Purpose - The primary purpose of this paper is to investigate which internal organizational factors drive digital transformation for business model innovation in small and medium-sized enterprises (SMEs). Design/methodology/approach – The paper builds on a Delphi study from both a South African and Dutch scholarly and industry perspective. The Delphi study uses a questionnaire to query the commonly cited internal organizational factors that drive digital transformation for business model innovation in SMEs in an emerging and a developed economy. Findings – The paper identifies two important internal organizational factors driving digital transformation for business model innovation in SMEs: (a) a renewal of business strategy and (b) a renewal of business culture. It identifies various subfactors within these two internal organizational factors.   Originality/value – The study contributes to the current understanding of the internal organizational factors that drive digital transformation for business model innovation in SMEs. Researchers can use these factors and associated subfactors as a basis for future research. To practitioners, the findings provide a guideline on which business activities in SMEs to (re)arrange to enable digital transformation-induced business model innovation.

- The low recovery of oil (only one-third) is mainly related to the displacement efficiency of porous media, which is influenced by wettability and interfacial tension. Since a large amount of oil deposits, two third of the original oil-in-place is trapped by the capillary forces, and there is a need to recover residual oil by improving oil recovery techniques. Although gas, thermal, microbial, and chemical injection is very popular and highly used techniques, they have some disadvantages. Therefore, tertiary oil recovery techniques, such as the application of nanofluids and nanocomposites, may solve this problem. The selection of appropriate techniques depends on the reservoir and economics. The mobility ratio and the mechanisms for nano-enhanced oil recovery have also been explained. Silica, zinc oxide, titanium dioxide, carbon-based nanoparticles, graphene quantum dots, graphene oxide nanosheets, and anionic surfactants are widely used in enhanced oil recovery research. Nanocomposites were discussed recently prepared, including potassium chloride/silicon dioxide/xanthan and zinc oxide/silicon dioxide/xanthan nanocomposite and others. The reviewed literature experimental data has shown that it is possible to increase the enhanced oil recovery in the 10 to 79% range depending on the applied nanofluid or nanocomposite.

Groundwater is an essential resource for drinking water, but its contamination with potentially toxic elements and arsenic (As) is a global issue. To evaluate As and its levels in the Coachella Valley, the US Geological Survey (USGS) collected 17 groundwater samples. This study looked into the arsenic distribution, enrichment, hydrogeochemical behavior, and health risks associated with the samples. The comparative analysis between groundwater contamination in Greater Palm Springs and similar regions, could provide valuable insights into regional differences and common challenges. The hydrogeochemical facies showed the dominance of calcium and magnesium-bicarbonate-carbonate, indicating permanent hardness and salt deposits of residual carbonate. The Gibbs plot demonstrated that chemical weathering of rock-forming minerals and evaporation are the primary forces impacting groundwater chemistry. Geochemical modeling revealed saturation for calcite and dolomite, and under-saturation for halite. Principal component analysis identified the potential contributory sources for contamination of groundwater. The carcinogenic and non-carcinogenic potentials of the toxic elements arsenic, cadmium, chromium (VI), and lead were calculated using a human health risk assessment model. For both adults and children, the highest non-carcinogenic mean value was observed for arsenic (8.52 × 10−1), with the lowest for cadmium (1.32 × 10−3). Children had the highest cumulative non-carcinogenic risk from potentially toxic elements. Our research offers crucial baseline data for assessing arsenic in groundwater at the regional level, which is important for health risk reduction and remediation programs. The data show that preventative action must be taken to reduce the potential health risks in the study area from drinking groundwater, particularly for children.

Capsella bursa-pastoris (L.) Medik. (known as shepherd's purse) is a plant whose parts are used as medicine in herbal medicine. It is applicable as a medicine in the treatment of all forms of internal bleeding, for the treatment of hemorrhoids, excessive menstruation, but also for the usual stopping of nosebleeds. Through this research, the influence of organic solvents and their aqueous mixtures on the efficiency of polyphenol extraction and antioxidant activity was compared. The inhibition of free radicals was tested by the DPPH method, while the FRAP method was used to test the reduction potential. Analyzes have shown that water is the most effective solvent in the isolation of polyphenols from the aerial parts of shepherd's purse. Mixtures of organic solvents with water also showed high efficiency in the extraction of bioactive components, while the weakest results were obtained for extracts prepared in pure organic solvents.

