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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.
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.
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.
The development of base metal electrodes that can act as active and stable oxygen generating electrodes in water electrolysis systems, especially at low pH levels, remains a challenge. The use of suspensions as electrolytes for water splitting has until recently been limited to photoelectrocatalytic approaches. A high current density (j=30 mA/cm2) for water electrolysis has been achieved at a very low oxygen evolution reaction (OER) potential (E=1.36 V vs. RHE) using a SnO2/H2SO4 suspension-based electrolyte in combination with a steel anode. More importantly, the high charge-to-oxygen conversion rate (Faraday efficiency of 88% for OER at j=10 mA/cm2 current density). Since cyclic voltammetry (CV) experiments show that oxygen evolution starts at a low, but not exceptionally low, potential, the reason for the low potential in chronoamperometry (CP) tests is an increase in the active electrode area, which has been confirmed by various experiments. For the first time, the addition of a relatively small amount of solids to a clear electrolyte has been shown to significantly reduce the overpotential of the OER in water electrolysis down to the 100 mV region, resulting in a remarkable reduction in anode wear while maintaining a high current density.
The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism, and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS-based synchronization model of INET so that it works over UDP/IP. With that, it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds of synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Furthermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.
Introduction: Patients undergoing revision total hip surgery (RTHS) have a high prevalence of mild and moderate preoperative anemia, associated with adverse outcomes. The aim of this study was to investigate the association of perioperative allogeneic blood transfusions (ABT) and postoperative complications in preoperatively mild compared to moderate anemic patients undergoing RTHS who did not receive a diagnostic anemia workup and treatment before surgery. Methods: We included 1,765 patients between 2007 and 2019 at a university hospital. Patients were categorized according to their severity of anemia using the WHO criteria of mild, moderate, and severe anemia in the first Hb level of the case. Patients were grouped as having received no ABT, 1–2 units of ABT, or more than 2 units of ABT. Need for intraoperative ABT was assessed in accordance with institutional standards. Primary endpoint was the compound incidence of postoperative complications. Secondary outcomes included major/minor complications and length of hospital and ICU stay. Results: Of the 1,765 patients, 31.0% were anemic of any cause before surgery. Transfusion rates were 81% in anemic patients and 41.2% in nonanemic patients. The adjusted risks for compound postoperative complication were significantly higher in patients with moderate anemia (OR 4.88, 95% CI: 1.54–13.15, p = 0.003) but not for patients with mild anemia (OR 1.93, 95% CI: 0.85–3.94, p < 0.090). Perioperative ABT was associated with significantly higher risks for complications in nonanemic patients and showed an increased risk for complications in all anemic patients. In RTHS, perioperative ABT as a treatment for moderate preoperative anemia of any cause was associated with a negative compound effect on postoperative complications, compared to anemia or ABT alone. Discussion: ABT is associated with adverse outcomes of patients with moderate preoperative anemia before RTHS. For this reason, medical treatment of moderate preoperative anemia may be considered.
Background
Beta-blocker (BB) therapy plays a central role in the treatment of cardiovascular diseases. An increasing number of patients with cardiovascular diseases undergoe noncardiac surgery, where opioids are an integral part of the anesthesiological management. There is evidence to suggest that short-term intravenous BB therapy may influence perioperative opioid requirements due to an assumed cross-talk between G-protein coupled beta-adrenergic and opioid receptors. Whether chronic BB therapy could also have an influence on perioperative opioid requirements is unclear.
Methods
A post hoc analysis of prospectively collected data from a multicenter observational (BioCog) study was performed. Inclusion criteria consisted of elderly patients (≥ 65 years) undergoing elective noncardiac surgery as well as total intravenous general anesthesia without the use of regional anesthesia and duration of anesthesia ≥ 60 min. Two groups were defined: patients with and without BB in their regular preopreative medication. The administered opioids were converted to their respective morphine equivalent doses. Multiple regression analysis was performed using the morphine-index to identify independent predictors.
Results
A total of 747 patients were included in the BioCog study in the study center Berlin. 106 patients fulfilled the inclusion criteria. Of these, 37 were on chronic BB. The latter were preoperatively significantly more likely to have arterial hypertension (94.6%), chronic renal failure (27%) and hyperlipoproteinemia (51.4%) compared to patients without BB. Both groups did not differ in terms of cumulative perioperative morphine equivalent dose (230.9 (BB group) vs. 214.8 mg (Non-BB group)). Predictive factors for increased morphine-index were older age, male sex, longer duration of anesthesia and surgery of the trunk. In a model with logarithmised morphine index, only gender (female) and duration of anesthesia remained predictive factors.
Conclusions
Chronic BB therapy was not associated with a reduced perioperative opioid consumption.
The development of non-precious metal-based electrodes that actively and stably support the oxygen evolution reaction (OER) in water electrolysis systems remains a challenge, especially at low pH levels. The recently published study has conclusively shown that the addition of haematite to H2 SO4 is a highly effective method of significantly reducing oxygen evolution overpotential and extending anode life. The far superior result is achieved by concentrating oxygen evolution centres on the oxide particles rather than on the electrode. However, unsatisfactory Faradaic efficiencies of the OER and hydrogen evolution reaction (HER) parts as well as the required high haematite load impede applicability and upscaling of this process. Here it is shown that the same performance is achieved with three times less metal oxide powder if NiO/H2 SO4 suspensions are used along with stainless steel anodes. The reason for the enormous improvement in OER performance by adding NiO to the electrolyte is the weakening of the intramolecular O─H bond in the water molecules, which is under the direct influence of the nickel oxide suspended in the electrolyte. The manipulation of bonds in water molecules to increase the tendency of the water to split is a ground-breaking development, as shown in this first example.
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.
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.