Conflicts are inherently emotional, yet parties in conflict may choose to explicitly express indifference. It is unclear, however, whether this represents an effective strategy. Drawing on emotions as social information (EASI) theory, we examined the interpersonal effects of indifference expressions in conflict and the processes that underlie these effects. Study 1 indicated that people believe indifference expressions constitute a neutral emotional signal. However, Study 2 demonstrated experimentally that counterparts’ indifference expressions reduce focal negotiators’ cooperative intentions through both affective (negative affective reactions) and inferential (decreased expected collaboration) processes when compared to negative (anger, contempt), positive (hope), and neutral (no emotion) expressions. Study 3 revealed negative effects of indifference (vs. neutral) expressions on cooperative intentions, expected collaboration, and heart rate variability as a physiological indicator of affective responding. Results further showed an indirect effect through expected collaboration, but not through affective reactions. Study 4 established the negative effects of indifference expressions on a behavioral measure of cooperation through expected collaboration. Studies 5 and 6 (pre-registered) demonstrated that the impact of indifference expressions on cooperative intentions (Study 5) and actual cooperation (Study 6) via counterpart’s expected collaboration is reduced when a counterpart explicitly indicates cooperative intentions, reducing the diagnostic value of indifference expressions. Across studies (N = 2,447), multiple expressive modalities of indifference were used, including verbal and non-verbal expressions. Findings demonstrate that explicit expressions of indifference have qualitatively different interpersonal effects than other emotional expressions, including neutral expressions, and cast doubt on the effectiveness of expressing indifference in negotiating social conflict.
Background The COVID-19 pandemic has placed exceptional demand on Intensive Care Units, necessitating the critical care transfer of patients on a regional and national scale. Performing these transfers required specialist expertise and involved moving patients over significant distances. Air Ambulance Kent Surrey Sussex created a designated critical care transfer team and was one of the first civilian air ambulances in the United Kingdom to move ventilated COVID-19 patients by air. We describe the practical set up of such a service and the key lessons learned from the first 50 transfers. Methods Retrospective review of air critical care transfer service set up and case review of first 50 transfers. Results We describe key elements of the critical care transfer service, including coordination and activation; case interrogation; workforce; training; equipment; aircraft modifications; human factors and clinical governance. A total of 50 missions are described between 18 December 2020 and 1 February 2021. 94% of the transfer missions were conducted by road. The mean age of these patients was 58 years (29–83). 30 (60%) were male and 20 (40%) were female. The mean total mission cycle (time of referral until the time team declared free at receiving hospital) was 264 min (range 149–440 min). The mean time spent at the referring hospital prior to leaving for the receiving unit was 72 min (31–158). The mean transfer transit time between referring and receiving units was 72 min (9–182). Conclusion Critically ill COVID-19 patients have highly complex medical needs during transport. Critical care transfer of COVID-19-positive patients by civilian HEMS services, including air transfer, can be achieved safely with specific planning, protocols and precautions. Regional planning of COVID-19 critical care transfers is required to optimise the time available of critical care transfer teams.
The epicardium constitutes an untapped reservoir for cardiac regeneration. Upon heart injury, the adult epicardium re-activates, leading to epithelial-to-mesenchymal transition (EMT), migration, and differentiation. While interesting mechanistic and therapeutic findings arose from lower vertebrates and rodent models, the introduction of an experimental system representative of large mammals would undoubtedly facilitate translational advancements. Here, we apply innovative protocols to obtain living 3D organotypic epicardial slices from porcine hearts, encompassing the epicardial/myocardial interface. In culture, our slices preserve the in vivo architecture and functionality, presenting a continuous epicardium overlaying a healthy and connected myocardium. Upon thymosin β4 treatment of the slices, the epicardial cells become activated, upregulating epicardial and EMT genes, resulting in epicardial cell mobilization and differentiation into epicardial-derived mesenchymal cells. Our 3D organotypic model enables to investigate the reparative potential of the adult epicardium, offering an advanced tool to explore ex vivo the complex 3D interactions occurring within the native heart environment.
Operational factors and microbial interactions affect the ecology in anaerobic digestion systems. From 12 lab-scale reactors operated under distinct engineering conditions, bacterial communities were found driven by temperature, while archaeal communities by both temperature and substrate properties. Combining the bacterial and archaeal community clustering patterns led to five sample groups (ambient, mesophilic low-solid-substrate, mesophilic, mesophilic co-digestion and thermophilic) for co-occurrence network analysis. Network topological properties were associated with substrate characteristics and hydrolysis-methanogenesis balance. The hydrolysis efficiency correlated ( p < 0.05) with clustering coefficient positively and with normalized betweenness negatively. The influent particulate COD ratio and the relative differential hydrolysis-methanogenesis efficiency ( D efficiency ) correlated negatively with the average path length ( p < 0.05). Individual genera’s topological properties showed more connector genera in thermophilic network, representing stronger inter-module communication. Individual genera’s normalized degree and betweenness revealed that lower-abundance genera (as low as 0.1%) could perform central hub roles and communication roles, maintaining the stability and functionality of the microbial community.