Purpose Data on long-term effect of mechanical thrombectomy (MT) in patients with large ischemic cores (≥ 70 ml) are scarce. Our study aimed to assess the long-term outcomes in MT-patients according to baseline advanced imaging parameters. Methods We performed a single-centre retrospective cohort study of stroke patients receiving MT between January 1, 2010 and December 31, 2018. We assessed baseline imaging to determine core and mismatch volumes and hypoperfusion intensity ratio (with low ratio reflecting good collateral status) using RAPID automated post-processing software. Main outcomes were cross-sectional long-term mortality, functional outcome and quality of life by May 2020. Analysis were stratified by the final reperfusion status. Results In total 519 patients were included of whom 288 (55.5%) have deceased at follow-up (median follow-up time 28 months, interquartile range 1–55). Successful reperfusion was associated with lower long-term mortality in patients with ischemic core volumes ≥ 70 ml (adjusted hazard ratio (aHR) 0.20; 95% confidence interval (95% CI) 0.10–0.44) and ≥ 100 ml (aHR 0.26; 95% CI 0.08–0.87). The effect of successful reperfusion on long-term mortality was significant only in the presence of relevant mismatch (aHR 0.17; 95% CI 0.01–0.44). Increasing reperfusion grade was associated with a higher rate of favorable outcomes (mRS 0–3) also in patients with ischemic core volume ≥ 70 ml (aOR 3.58, 95% CI 1.64–7.83). Conclusion Our study demonstrated a sustainable benefit of better reperfusion status in patients with large ischemic core volumes. Our results suggest that patient deselection based on large ischemic cores alone is not advisable.

Abstract This article examines the user experience and cognitive workload in digital storytelling through immersive virtual reality (VR). Specifically, it explores the relationship between various aspects of the user experience and their link to the cognitive workload. The investigation was conducted by means of a large-scale evaluation of an underwater archaeological VR experience that simulated diving into the reconstructions of a submerged ancient site combined with interactive 360° video storytelling. The evaluation included 125 participants from two different geographical locations. Results revealed a strong interdependence between all user experience scales, including presence, immersion, engagement, emotional response, state of flow, subjective judgment, and technological adoption of the VR equipment. Moreover, the sense of presence in VR was strongly related to the reported task performance. The article highlights the importance of understanding the user experience and cognitive workload when creating digital storytelling applications in immersive VR.

Introduction: COVID-19 has been a major focus of scientific research since early 2020. Due to its societal, economic, and clinical impact worldwide, research efforts aimed, among other questions, to address the effect of host genetics in susceptibility and severity of COVID-19. Methods: We, therefore, performed next-generation sequencing of coding and regulatory regions of 16 human genes, involved in maintenance of the immune system or encoding receptors for viral entry into the host cells, in a subset of 60 COVID-19 patients from the General Hospital Tešanj, Bosnia and Herzegovina, classified into three groups of clinical conditions of different severity (“mild,” “moderate,” and “severe”). Results: We confirmed that the male sex and older age are risk factors for severe clinical picture and identified 13 variants on seven genes (CD55, IL1B, IL4, IRF7, DDX58, TMPRSS2, and ACE2) with potential functional significance, either as genetic markers of modulated susceptibility to SARS-CoV-2 infection or modifiers of the infection severity. Our results include variants reported for the first time as potentially associated with COVID-19, but further research and larger patient cohorts are required to confirm their effect. Discussion: Such studies, focused on candidate genes and/or variants, have a potential to answer the questions regarding the effect of human genetic makeup on the expected infection outcome. In addition, loci we identified here were previously reported to have clinical significance in other diseases and viral infections, thus confirming a general, broader significance of COVID-19-related research results following the end of the pandemic period.

Understanding the behaviour of a system’s API can be hard. Giving users access to relevant examples of how an API behaves has been shown to make this easier for them. In addition, such examples can be used to verify expected behaviour or identify unwanted behaviours. Methods for automatically generating examples have existed for a long time. However, state-of-the-art methods rely on either white-box information, such as source code, or on formal specifications of the system behaviour. But what if you do not have access to either? This may be the case, for example, when interacting with a third-party API. In this paper, we present an approach to automatically generate relevant examples of behaviours of an API, without requiring either source code or a formal specification of behaviour. Evaluation on an industry-grade REST API shows that our method can produce small and relevant examples that can help engineers to understand the system under exploration.

