Cumulative Impact: Digital Ethnography of Environmental Activism in the Mountain View Community

While digital ethnography is a growing genre in communication, there is a salient need to fill the gaps of knowledge concerning health communication using a digital format. Digital ethnography holds the potential of reaching larger audiences, incorporating more stakeholders, and adding previously muted voices – individuals from non-academic communities as well as communities of color – to health research dialectics. With limited use of this tool in the field of health communication, there remain untapped opportunities for intra-disciplinary work within the communication field (e.g., ethnography, performance ethnography, critical ethnography) and beyond. By combining ethnography and community-based participatory research as theoretical frameworks and digital ethnography as an approach, seminal opportunities may be discovered in understanding the role of environmental discourse in human behavior, promoting higher levels of community participation, engaging those who are most greatly impacted by environmental issues, and fostering positive social change. The purpose of this study is three-fold: (1) to document on-going efforts of Mountain View community leaders and residents to survive, cope with, and remediate environmental damage resulting from the hazard waste siting in their area of 33 out of 35 EPA sites; (2) to explore lives, unique culture, and continuing activism of Mountain View residents, as they seek to construct a reality that transcends their being targeted as a dumping ground for environmental pollutants; and (3) to encourage social action by offering ways in which people can not only procure knowledge and empowerment regarding environmental threats, but also pursue practical responses to alleviate them. The Mountain View neighborhood has long battled against environmental injustice. While the EPA Office of Justice (2015) suggests that no group of people should bear disproportionate shares of environmental threats and promotes environmental justice through the fair treatment and meaning involvement of all people groups, Mountain View is one New Mexican community where residents continue to experience unequal protection against environmental hazards. This Cumulative Impact Project focuses upon on the 35-year period of Mountain View history beginning in the early 1980’s when community organizing efforts became increasingly galvanized. When a community infant was poisoned and hospitalized with “blue baby syndrome,” a medical condition that causes an infant’s hands, feet, nails, and skin to become bluish as a result of the blood’s reduced ability to carry oxygen, community residents were in an uproar over the threat of potential suffocation of the area’s most vulnerable citizens

What nonpharmacological interventions are effective for treating fatigue in adolescents? A systematic review of randomised controlled trials

Poster presentation for the PsyPAG 2021 Conferenc

The welfare consequences and efficacy of training pet dogs with remote electronic training collars in comparison to reward based training

This study investigated the welfare consequences of training dogs in the field with manually operated electronic devices (e-collars). Following a preliminary study on 9 dogs, 63 pet dogs referred for recall related problems were assigned to one of three Groups: Treatment Group A were trained by industry approved trainers using e-collars; Control Group B trained by the same trainers but without use of e-collars; and Group C trained by members of the Association of Pet Dog Trainers, UK again without e-collar stimulation (n = 21 for each Group). Dogs received two 15 minute training sessions per day for 4-5 days. Training sessions were recorded on video for behavioural analysis. Saliva and urine were collected to assay for cortisol over the training period. During preliminary studies there were negative changes in dogs' behaviour on application of electric stimuli, and elevated cortisol post-stimulation. These dogs had generally experienced high intensity stim uli without pre-warning cues during training. In contrast, in the subsequent larger, controlled study, trainers used lower settings with a pre-warning function and behavioural responses were less marked. Nevertheless, Group A dogs spent significantly more time tense, yawned more often and engaged in less environmental interaction than Group C dogs. There was no difference in urinary corticosteroids between Groups. Salivary cortisol in Group A dogs was not significantly different from that in Group B or Group C, though Group C dogs showed higher measures than Group B throughout sampling. Following training 92% of owners reported improvements in their dog's referred behaviour, and there was no significant difference in reported efficacy across Groups. Owners of dogs trained using e-collars were less confident of applying the training approach demonstrated. These findings suggest that there is no consistent benefit to be gained from e-collar training but greater welfare concer! ns compared with positive reward based training

Using Glucan Water Dikinase for in vitro glucan phosphorylation

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Čimbenici koji utječu na sadržaj kolesterola u mlijeku krava hranjenih konzerviranim krmivima TMR sustavom tijekom godine

The aim of this study was to evaluate changes in the cholesterol content in the milk of high yielding cows fed a uniform diet composed of conserved feeds over the whole year. The investigations were conducted on 124 Polish Holstein-Friesian cows, selected fromherd yielding 8457 kg milk with 4,58 % and 3,56 % of fat and protein content, respectively. The cows were maintained in a loose barn and fed ad libitum with TMR (total mixed ration) throughout the year. The diets consisted of corn silage and grass silage (at 50:50 ration on dry matter basis) and concentrates with mineral-vitamin mixture additives. Samples of milk were collected individually from each cow at monthly intervals during the whole year. The cholesterol content in milk (mg/dL) and in milk fat (mg/g) was related to the stage of lactation, season of the year, somatic cell count and fat content, but was not affected by the parity. The cholesterol content in daily milk yield (mg/cow/day) depended also on parity. Even though the cows were fed a uniform diet throughout the year according to the TMR system the cholesterol content in milk differed among seasons.Cilj ovog istraživanja bio je ispitati promjene udjela kolesterola u visokomliječnih krava hranjenih obrokom sastavljenim od konzerviranih krmiva tijekom cijele godine. Istraživanja su provedena na 124 holstein-frizijske krave, odabrane iz stada koje proizvode 8457 kg mlijeka s 4,58 % mliječne masti i 3,56 % proteina, respektivno. Krave su držane u otvorenom tipu staja i hranjene ad libitum s potpuno izmiješanim obrokom (TMR, total mixed ration) tijekom cijele godine. Obrok se sastojao od kukuruzne i travne silaže (50:50, na bazi suhe tvari) i koncentrata s mineralno-vitaminskim dodacima. Uzorci mlijeka prikupljani su pojedinačno od svake krave u mjesečnim intervalima tijekom cijele godine. Sadržaj kolesterola u mlijeku (mg/dL) i mliječnoj masti (mg/g) u vezi je sa stadijem laktacije, godišnjim dobom, brojem somatskih stanica i udjelom masti, ali nije bio pod utjecajem redoslijeda laktacije. Sadržaj kolesterola u dnevnoj količini mlijeka (mg/kravi/dan) ovisi također o redoslijedu laktacije. Unatoč hranidbi krava uniformnom prehranom tijekom cijele godine TMR sustavom, sadržaj kolesterola u mlijeku mijenjao se pod utjecajem sezone

On Computing Probabilistic Abductive Explanations

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.Comment: arXiv admin note: text overlap with arXiv:2207.04748, arXiv:2205.0956