Bladder cancer is the fourth most common malignancy in males. It can present across the whole continuum of severity, from mild through well-differentiated disease to extremely malignant tumours with poor survival rates. As with other vital organ malignancies, proper clinical management involves accurate diagnosis and staging. Chemotherapy consisting of a cisplatin-based regimen is the mainstay in the management of muscle-invasive bladder cancers. Control via cisplatin-based chemotherapy is threatened by the development of chemoresistance. Intracellular cholesterol biosynthesis in bladder cancer cells is considered a contributory factor in determining the chemotherapy response. Farnesyl-diphosphate farnesyltransferase 1 (FDFT1), one of the main regulatory components in cholesterol biosynthesis, may play a role in determining sensitivity towards chemotherapy compounds in bladder cancer. FDFT1-associated molecular identification might serve as an alternative or appendage strategy for early prediction of potentially chemoresistant muscle-invasive bladder cancer tissues. This can be accomplished using Raman spectroscopy. Developments in the instrumentation have led to it becoming one of the most convenient forms of analysis, and there is a highly realistic chance that it will become an effective tool in the pathology lab. Chemosensitive bladder cancer tissues tend to have a higher lipid content, more protein genes and more cholesterol metabolites. These are believed to be associated with resistance towards bladder cancer chemotherapy. Herein, Raman peak assignments have been tabulated as an aid to indicating metabolic changes in bladder cancer tissues that are potentially correlated with FDFT1 expression.
Background Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics. Methods We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case–control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses. Results Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls. Conclusions These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity.
Background SATB2-associated syndrome (SAS) is a multisystem neurodevelopmental disorder characterised by intellectual disability, speech delay, and craniofacial anomalies. Although the clinical presentation of SAS is well-delineated, behaviours associated with SAS are less well-defined. Given the varied social profile reported in SAS of a ‘jovial’ predisposition and autistic behaviours, there may be phenotypic overlap with both Angelman syndrome (AS) and non-syndromal autism. This study aimed to describe behaviours in SAS in relation to chronological age and level of ability and contrast aspects of the behavioural phenotype with AS and non-syndromal autism. Methods Informant report questionnaire measures of behaviour, emotion, and autism characteristics were completed for 81 individuals with SAS (aged 1–36 years; 43 male). Within-group associations were analysed, and categorical data were compared between pre-school (1–5 years), school-age (6–15 years), and adolescent and adult SAS sub-groups (16 years and over). Cross-syndrome subscale and item-level analyses were conducted for 63 individuals with SAS (aged 1–27 years; 31 male), who were matched according to age and level of ability to 63 individuals with AS (aged 2–25 years; 32 male) and 63 individuals with non-syndromal autism (aged 3–26 years; 53 male). Results In SAS, higher rates of overactivity were moderately associated with lower self-help ability, and higher general anxiety scores were reported for males compared with females. Cross-syndrome subscale analyses uncovered several significant differences (p < .01), with comparatively low rates of stereotyped behaviour, overactivity, insistence on sameness and positive affect, and comparatively greater interest and pleasure and compulsive behaviour in individuals with SAS. Item-level analyses revealed a distinct profile of repetitive and autistic behaviours. Limitations Developmental analysis was based on a cross-sectional rather than a longitudinal research design, the contribution of pain and sleep to behaviour was not explored, and molecular genetic testing to determine genotype–phenotype behavioural relationships was not possible. Conclusions This study highlights the importance of behavioural comparisons to well-delineated groups and the utility of fine-grained item-level analyses to elucidate aspects of behaviour that might be syndrome related or shared across neurodevelopmental disorders. Future research is needed to further describe the distinctive repetitive and autistic behavioural phenotype in SAS.
The last decade has seen renewed concern within the scientific community over the reproducibility and transparency of research findings. This paper outlines some of the various responsibilities of stakeholders in addressing the systemic issues that contribute to this concern. In particular, this paper asserts that a united, joined-up approach is needed, in which all stakeholders, including researchers, universities, funders, publishers, and governments, work together to set standards of research integrity and engender scientific progress and innovation. Using two developments as examples: the adoption of Registered Reports as a discrete initiative, and the use of open data as an ongoing norm change, we discuss the importance of collaboration across stakeholders.
