Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Maturity of Hospitals in Practice: A Qualitative Design-Approach

Digitalization of hospitals is advancing slowly. Progress in practice is seldom structurally measured and supported. Available approaches to model maturity regarding digitalization are highly specialized, neglect transformational properties and lack utilizable value for the end user. In order to develop a practically applicable maturity measurement approach the authors incorporate needs and feedback from the field. This leads to a combination of quantifiable and quality aspects of characteristics associated with digitalization status. Furthermore, practice requires the assessment to be able to be adapted to individual needs. The approach presented comprises three assessing tools, which are eventually integrated into one assessment. The defined maturity scale leaves room for incorporating future technical developments. In an iterative building and review cycle, the presented measurement approach was found suitable and reasonable by experts from the sector. Practical application of the MM approach in the future will contribute to describing causalities in the expert organization “hospital” .
Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems

Autoregressive exogenous (ARX) systems are the general class of input-output dynamical systems used for modeling stochastic linear dynamical systems (LDS) including partially observable LDS such as LQG systems. In this work, we study the problem of system identification and adaptive control of unknown ARX systems. We provide finite-time learning guarantees for the ARX systems under both open-loop and closed-loop data collection. Using these guarantees, we design adaptive control algorithms for unknown ARX systems with arbitrary strongly convex or convex quadratic regulating costs. Under strongly convex cost functions, we design an adaptive control algorithm based on online gradient descent to design and update the controllers that are constructed via a convex controller reparametrization. We show that our algorithm has $\tilde{\mathcal{O}}(\sqrt{T})$ regret via explore and commit approach and if the model estimates are updated in epochs using closed-loop data collection, it attains the optimal regret of $\text{polylog}(T)$ after $T$ time-steps of interaction. For the case of convex quadratic cost functions, we propose an adaptive control algorithm that deploys the optimism in the face of uncertainty principle to design the controller. In this setting, we show that the explore and commit approach has a regret upper bound of $\tilde{\mathcal{O}}(T^{2/3})$, and the adaptive control with continuous model estimate updates attains $\tilde{\mathcal{O}}(\sqrt{T})$ regret after $T$ time-steps.
A New Approach to Model Free Option Pricing

In this paper we introduce a new approach to model-free path-dependent option pricing. We first introduce a general duality result for linear optimisation problems over signed measures introduced in [3] and show how the the problem of model-free option pricing can be formulated in the new framework. We then introduce a model to solve the problem numerically when the only information provided is the market data of vanilla call or put option prices. Compared to the common approaches in the literature, e.g. [4], the model does not require the marginal distributions of the stock price for different maturities. Though the experiments are carried out for simple path-dependent options on a single stock, the model is easy to generalise for multi-asset framework.
On the Role of Micro-categories to Characterize Event Popularity in Meetup

Event-based social networking platforms such as Meetup have recently witnessed a huge growth. However, with the rise in the volume of groups and events, making individual events attractive has become increasingly challenging for its organizers. As a result, we find that events hosted by groups of same category at similar venues and similar times, also widely differ in their popularity. Data study reveals that the topics specified in textual descriptions of events may be key to their popularity. In this paper, we introduce a novel concept of topical micro-categories in the context of EBSNs for accurately characterizing events, such that events belonging to the same micro-category exhibit similar popularity profile. We develop a principled method to detect such micro-categories from the textual descriptions of individual events. Our experiments reveal the significance of the detected micro-categories in determining the popularity of associated Meetup events and groups. We also investigate the effectiveness of the micro-categories in a real-world application scenario by developing a recommendation model; this model recommends relevant micro-categories to a group for hosting its future events with enhanced popularity. Notably, our model achieves an average NDCG score of around 0.75 showing a straight 5% improvement over the best performing competing method.
OMCoRP: An Online Mechanism for Competitive Robot Prioritization

We propose a collision-avoiding mechanism for a group of robots moving on a shared workspace. Existing algorithms solve this problem either (a) in an offline manner using the source-destination information of all the robots or (b) in an online manner with cooperative robots. We take a paradigm shift to the setting with competitive robots, that may strategically reveal their urgency of reaching the destinations and design online mechanisms that take decisions on-the-fly, reducing the overhead of an offline planning. We propose a mechanism OMCoRP in this setting that ensures truthful revelation of the robots' priorities using principles of economic theory and provides locally efficient movement of the robots. It is free from collisions and deadlocks, and handles dynamic arrival of robots. In practice, this mechanism gives a smaller delay for robots of higher priority and scales well for a large number of robots without compromising on the path optimality too much.
But Keep your Customers Closer: The Value of Regionality in Electronic Commerce

