multidimensional concept focused on the individual’s well/ill-being simultaneously, changes over time. Using the integrated theoretical frameworks of self-determination theory (SDT) and the hierarchical model of self-determined motivation (H-SDT), this research sought to examine the motivational changes following the dual-path model. In particular, this work sought to unpack the temporal dynamics in employees’ subjective well/ill-beings predicted by the changes in basic needs satisfaction/frustration through autonomous/controlled motivation, while considering the characteristics of people’s general causality orientations (trait-level motivation). Over four months, longitudinal field data were collected from the employees in several private small businesses in the consumer product retail industry. Latent growth modeling (LGM) results supported the positive dual relations between the changes in employees’ psychological health and basic psychological needs satisfaction/frustration, but neither the changes of autonomous/controlled work motivation nor the indirect change paths via autonomous/controlled work motivation were significant. Finally, we discussed the theoretical and practical implications of the findings. Limitations and possible future research directions to further this line of research on the dynamic of work motivation were also summarized.

Elliptic flow measurements from two-, four-, and six-particle correlations are used to investigate flow fluctuations in collisions of U+U at √sNN=193 GeV, Cu+Au at √sNN=200 GeV and Au+Au spanning the range √sNN=11.5–200 GeV. The measurements show a strong dependence of the flow fluctuations on collision centrality, a modest dependence on system size, and very little if any, dependence on particle species and beam energy. The results, when compared to similar LHC measurements, viscous hydrodynamic calculations, and trento model eccentricities, indicate that initial-state-driven fluctuations predominate the flow fluctuations generated in the collisions studied.

The green, sustainable, and inexpensive creation of novel materials, primarily nanoparticles, with effective energy-storing properties, is key to addressing both the rising demand for energy storage and the mounting environmental concerns throughout the world. Here, an orange peel extract is used to make cobalt oxide nanoparticles from cobalt nitrate hexahydrate. The orange peel extract has Citrus reticulata, which is a key biological component that acts as a ligand and a reducing agent during the formation of nanoparticles. Additionally, the same nanoparticles were also obtained from various precursors for phase and electrochemical behavior comparisons. The prepared Co-nanoparticles were also sulfurized and phosphorized to enhance the electrochemical properties. The synthesized samples were characterized using scanning electron microscopic and X-ray diffraction techniques. The cobalt oxide nanoparticle showed a specific capacitance of 90 F/g at 1 A/g, whereas the cobalt sulfide and phosphide samples delivered an improved specific capacitance of 98 F/g and 185 F/g at 1 A/g. The phosphide-based nanoparticles offer more than 85% capacitance retention after 5000 cycles. This study offers a green strategy to prepare nanostructured materials for energy applications.

Emerging literature on coastal transitions in the face of the climate crisis establishes a need for identifying appropriate stakeholder engagement processes for

As anthropogenic impacts such as climate change ensue, coastal regions become increasingly threatened. Transdisciplinary action research (TAR) emerged as a

Offshore renewable, or Blue Energy, installations are a crucial component of many countries’ energy policies and pathways towards a sustainable low-carbon

This book presents state-of-the-art perspectives on the Blue Economy. It applies important geographical and sustainability transitions perspectives and

The recent attention on Blue Carbon (BC) signals its importance in the burgeoning Blue Economy discourse. BC has traditionally referred to carbon that is

Given commutative, unital rings $$\mathcal {A}$$and $$\mathcal {B}$$with a ring homomorphism $$\mathcal {A}\rightarrow \mathcal {B}$$making $$\mathcal {B}$$free of finite rank as an $$\mathcal {A}$$-module, we can ask for a “trace” or “norm” homomorphism taking algebraic data over $$\mathcal {B}$$to algebraic data over $$\mathcal {A}$$. In this paper we we construct a norm functor for the data of a quadratic algebra: given a locally-free rank-2 $$\mathcal {B}$$-algebra $$\mathcal {D}$$, we produce a locally-free rank-2 $$\mathcal {A}$$-algebra $$\textrm{Nm}_{\mathcal {B}/\mathcal {A}}(\mathcal {D})$$in a way that is compatible with other norm functors and which extends a known construction for étale quadratic algebras. We also conjecture a relationship between discriminant algebras and this new norm functor.

Objective: Recent epidemiological research has found food insecurity to be a growing public health concern among college students. This study investigated food insecurity, mental health, and substance use behaviors among state university undergraduate students. Study Design: Cross-sectional survey. Methods: This was a secondary data analysis of the Student Health Survey. Undergraduate participants ( n = 589) completed the paper-based survey, which had an 84% response rate overall. Results: Approximately 38.5% of students were considered food insecure, 24.8% experiencing low food security, and 13.8% experiencing very low food security. Having a diagnosis of depression, experience of depressive symptoms, and marijuana use in the past 30 days were associated with food insecurity. Conclusion and Implications: Food insecurity is a serious health concern for college students. The results of this study indicate collocating food security and counseling services may enhance existing student resources to better support students facing food and nutrition insecurity, substance use, and depression.

BACKGROUND: Growing evidence supports the superior benefits of exposure to mother's own milk (MOM) in reducing prematurity-related comorbidities. Neonatal exposure to donor human Milk (DHM) is a suitable alternative when MOM is insufficient or unavailable. However, the same protective composition and bioactivity in MOM are not present in DHM. Additional evidence is needed to justify and inform evidence-based practices increasing MOM provision while optimizing adequate use of DHM for premature infants. PURPOSE: A systematic review of the literature was conducted to determine differences in neonatal outcomes among premature infants exposed to predominately MOM versus DHM. METHODS/SEARCH STRATEGY: Databases including PubMed, CINAHL and Cochrane were searched (2020-2021) using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) guidelines. Evidence was classified using the John Hopkins evidence-based practice levels and quality of evidence. RESULTS: Eleven studies met inclusion criteria. Studied neonatal outcomes included ( a ) growth parameters (n = 8), ( b ) neonatal morbidities (n = 6), and ( c ) gut microbiome (n = 4). Overall, evidence suggests DHM exposure is beneficial but not equivalent to MOM feeding. Compared with DHM, greater doses of MOM are ideal to enhance protection primarily related to infant growth, as well as gut microbiome diversity and richness. IMPLICATIONS FOR PRACTICE: Standardized and evidence-based practices are needed to clearly delineate optimal use of DHM without undermining maternal and neonatal staff efforts to support and promote provision of MOM. IMPLICATIONS FOR RESEARCH: Additional evidence from high-quality studies should further examine differences in neonatal outcomes among infants exposed to predominately MOM or DHM in settings using standardized and evidence-based feeding practices.