It is well established that spatial thinking is central to discovery, learning, and communication in mathematics, as indicated by convincing evidence that those with strong spatial skills also demonstrate advantages for Science, Technology, Engineering and Mathematics (STEM) performance. Yet, spatial thinking—the ability recall, generate, manipulate, and reason about spatial relations—is often absent from modern mathematics curricula. In this commentary, we outline evidence from our recent meta-analysis, demonstrating a causal role of spatial thinking on mathematics. We subsequently discuss the implications of educational policy decisions made across different countries, regarding the prioritization of spatial reasoning in the classroom. Given the increasing global demand for highly qualified STEM graduates, and evidence that spatial skills promote improvements in STEM outcomes, we argue that it is remiss to continue to ignore spatial skill development as a component of educational policy.
The growing popularity of robot-related research contexts in hospitality and tourism calls for in-depth analysis of how different product/service designs strategies integrating robots may influence customers' experiences. Employing a scenario-based 2 × 2 × 2 experimental research design, this study assesses service robots applied at three different product/service levels (i.e., core, facilitating, and augmented). From surveying 378 customers of mid-priced casual restaurants and 312 tourists of a mid-priced theme park restaurant, findings of the study suggest that using robots at all three product/service levels lead to a more positive educational experience but not entertainment experience. The study further extends the literature by positioning dining at a robotic restaurant as an important occasion to showcase the latest technologies to customers. By providing memorable entertainment and educational experiences, customers’ technology readiness could be enhanced, making them more willing to try new technologies. Such a focus brings in unique contributions both in literature and practice.
Tourism is acknowledged as a contributor to destination economies in many countries. However, COVID-19 has devastated the tourism industry in numerous national economies. Although the economic impact of tourism on destinations has been examined in a large body of tourism literature, most studies have utilized the tourism-led economic growth hypothesis and traditional methods and data rather than cutting-edge economic methods. This study conducts a systematic literature review on tourism economic impact between 1975 and 2020, analyzing the general bibliometrics and examining the key themes and methods of assessing tourism economic impact. It contributes to an accurate assessment of tourism economic impact, works to identify gaps in the literature, highlights emerging trends in the field, and proposes directions for future research.
Choice of pricing strategy plays a central role in value creation and the effective functioning of markets. Shifts in technology and the growing availability of data are facilitating ever more innovative forms of pricing strategy. Within the emerging literature on pricing ethics, there is a gap in our understanding of the specific challenges of algorithmically generated dynamic pricing. Increasing pricing automation shifts the managerial focus from the selection of prices to the choice of algorithms. This paper expands the literature on pricing ethics by conceptualizing the ethical challenges raised by the contemporary use of dynamic pricing. We propose a governance model for algorithmically generated dynamic pricing, taking into account the role of the customer as a stakeholder in value generation.
Computational simulations can be used to save on both time and costs, complementing experimental work and providing further guidance. Blends of immiscible polymers induce phase segregation, and in some cases can produce useful multicoat systems. This work uses a range of molecular dynamics simulations methods, including an extended Flory Huggins Interaction Parameter (χ) approach to initially probe the interactions and miscibility between ester monomers commonly used in coil coatings. The work indicates that blends with similar backbone structures or “like with like” show increased miscibility and those with different structures lead to a larger χ value and immiscibility. Further to this, polyester blends with different backbone structures have been coarse grained with MARTINI beads and simulations of 10 μs have been run to identify the morphology of the blends at the mesoscopic level. Finally, simulations of the melamine crosslinker commonly used in polyester formulations are consistent with the previously seen formation of agglomerates at higher melamine content.
One of the obstacles in developing speech emotion recognition (SER) systems is the data scarcity problem, i.e., the lack of labeled data for training these systems. Data augmentation is an effective method for increasing the amount of training data. In this paper, we propose a cycle-generative adversarial network (cycle-GAN) for data augmentation in the SER systems. For each of the five emotions considered, an adversarial network is designed to generate data that have a similar distribution to the main data in that class but have a different distribution to those of other classes. These networks are trained in an adversarial way to produce feature vectors similar to those in the training set, which are then added to the original training sets. Instead of using the common cross-entropy loss to train cycle-GANs, we use the Wasserstein divergence to mitigate the gradient vanishing problem and to generate high-quality samples. The proposed network has been applied to SER using the EMO-DB dataset. The quality of the generated data is evaluated using two classifiers based on support vector machine and deep neural network. The results showed that the recognition accuracy in unweighted average recall was about 83.33%, which is better than the baseline methods compared.
