Electrical doping of charge carrier injection and extraction layers for solution processed organic optoelectronic devices

In this work different charge transport materials are p- and n-doped by different doping reactions, processed from solution. The doping and its efficiency is characterized by different methods. Additionally crosslinkable polymers are p-doped and compared with their corresponding non-crosslinkable polymeric and low-molecular counterpart

“At first, I was only a subscriber”: re-mediating food citizens’ solidarity practices through digital technologies

In this paper, we explore how digital technologies re-mediate solidarity practices in alternative food networks (AFNs). To do so, the first author conducted an 8-month (auto-)ethnography of a community supported agriculture (CSA) initiative in Switzerland and 12 semi-structured interviews with CSA members. We identified three types of solidarity practices in our analysis that aim to support social inclusiveness, increase responsibility and sustainability, and foster the sharing of risk, work and infrastructure amongst CSA members. Digital technologies are central for joining and becoming a member of the CSA and also play a vital role in sharing information and organizing members’ work assignments. By becoming a member, consumers become subscribers voting with their wallet. If they regularly engage in farm work, they become prosumers or co-producers. Thus, our analysis foregrounds the continuum of food citizenship in the CSA we studied. However, the number of subscribers increases through digital technologies, transforming the initiative from an alternative to the market to an alternative within the market, whereby certain aspects of solidarity, such as social inclusiveness and sharing, are not realized anymore. Our study contributes to the emerging field of digital food studies by showing how solidarity is digitally enabled and negotiated in CSA, and how this shapes food citizenship

Towards automatic data extraction from clinical research reports: A case study of a systematic review of oral pain relief

In healthcare, it takes a long time for new treatments to move from clinical studies into practice: perhaps an average of 17 years [Balas et al., 2000]. Systematic review is a critical step in this research translation process because it determines what is known. To do this, a systematic review analyzes all available evidence on a particular question through a series of steps, including data extraction. The current best practice for data extraction is for two people to independently identify and extract data from each research paper. Because the data extraction step is almost always performed manually, it is very time-consuming [Tsafnat et al., 2014] yet methodological errors may cause problems with the review's conclusions [Lundh et al., 2009]. Our long-term goal is to help reviewers synthesize the literature quickly and accurately by developing a semi-automatic support system for data extraction. Towards this end, we are currently conducting an in-depth case study of a single systematic review, a Cochrane Review about oral pain relief. Through manual annotation and a content analysis of the six studies synthesized by this Cochrane Review, we will develop hypotheses about which clinical data elements can be automatically extracted. We will also develop an annotated corpus which will enable us to propose methods for automatically supporting human reviewers in data extraction. Eventually, we plan to design a semi-automated support system, and to test the two hypotheses (1) that it can reduce the time and human labor required to conduct a review and (2) that it can maintain or increase the quality of the resulting review.Ope

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Humans spend most of their lives indoors, so indoor air quality (IAQ) plays a key role in human health. Thus, human health is seriously threatened by indoor air pollution, which leads to 3.8 × 106 deaths annually, according to the World Health Organization (WHO). With the ongoing improvement in life quality, IAQ monitoring has become an important concern for researchers. However, in machine learning (ML), measurement uncertainty, which is critical in hazardous gas detection, is usually only estimated using cross-validation and is not directly addressed, and this will be the main focus of this paper. Gas concentration can be determined by using gas sensors in temperature-cycled operation (TCO) and ML on the measured logarithmic resistance of the sensor. This contribution focuses on formaldehyde as one of the most relevant carcinogenic gases indoors and on the sum of volatile organic compounds (VOCs), i.e., acetone, ethanol, formaldehyde, and toluene, measured in the data set as an indicator for IAQ. As gas concentrations are continuous quantities, regression must be used. Thus, a previously published uncertainty-aware automated ML toolbox (UA-AMLT) for classification is extended for regression by introducing an uncertainty-aware partial least squares regression (PLSR) algorithm. The uncertainty propagation of the UA-AMLT is based on the principles described in the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements. Two different use cases are considered for investigating the influence on ML results in this contribution, namely model training with raw data and with data that are manipulated by adding artificially generated white Gaussian or uniform noise to simulate increased data uncertainty, respectively. One of the benefits of this approach is to obtain a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision. Finally, an increase in robustness against random noise by training a model with noisy data is demonstrated

Mobile Activism, Material Imaginings, and the Ethics of the Edible: Framing Political Engagement through the Buycott App

