Application of the analytic hierarchy process in a comparative analysis of automated information systems

In a scientific study, we investigated decision-making methods, in particular, the methods of expert estimations to solve problems. Reflected the possibility of using the analytic hierarchy process for researching and selection of information systems in accordance with the requirements of the customer. Comparative analysis has been performed on the example of automated library information systems

AI in marketing, consumer research and psychology: A systematic literature review and research agenda

This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross-fertilization of theories used across fields, and neglected research topics

The acclimative biogeochemical model of the southern North Sea

Ecosystem models often rely on heuristic descriptions of autotrophic growth that fail to reproduce various stationary and dynamic states of phytoplankton cellular composition observed in laboratory experiments. Here, we present the integration of an advanced phytoplankton growth model within a coupled three-dimensional physical-biogeochemical model and the application of the model system to the southern North Sea (SNS) defined on a relatively high resolution (∼1.5-4.5 km) curvilinear grid. The autotrophic growth model, recently introduced by Wirtz and Kerimoglu (2016), is based on a set of novel concepts for the allocation of internal resources and operation of cellular metabolism. The coupled model system consists of the General Estuarine Transport Model (GETM) as the hydrodynamical driver, a lower-trophic-level model and a simple sediment diagenesis model. We force the model system with realistic atmospheric and riverine fluxes, background turbidity caused by suspended particulate matter (SPM) and open ocean boundary conditions. For a simulation for the period 2000-2010, we show that the model system satisfactorily reproduces the physical and biogeochemical states of the system within the German Bight characterized by steep salinity; nutrient and chlorophyll (Chl) gradients, as inferred from comparisons against observation data from long-term monitoring stations; sparse in situ measurements; continuous transects; and satellites. The model also displays skill in capturing the formation of thin chlorophyll layers at the pycnocline, which is frequently observed within the stratified regions during summer. A sensitivity analysis reveals that the vertical distributions of phytoplankton concentrations estimated by the model can be qualitatively sensitive to the description of the light climate and dependence of sinking rates on the internal nutrient reserves. A non-acclimative (fixed-physiology) version of the model predicted entirely different vertical profiles, suggesting that accounting for physiological flexibility might be relevant for a consistent representation of the vertical distribution of phytoplankton biomass. Our results point to significant variability in the cellular chlorophyll-to-carbon ratio (Chl : C) across seasons and the coastal to offshore transition. Up to 3-fold-higher Chl : C at the coastal areas in comparison to those at the offshore areas contribute to the steepness of the chlorophyll gradient. The model also predicts much higher phytoplankton concentrations at the coastal areas in comparison to its non-acclimative equivalent. Hence, findings of this study provide evidence for the relevance of physiological flexibility, here reflected by spatial and seasonal variations in Chl : C, for a realistic description of biogeochemical fluxes, particularly in the environments displaying strong resource gradients. © 2017 Author(s)

The impact of service robots on customer satisfaction online ratings: The moderating effects of rapport and contextual review factors

Recent research has established a positive relationship between the use of service robots powered by artificial intelligence in hospitality firms and customer satisfaction online ratings, a particularly important form of electronic word of mouth. However, it is not clear if and how this relationship is augmented or diminished by moderating factors. In this study, we examined four potential moderators by using machine learning and natural language processing techniques to analyze 20,166 online reviews of hotels that had implemented service robots. We had four key findings. First, a positive service robot-satisfaction rating relationship was further enhanced by improved customer-service robot rapport during the service encounter. Second, higher customer effort focused on service robots in a review reduced the service robot-satisfaction rating relationship. Third, posting reviews using a mobile device (vs. other devices) showed higher satisfaction ratings. Finally, customers' prior experience in writing online reviews was unrelated to the service robot-satisfaction rating relationship. Taken together, these results suggest that service robots should be designed to be interactive and encourage customers to build rapport, for example, by service robots engaging in conversational flows. Moreover, customers should be nudged to use their mobile devices to post timely reviews on their positive human–robot interactions

Blood manufacturing methods affect red blood cell product characteristics and immunomodulatory activity