Images have become integral to consumers' sharing of consumption experiences due to their abilities of carrying rich and vivid information. This study investigates the impacts of restaurant review photo sentiment on customers’ perceived review usefulness and enjoyment using deep learning and econometric model analysis. The results indicate that (1) reviews with photos are more useful and enjoyable than reviews without photos; (2) a U-shaped relationship exists between review photo sentiment and review usefulness, with the effect of review photo sentiment on review enjoyment being positive and linear. Moreover, the effects can be strengthened by the number of review photos while weakened by the text-photo sentiment disparity. The above findings are reinforced by a sample of restaurant online reviews written by tourists in Las Vegas. This study contributes to the electronic word-of-mouth literature as well as to the application of machine learning technologies in computer vision to tourism and hospitality research.
The sharing economy (SE) has been variously described as a disruptive, discontinuous, and social innovation. Now, more than a decade since the emergence of seminal platforms such as Airbnb, and amid heightened competition and macroenvironmental pressures, service innovation has become a strategic priority. Our editorial essay is guided by three objectives. First, as a prelude to this Special Issue, we examine the current state of SE service innovation literature. Despite some important contributions, especially in relation to business model innovation, other salient types of service innovation remain underexplored. Second, we position the contributions of the 13 papers in this Special Issue on our novel Sharing Economy Innovation Framework, which stipulates both the type of service innovation examined, and the focal dyadic relationships involved. Third, based on remaining gaps in the framework, we outline an agenda for future research on SE innovations.
Additional filter method (AFM) and remote microphone method (RMM) are the two common virtual sensing methods used to compensate for active noise control (ANC) in headrest systems where mounting the error microphone at the desired zone is inconvenient. AFM and RMM are divided into two stages: the training and control stages. In the training stage, a temporary microphone is installed at the target to generate the additional and observation filters for AFM and RMM, respectively. However, they are fully effective only when the disturbances are consistent during both the training and control stages. To solve this dilemma, we propose a robust parallel virtual sensing method (PVSM) for multichannel adaptive feedback ANC system to attenuate the varying narrowband noises. During the training stage, the potential primary noises are used to model a group of parallel additional filters. Subsequently, the synthetic observation filter required for estimating the virtual error signal is derived using minimax optimisation. In the control stage, the most eligible additional filter that minimises the energy of the estimated virtual error signal is matched per frame based on the minimum-mean-square-estimated-error-matching mechanism and applied to the adaptive controller. Theoretical analysis validates that when the primary source varies, PVSM is more robust than both AFM and RMM, and performs noise reduction similar to the direct control with the temporary error microphone placed at the target. The proposed PVSM is further analysed in terms of its convergence condition and computational complexity. Numerous simulations and real-time experiments conducted using a dual-channel ANC headrest validate the feasibility of the proposed algorithm and present a guideline for the selection of frame length in PVSM. Therefore, the proposed PVSM can be considered a robust virtual sensing method against varying primary disturbances.
Thin carbon fibre reinforced polymer (CFRP) tape-springs are attractive structures for use in space-based optical instruments because of their compact stowed form, and their high dimensional stability when deployed. In this paper we present, with examples, two inexpensive methods to assess the thermal expansion properties of tape-spring structures: one based on strain gauges to obtain coupon level values, and another based on laser interferometry for structure level measurements. The strain gauge technique is a versatile approach that exploits the thermal output characteristics of the sensors. The thermal expansion characterisation of thin-composite samples measured a longitudinal expansion of 4.44 ppm/∘C and transverse expansion 5.95 of ppm/∘C. The interferometry system is designed with a view to capturing the displacements and tilts that occur when a structure with a low thermal mass, like a tape-spring, experiences a rapid change in flux, as occurs in the space environment. The homodyne interferometer is developed for three degree-of-freedom (DoF) measurements with a resolution of 10−8 m for distances and 10−6 rad for angles. The interferometric setup is based on the classical Michelson architecture and consists of few inexpensive commercial optical components. The source is a 0.8 mW Helium–Neon laser with a wavelength of 632.8 nm. The other elements include two spherical singlets, a right-angle prism, a cubic beamsplitter and a CMOS camera. The recorded interference fringes are analysed by using an algorithm based on Discrete Fourier Transform (DFT). Spectral information on the light intensity signals can be used to determine relative displacements and tilts. The dimensional stability of an optical payload based on high-strain composites was tested. The telescope has a deployable Cassegrain design, which uses six extendable members for the separation of its secondary mirror. Axial deformations between 20–30μm along with angle variations of the order of 0.1 mrad were recorded with good repeatability.
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