Castleman disease (CD) is a rare and heterogeneous lymphoproliferative disorder with shared lymph node (LN) histology. 1 While multicentric CD (MCD) involves multiple enlarged LNs, systemic inflammation

Child-robot interaction (CRI) has been mostly studied in labs and classroom settings. In this work, we share a CRI language processing study carried out in children’s homes. Any automated system deployed “in-the-wild” faces practical problems, but when the target users are children, these problems get even more sensitive and challenging. In this work we analyse how each language processing layer performs with children at home with no researcher present. We carried out an experiment with 7 families [N=14 children, 6-13 years old] cohabiting with a simulated robot for 2 weeks in their own homes. Our goal in this study is to evaluate the performance of voice recognition, language understanding and dialogue management when children interact with a robot at home. Our results indicate that dialogue management capabilities are becoming the key element in the language processing pipeline; they also denote that the dialogue engine should include mixed-initiative capabilities and show the relative usage of different common built-in intents.

Haru4Kids (H4K) is a system that emulates the physical, social, family-oriented robot Haru, designed with the goal to cohabitate with children in their home for extended periods of time. Seven families kept H4K for two net weeks in their homes. Throughout this period of cohabitation, we collected user logs comprised of the children users ’ head angles, the rotation angles of the platform, and the actions taken by H4K as well as captured images which were afterwards hand-annotated to estimate user engagement. We report the trends of these external metrics that we collected during every session of interaction. We also developed an annotation tool and report the Engagement Level Metric we chose to estimate child engagement throughout interactions “in-the-wild.” Overall, our platform offers a feasible system that can engage with children while also allowing us to monitor their engagement and behaviour throughout each interaction.

In this paper, we present an in-depth illustration of interaction failures relatively unexplored in the field of human-robot interaction (HRI). Our qualitative analysis of interactions between a social robot and 12 participants sheds light on different types of erroneous interactions initiated by human and robot actors and their outcomes. Our findings show that a small portion of observed failures had fatal impacts on interactions. In most cases, they had little negative effects on interactions or even led to favorable outcomes, causing laughter and giggling from participants, for example. Overall, our study calls for further examination of the roles of failures and contextual factors that influence the consequences of failures in HRI.

Robots may be able to significantly assist older adults through making activity recommendations. Prior research suggests that gender and age of a robot’s voice may affect how people respond to such recommendations, but few studies have explored how a robot’s voice is perceived by older adults, and whether their perceptions differ across cultures. We conducted a survey study with older adult participants (aged 65+) in the U.S. (N=225) and Japan (N=466), asking them to evaluate a humanoid robot speaking with three different voices (male, female, child). After seeing a video of a robot making recommendations, participants rated the fit of the voice to the robot, its sociality (via the Robotic Social Attributes Scale - RoSAS), and their willingness to use the robot in various contexts. We discovered that robot’s social attributes and participants’ culture impacted willingness to use the robot in both countries. Having positive social attributes and lower negative attributes increases willingness to use the robot. The U.S. older adults preferred the adult robot voices, had more positive social attributes, less negative social attributes, and were more likely to accept lifestyle recommendations than Japanese older adults. This study contributes to our understanding of older adults’ perceptions of robot voice and provides design implications for robots that make recommendations to older adults.

Previous work suggests that older adults’ meaning and happiness may be increased simply by having them engage in self-reflection exercises. Therefore, we design four modules to promote self-reflection, delivered by the QT robot. These modules were created with reference to three different time orientations — past, present, and future — and by incorporating aspects of life satisfaction and the PERMA model of well-being. Results show that about half of our older adult participants experienced subjective changes in meaning, happiness, and desire to make positive life changes during each module, with 11 of 15 participants experiencing changes to one of these measures after engaging in all four interactions. We make several suggestions for updating these modules for autonomous and longer-term deployment using the robot.

Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer’s, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people’s daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments? To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (United States and South Korea) with SARs deployed in each user’s home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and SAR. Models built on that data were capable of achieving nearly 84% accuracy for detecting specific interaction modalities (AUC 0.885) when trained/tested on the same location, though suffered significant performance drops when applied to a different location. Further analysis and participant interviews showed that was likely due to differences in home living environments in the US and Korea. The results suggest that our ability to create adaptable behaviors for robotic pets may be dependent on the human-robot interaction (HRI) data available for modeling.