Transfusion of red cell concentrates (RCCs) is associated with increased risk of adverse outcomes that may be affected by different blood manufacturing methods and the presence of extracellular vesicles (EVs). We investigated the effect of different manufacturing methods on hemolysis, residual cells, cell-derived EVs, and immunomodulatory effects on monocyte activity. Thirty-two RCC units produced using whole blood filtration (WBF), red cell filtration (RCF), apheresis-derived (AD), and whole blood-derived (WBD) methods were examined (n = 8 per method). Residual platelet and white blood cells (WBCs) and the concentration, cell of origin, and characterization of EVs in RCC supernatants were assessed in fresh and stored supernatants. Immunomodulatory activity of RCC supernatants was assessed by quantifying monocyte cytokine production capacity in an in vitro transfusion model. RCF units yielded the lowest number of platelet and WBC-derived EVs, whereas the highest number of platelet EVs was in AD (day 5) and in WBD (day 42). The number of small EVs (<200 nm) was greater than large EVs (≥200 nm) in all tested supernatants, and the highest level of small EVs were in AD units. Immunomodulatory activity was mixed, with evidence of both inflammatory and immunosuppressive effects. Monocytes produced more inflammatory interleukin-8 after exposure to fresh WBF or expired WBD supernatants. Exposure to supernatants from AD and WBD RCC suppressed monocyte lipopolysaccharide-induced cytokine production. Manufacturing methods significantly affect RCC unit EV characteristics and are associated with an immunomodulatory effect of RCC supernatants, which may affect the quality and safety of RCCs

Prevalence and treatment of hypertension, diabetes and asthma in Kenya: A representative household survey in eight counties in 2016

Objectives: In 2014, 27% of total deaths in Kenya were due to non-communicable diseases (NCDs). The objectives of this study were: 1) To report on the prevalence of households with members diagnosed and treated for hypertension, diabetes, and asthma in eight counties in Kenya, and 2) To explore possible reasons for the variation in prevalence of these three NCDs in the different counties. Design, Setting andSubjects: A total of 7,870 households in a representative sample in eight Kenyan counties were screened for the presence of any non-communicable disease. Diagnosis and treatment data on these NCDs was collected and compared using county specific independent data from the 2014 Kenyan Demographic Health Survey (DHS).Main Outcome Measures: Over all the eight surveyed counties, 10.7% of households reported having one or more individuals with an NCD. The county specific prevalence varied from 3% to 30.2%. Of the 7,870 households surveyed, 6.9% reported having a diagnosis of hypertension, 3.2% of asthma, and 2.3% of diabetes.Results: The strongest explanatory variables for the variation in overall prevalence of NCDs related to access to health services and lifestyle risk factors. Conclusion: The prevalence of reported NCDs varies considerably between  counties in Kenya. Reasons may relate to a lack of access to diagnostic facilities or  ifferences in lifestyle risk factors. We recommend a comprehensive field survey of biometric, health access, and lifestyle risk factors to determine the true prevalence and related risk factors for NCDs in Kenya

Relationship between dielectric properties and critical behavior of the electric birefringence in binary liquid mixtures

We present experimental results on the critical exponent ψEKE describing the divergence of the Kerr constant of binary liquid mixtures near the critical consolute point. We show that the measured value of ψEKE agrees with the theoretical prediction only if the measurement is performed with a mixture of two liquids presenting a small mismatch in the dielectric constant, and that the measured ψEKE grows as the dielectric constant mismatch increases. Such findings are consistent with a recent model which assumes that the elongation of critical fluctations along the direction of the electric field can become so strong that fluctuations in the direction perpendicular to the electric field may cross over from Ising to mean-field behavior

Transport through open quantum dots: making semiclassics quantitative

We investigate electron transport through clean open quantum dots (quantum billiards). We present a semiclassical theory that allows to accurately reproduce quantum transport calculations. Quantitative agreement is reached for individual energy and magnetic field dependent elements of the scattering matrix. Two key ingredients are essential: (i) inclusion of pseudo-paths which have the topology of linked classical paths resulting from diffraction in addition to classical paths and (ii) a high-level approximation to diffractive scattering. Within this framework of the pseudo-path semiclassical approximation (PSCA), typical shortcomings of semiclassical theories such as violation of the anti-correlation between reflection and transmission and the overestimation of conductance fluctuations are overcome. Beyond its predictive capabilities the PSCA provides deeper insights into the quantum-to-classical crossover.Comment: 20 pages, 19 figure

Explainability of deep neural networks for MRI analysis of brain tumors

Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI