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Institute
While the topic of artificial intelligence (AI) in multinational enterprises has been receiving attention for some time, small and medium enterprises (SMEs) have recently begun to recognize the potential of this new technology. However, the focus of previous research and AI applications has therefore mostly been on large enterprises. This poses a particular issue, as the vastly different starting conditions of various company sizes, such as data availability, play a central role in the context of AI. For this reason, our systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size. This allowed us to identify various business activities that have been scarcely considered. Simultaneously, it led to the discovery of a total of 27 different challenges perceived by SMEs in the adoption of AI. This enables SMEs to apply the identified challenges to their own AI projects in advance, preventing the oversight of any potential obstacles or risks. The lack of knowledge, costs, and inadequate infrastructure are perceived as the most common barriers to implementation, addressing social, economic, and technological aspects in particular. This illustrates the need for a wide range of support for SMEs regarding an AI introduction, which covers various subject areas, like funding and advice, and differentiates between company sizes.
Purpose
This study operationalizes risks in stakeholder dialog (SD). It conceptualizes SD as co-produced organizational discourse and examines the capacities of organizers' and stakeholders' practices to create a shared understanding of an organization’s risks to their mutual benefit. The meetings and online forum of a German public service media (PSM) organization were used as a case study.
Design/methodology/approach
The authors applied corpus-driven linguistic discourse analysis (topic modeling) to analyze citizens' (n = 2,452) forum posts (n = 14,744). Conversation analysis was used to examine video-recorded online meetings.
Findings
Organizers suspended actors' reciprocity in meetings. In the forums, topics emerged autonomously. Citizens' articulation of their identities was more diverse than the categories the organizer provided, and organizers did not respond to the autonomous emergence of contextualizations of citizens' perceptions of PSM performance in relation to their identities. The results suggest that risks arise from interactionally achieved occasions that prevent reasoned agreement and from actors' practices, which constituted autonomous discursive formations of topics and identities in the forums.
Originality/value
This study disentangles actors' practices, mutuality orientation and risk enactment during SD. It advances the methodological knowledge of strategic communication research on SD, utilizing social constructivist research methods to examine the contingencies of organization-stakeholder interaction in SD.
While recent studies have demonstrated that events are fundamentally climate sensitive, this seems to not be fully considered in event research or corporate event practice. Thus, this study aims to identify the influencing factors that affect the acceptance of climate adaptation measures among decision-makers in the event industry. The analysis was divided into three main parts. First, the existing literature related to climate change in an events context was reviewed. Using 15 semi structured interviews, the findings from this review were then critically discussed with stakeholders in Germany involved in event planning. Finally, explicit climate adaptation measures were proposed and discussed. Based on all findings, there appears to be a low level of awareness of and interest in climate adaptation amongst German event industry players. There is an imminent need for further research on climate adaptation and for decision-makers to better prepare for climate change in order to counteract resulting negative impacts.
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
Recording of Low-Oxygen Stress Response Using Chlorophyll Fluorescence Kinetics in Apple Fruit
(2023)
Long-term storage of apples (Malus x domestica, Borkh.) is increasingly taking place under Dynamic Controlled Atmosphere (DCA). The oxygen level is lowered to ≤ 1 kPa O2 and the apples are stored just above the Lower Oxygen Limit (LOL). Low oxygen stress during controlled atmosphere storage can lead to fermentation in apples if oxygen levels are too low. Chlorophyll fluorescence can be used to detect low-oxygen stress at an early stage during storage. The currently available non-imaging fluorescence systems often use the minimal fluorescence (Fo) parameter. In contrast, the use of chlorophyll fluorescence kinetics is insufficiently described. Therefore, this study aimed to gain more knowledge about the response of chlorophyll fluorescence kinetics to low oxygen stress in apples using a fluorescence imaging system. The results show that the kinetic fluorescence curves differ under aerobic and fermentation conditions. The fermentative conditions initiated a decrease in fluorescence intensity upon application of the saturation pulses during exposure to actinic light. This result was made at 18 °C and 2 °C ambient temperatures. Interestingly, the kinetic curve changed at 2 °C before fermentation products accumulated in the apples. Non-photochemical quenching (NPQ) decreased under fermentation conditions in the dark phase after relaxation. Upon entering the dark relaxation phase after Kautsky induction, ɸPSII began to increase. Under atmospheric oxygen conditions, ɸPSII reached values of 0.81 to 0.76, while under fermentation, ɸPSII values ranged from 0.57 to 0.44.