Green IS research addresses the pressing challenge of climate change. In particular, leading IS scholars have called for research on the design of IS solutions that support decision making for more sustainable practices. In this sense, our research explores how ICT artifacts should be designed to support consumer decision making in favor of regional products and services. We employ hedonic price regressions to analyze consumer preferences for regional products and services based on actual market data from an online price comparison platform. We find that consumers are willing to pay mark-ups for regional providers as long as those are both 1) based in close proximity to consumers (geographic regionality) and 2) strongly connected with and within the region (entrepreneurial regionality). We contribute to the Green IS debate by deriving tangible implications for the design of different types of user interfaces such as websites and comparison platforms.
CDCN: A New NMF-Based Community Detection Method with Community Structures and Node Attributes

Community discovery can discover the community structure in a network, and it provides consumers with personalized services and information pushing. It plays an important role in promoting the intelligence of the network society. Most community networks have a community structure whose vertices are gathered into groups which is significant for network data mining and identification. Existing community detection methods explore the original network topology, but they do not make the full use of the inherent semantic information on nodes, e.g., node attributes. To solve the problem, we explore networks by considering both the original network topology and inherent community structures. In this paper, we propose a novel nonnegative matrix factorization (NMF) model that is divided into two parts, the community structure matrix and the node attribute matrix, and we present a matrix updating method to deal with the nonnegative matrix factorization optimization problem. NMF can achieve large-scale multidimensional data reduction processing to discover the internal relationships between networks and find the degree of network association. The community structure matrix that we proposed provides more information about the network structure by considering the relationships between nodes that connect directly or share similar neighboring nodes. The use of node attributes provides a semantic interpretation for the community structure. We conduct experiments on attributed graph datasets with overlapping and nonoverlapping communities. The results of the experiments show that the performances of the F1-Score and Jaccard-Similarity in the overlapping community and the performances of normalized mutual information (NMI) and accuracy (AC) in the nonoverlapping community are significantly improved. Our proposed model achieves significant improvements in terms of its accuracy and relevance compared with the state-of-the-art approaches.
‘SPREAD THE APP, NOT THE VIRUS’ – AN EXTENSIVE SEM-‘SPREAD THE APP, NOT THE VIRUS’ – AN EXTENSIVE SEM-APPROACH TO UNDERSTAND PANDEMIC TRACING APP USAGE APPROACH TO UNDERSTAND PANDEMIC TRACING APP USAGE IN GERMANY IN GERMANY

The release of the Corona-Warn-App (CWA), a governmental pandemic tracing app to track infection chains related to COVID-19 in Germany, marks an unprecedented situation that offers a unique opportunity for investigating population-wide adoption of novel technology. We develop a conceptual model to investigate the effects and path relationships of multiple constructs related to technology adoption, data security, morality, social influence, trust, and COVID-19 to predict behavioral intentions and actual usage behavior. We use structural equation modelling with the partial least squares method and identify effort expectancy, social influence, prevailing opinions on COVID-19 and the CWA, as well as moral and ethical considerations as the most influential predictors. We are able to explain moderate to high amounts of variance with our model. Our results offer valuable insights for the technology acceptance literature and enable practical recommendations for improving the public communication and elevating user numbers of pandemic tracing apps in Germany
Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF

With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM-CRF-based method provides superior performance in comparison with various baseline methods.
Risk-aware Regularization for Opinion-based Portfolio Selection

Every investor faces the risk-return tradeoff when making investment decisions. Most of the investors construct a portfolio instead of putting all of their wealth on a certain stock. However, most of the previous works in the NLP community focus on predicting the movement of stocks' prices or volatilities, but do not consider the portfolio selection issue. On the other hand, few works consider unstructured data in the financial community when dealing with this issue. This paper introduces a novel opinion-based portfolio selection task, and proposes new objective functions presenting different risk appetites of investors. The empirical studies of the selecting portfolio are also discussed with both Sharpe ratio and volatility metrics.
A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm

In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it.
Influencers of Digital Transformation: a New Concept of User Participation in is Projects

Influencers are very common in online marketing. Our study claims that the influencer concept can also be applied to digital transformation projects. Influencers of digital transformation projects may contribute to visualise the benefits and to convince the end users of these projects. Therefore, we introduce the “influencer” as a new concept for user participation and investigate how it enhances the conversational key-user concept. We conducted an explorative study in small and medium-sized enterprises (SMEs), which implemented either the key user or the influencer as participation concepts for digital transformation projects. We explored the relevance of influencers, by highlighting the differences of both concepts (i.e. influencer and key user) in terms of focus, role, motivation, communication, function, direction and objective. Finally, we investigated the success factors of digital transformation in this influencer concept. Based on our results, we formalise the influencer concept enabling the further implementation within organisations.
The DevOps Continuum: Walking the Shadowy Bridge from Information Systems Development to Operations