In this article, we explore the discursive constructions of Buycott, a free mobile app that provides a platform for user-generated ethical consumption campaigns. Unlike other ethical consumption apps, Buycott’s mode of knowledge production positions the app itself as neutral, with app users generating activist campaigns and providing both data and judgment. Although Buycott is not a dedicated food activism app, food features centrally in its campaigns, and the app seems to provide a mobile means of extending, and perhaps expanding, alternative food network (AFN) action across geographies and constituencies. Thus, as a case study, Buycott unveils contemporary possibilities for citizen participation and the formation of activist consumer communities, both local and trans-national, through mobile technologies. Our analysis shows, however, that despite the app’s user-generated format, the forms of activism it enables are constrained by the app’s binary construction of action as non/consumption and its guiding ‘mission’ of ‘voting with your wallet’. Grounded in texts concerning Buycott’s two largest campaigns (Demand GMO Labeling and Long live Palestine boycott Israel), our analysis delineates how Buycott, its campaigns, and its modes of action take shape in user, media, and app developer discourses. We find that, as discursively framed, Buycott campaigns are commodity-centric, invoking an ‘ethics of care’ to be enacted by atomized consumers, in corporate spaces and through mainstream, barcode-bearing, retail products. In user discourses, this corporate spatiality translates into the imagined materializing of issues in products, investing commodities with the substance of an otherwise ethereal cause. This individualized, commodity-centric activism reinforces tenets of the neoliberal market, ultimately turning individual users into consumers not only of products, but also of the app itself. Thus, we suggest, the activist habitus constructed through Buycott is a neoliberal, consumer habitus

Does intraoperative bone density testing correlate with parameters of primary implant stability? A pilot study in minipigs

Objectives: Bone density, surgical protocol, and implant design are the major deter-minants of primary stability. The goal of this animal trial was to investigate potentialcorrelations of intraoperative bone density testing with clinical and histologic param-eters of primary implant stability.Material and methods: Following extractions of all mandibular premolars and subse-quent healing, four implants each were placed in a total of four minipigs. Bone den-sity was determined by applying intraoperative compressive tests using a devicenamed BoneProbe whereas measurements of implant insertion torque and resonancefrequency analysis were used for evaluating implant stability. Bone mineral density(BMD) and bone to implant contact were quantified after harvesting mandibularblock sections. Spearman rank correlation tests were performed for evaluating corre-lations (α = .05).Results: Due to variation in clinical measurements, only weak correlations could beidentified. A positive correlation was found between the parameters bone to implantcontact and BMD (Spearman's rho .53; p=.05) whereas an inverse correlation wasobserved between BMD and implant stability (Spearman's rho −.61; p=.03). BothBoneProbe measurements in the cortical and trabecular area positively correlatedwith implant insertion torque (Spearman's rho 0.60; p=.02). A slightly stronger corre-lation was observed between the average of both BoneProbe measurements andimplant insertion torque (Spearman's rho.66; p=.01).Conclusions: While establishing exact relationships among parameters of implant sta-bility and the measurement techniques applied would require greater sample size,intraoperative compressive testing of bone might, despite the weak correlations seenhere, be a useful tool for predicting primary implant stability

Система обработки данных по оптическим и микрофизическим характеристикам аэродисперсной среды для оценки ослабления лучистой энергии

Разработана информационно-вычислительная система, реализующая численный эксперимент по определению ослабления и пропускания оптического излучения аэродисперсной средой. В качестве моделей отдельных рассеивателей рассмотрены столбики, пластинки, сферы, а также их агрегатов. В систему включаются архивы баз данных Aeronet и Hitran оптических и микрофизических характеристик кристаллических облаков. Программный комплекс ориентирован на обработку данных по ослаблению видимого и ИК излучения. Сравнительный анализ данных численного и натурного экспериментов показал возможность оценки физико-химических параметров среды

The digital labor of ethical food consumption : a new research agenda for studying everyday food digitalization

This paper explores how consumers’ ethical food consumption practices, mediated by mobile phone applications (apps), are transformed into digital data. Based on a review of studies on the digitalization of ethical consumption practices and food apps, we find that previous research, while valuable, fails to acknowledge and critically examine the digital labor required to perform digitalized ethical food consumption. In this paper, we call for research on how digital labor underlies the digitalization of ethical food consumption and develop a conceptual framework that supports this research agenda. Our proposed conceptual framework builds on three interconnected analytical concepts—datafication, affordances and digital labor—that enable the study of digital labor as an infrastructural element of digitalized food consumption. We illustrate our conceptual framework through our previous research concerning Buycott, a US-based mobile app whose stated aim is to facilitate consumers’ ethical purchasing decisions. Using the walkthrough method, we consider how the Buycott app engages user-generated data and what implications this holds for consumers. The app’s infrastructure, we suggest, connects ethical consumption and digital labor. A richer understanding of the digital food economy, we propose, enables social scientists not only to elucidate how consumers engage in digital labor, but also to contribute to the development of new data governance structures in the digital food economy. We therefore call for social scientists interested in food, consumption and the digital economy to contribute to a new research agenda for studying everyday food digitalization by empirically examining how ethical consumption apps implicate ethical consumers’ work