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Dairy farming has been the subject of public debate on animal welfare for a number of years now. Animal welfare discussions on dairy farming often include the demand for more nature connectedness in this area. This study focuses on the divergent perspectives of consumers and scientists on the importance of more nature connectedness for animal welfare strategies in German dairy farming. Within Europe, Germany is the main producer of cow’s milk and an important industry in many rural areas in Germany is dairy farming. The insights presented are based on qualitative interviews with dairy farming and livestock researchers from Germany and Austria. A key finding of this study is that we need to look more closely at the actual content of nature claims in animal welfare debates. The scientists interviewed tend to see idealized conditions in animal welfare discussions with images of nature which in fact seldom lead to improved conditions in dairy farming and, even then, only to a limited extent. The scientists interviewed rate calls for more nature connectedness in dairy farming from the nonagricultural public as anti-modern, complexity-reducing, and normative. Nevertheless, some of the scientists interviewed did have valuable insights into the nonagricultural public’s criticism of dairy farming practices. These scientists argued, however, that animal welfare needs to differentiate between nature connectedness and the innate needs of cattle when it comes to animal welfare strategies. An important conclusion of the study is that more discussion formats are needed to promote the exchange of ideas between different social groups attempting to understand animal welfare in dairy farming.
Iron deficiency is still widespread as a major health problem even in countries with adequate food supply. It mainly affects women but also vegans, vegetarians, and athletes and can lead to various clinical pictures. Biofortification of vitamin C-rich vegetables with iron may be one new approach to face this nutritional challenge. However, so far, little is known about the consumer acceptance of iron-biofortified vegetables, particularly in developed countries. To address this issue, a quantitative survey of 1000 consumers in Germany was conducted. The results showed that depending on the type of vegetable, between 54% and 79% of the respondents were interested in iron-biofortified vegetables. Regression analysis showed a relationship between product acceptance, gender, and area of residence. In addition, relationships were found between consumer preferences for enjoyment, sustainability, and naturalness. Compared to functional food and dietary supplements, 77% of respondents would prefer fresh iron-rich vegetables to improve their iron intake. For a market launch, those iron-rich vegetables appear especially promising, which can additionally be advertised with claims for being rich in vitamin C and cultivated in an environmentally friendly way. Consumers were willing to pay EUR 0.10 to EUR 0.20 more for the iron-biofortified vegetables.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Primary Liver Cancers : Connecting the Dots of Cellular Studies and Epidemiology with Metabolomics
(2023)
Liver cancers are rising worldwide. Between molecular and epidemiological studies, a research gap has emerged which might be amenable to the technique of metabolomics. This review investigates the current understanding of liver cancer’s trends, etiology and its correlates with existing literature for hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA) and hepatoblastoma (HB). Among additional factors, the literature reports dysfunction in the tricarboxylic acid metabolism, primarily for HB and HCC, and point mutations and signaling for CCA. All cases require further investigation of upstream and downstream events. All liver cancers reported dysfunction in the WNT/β-catenin and P13K/AKT/mTOR pathways as well as changes in FGFR. Metabolites of IHD1, IDH2, miRNA, purine, Q10, lipids, phosphatidylcholine, phosphatidylethanolamine, acylcarnitine, 2-HG and propionyl-CoA emerged as crucial and there was an attempt to elucidate the WNT/β-catenin and P13K/AKT/mTOR pathways metabolomically.