In recent years, enterprises have observed that a holistic approach to agile information systems development and a close integration of information systems operations is essential to maximize the probability of success, leading to the emerging DevOps phenomenon. While first studies have delivered first preliminary insights about DevOps, a foundational understanding of DevOps implementations and theoretical grounding of DevOps is still missing. To close this gap, we conducted a multiple-case study to explore a variety of characteristics of DevOps, and how these different characteristics influence its implementation in different contexts. We find that variations of DevOps implementations are gradual transitions on what we call a DevOps continuum. Based on this insight, we propose a conceptual model which fosters our understanding of the DevOps phenomenon, relates DevOps to existing theories, and identifies new paths for future research.
Digital Health: A Systematic Literature Review and Future Research Directions

With the widespread diffusion of using Information Systems (IS), we see ambivalent effects on individual’s health. On the one hand, IS use has favourable effects on an individual’s health, such as reducing mortality. On the other hand, we see unfavourable effects of IS use, including technostress-induced job burnout and sleep problems. To provide an overview of research in that stream, we review literature anchored around the topic of digital health. Following the grounded-theory method, we identify 41 articles in the major IS journals and classify them by providing a) three forms of digital health (mental, physical, and social), b) three causes of digital health (individual, environmental, and technological), and c) the relationships between causes and forms. With that, we contribute to digital health research with a more nuanced understanding of causes, forms, and their relationships. We come up with fruitful avenues for future research.
Roles of DNA polymerase epsilon and TopBP1 in DNA replication and damage response

During DNA replication cells accurately copy their DNA to transfer the genetic information to daughter cells. DNA polymerases synthesise the new DNA strand using the old strand as a template. Other functions of DNA polymerases are recombination linked and DNA iamage repair linked DNA synthesis, regulation of replication complex formation and regulation of transcription – a process in which the genetic information is transformed into an RNA sequence needed to guide protein synthesis. In this study, the TopBP1 protein was shown to associate with DNA polymerase epsilon. TopBP1 contains eight BRCT domains mediating interactions between phosphorylated proteins and is a human homolog of bakers yeast Dpb11 and fission yeast Cut5. These yeast proteins act on DNA replication and cell cycle arrest after DNA damage. TopBP1 was found to be phosphorylated and expressed in elevated amounts during S phase suggesting an involvement in DNA replication. This was directly demonstrated by DNA synthesis inhibition by a competing TopBP1 fragment and by an antibody targeted to block TopBP1. Ultraviolet irradiation damages DNA and decreases the amount of TopBP1 in the nucleus. The transcription factor Miz-1 was found to associate with TopBP1 and was released from this interaction after UV damage. Free Miz-1 activated the expression of the cell cycle arresting proteins p15 and p21 cooperatively with other transcription factors and allowed extra time for DNA damage repair. TopBP1 was also found to interact with the breast cancer susceptibility protein 1 and both proteins localised together to arrested DNA synthesis apparatuses. The interaction of TopBP1 with the damage recognition and processing protein Rad9 is still further evidence of a link between TopBP1 and DNA damage. DNA polymerase epsilon forms a complex with Cdc45, a protein involved in DNA replication initiation and elongation. This complex does not interact with Cdc45 complexed with DNA polymerase delta, suggesting that these complexes synthesise DNA independently of each other. Our results are in agreement with the view that polymerase epsilon synthesises the first strand of DNA and polymerase delta the other. Finally,DNA polymerase epsilon binds to the RNA synthesising form of RNA polymerase II and nascent transcripts. The physiological meaning of this interaction needs to be determined.
Direct Determination of Phenol and Aniline in Environmental Water by Fluorescence Spectrophotometry

A new method for direct determination of two kinds of polluants, phenol and aniline in environmental water by fluorescence spectrophotometry was developed. For determination of phenol,the linear range and detection limit are 0～3 2 μg/mL, 4.0 ng/mL,respectively,at λ ex / λ em =270/297 nm(pH=1). For determination of aniline the linear range and detection limit are 0～2.0 μg/mL ,4.3 ng/mL respectively,at λ ex / λ em =280/340 nm(pH=12). The recovery is between 99% and 101%.
Electrochemical Investigation of La0.2Sr0.8TiO3+δ-Ce0.8Sm0.2O2-δ Composite Cathode for the Direct High Temperature Steam Electrolysis

This paper investigates a composite cathode La0.2Sr0. 8TiO3+δ-Ce0.8Sm0. 2O2-δ (LSTO-SDC) for the direct steam electrolysis in an oxide-ion-conducting solid oxide electrolyzer. The dependences of electrical conductivity of the reduced LSTO on temperature and oxygen partial pressure are studied and further correlated to the electrochemical properties of the cathode in symmetric cell LSTO-SDC/YSZ/LSTO-SDC and solid oxide electrolyzer LSTO-SDC/YSZ/LSM-SDC, respectively. Current efficiencies of the solid oxide electrolyzer with LSTO-SDC cathode were found to be 92.38% and 91.17% with or without reducing gas flowing over them under the applied voltage of 1.8 V at 800 °C, respectively.
Tauroursodeoxycholic Acid Attenuates Diet-Induced and Age-Related Peripheral Endoplasmic Reticulum Stress and Cerebral Amyloid Pathology in a Mouse Model of Alzheimer’s Disease

Obesity and diabetes are well-established risk factors of Alzheimer’s disease (AD). In the brains of patients with AD and model mice, diabetes-related factors have been implicated in the pathological changes of AD. However, the molecular mechanistic link between the peripheral metabolic state and AD pathophysiology have remained elusive. Endoplasmic reticulum (ER) stress is known as one of the major contributors to the metabolic abnormalities in obesity and diabetes. Interventions aimed at reducing ER stress have been shown to improve the systemic metabolic abnormalities, although their effects on the AD pathology have not been extensively studied. We examined whether interventions targeting ER stress attenuate the obesity/diabetes-induced Aβ accumulation in brains. We also aimed to determine whether ER stress that took place in the peripheral tissues or central nervous system was more important in the Aβ neuropathology. Furthermore, we explored if age-related metabolic abnormalities and Aβ accumulation could be suppressed by reducing ER stress. APP transgenic mice (A7-Tg), which exhibit Aβ accumulation in the brain, were used as a model of AD to analyze parameters of peripheral metabolic state, ER stress, and Aβ pathology in the brain. Intraperitoneal or intracerebroventricular administration of taurodeoxycholic acid (TUDCA), a chemical chaperone, was performed in high-fat diet (HFD)-fed A7-Tg mice for ∼1 month, followed by analyses at 9 months of age. Mice fed a normal diet were treated with TUDCA by drinking water for 4 months and intraperitoneally for 1 month in parallel, and analyzed at 15 months of age. Intraperitoneal administration of TUDCA suppressed ER stress in the peripheral tissues and ameliorated the HFD-induced obesity and insulin resistance. Concomitantly, Aβ levels in the brain were significantly reduced. In contrast, intracerebroventricular administration of TUDCA had no effect on the Aβ levels. Peripheral administration of TUDCA was also effective against the age-related obesity and insulin resistance, and markedly reduced amyloid accumulation. Interventions that target peripheral ER stress might be beneficial therapeutic and prevention strategies against brain Aβ pathology associated with metabolic overload and aging.
Atomic layer deposited TiO2 on a nitrogen-doped graphene/sulfur electrode for high performance lithium–sulfur batteries

Nitrogen-doped graphene (NG) has been fabricated and used as a carbon matrix for sulfur impregnation to construct cathodes for lithium–sulfur (Li–S) batteries. Atomic layers of TiO2 were further deposited on the electrode and the thickness was controlled by adjusting the number of deposition cycles to 0, 5, 20 and 40. The results showed that all the surface modified electrodes demonstrate high capacity, good rate capability, and enhanced cyclability compared to the bare electrode. Specifically, the electrode contained 59% (by weight, wt%) sulfur and with the addition of 20 cycle-TiO2 it demonstrated a superior boost in the active sulfur utilization (discharge capacity: 1374 mA h g−1 at 0.1C). It also delivered initial discharge capacity up to 1069.5 mA h g−1 and 918.3 mA h g−1 after 500 cycles at 1C with an average coulombic efficiency of about 99.7%. Moreover, the capacity retention increased from 42% to 61% from 0.1C to 4C with the addition of 20 cycle-TiO2. The improved electrochemical performance could be attributed to the on-site TiO2 absorption for polysulfide retention as well as the charge transfer enhancement. Theoretical calculations revealed that TiO2 exhibits a strong binding energy for lithium polysulfide species. These results suggest that the TiO2 modified NG has potential to be used as a cathode for high-performance Li–S batteries.