Abstract
Hybrid-material optical fibers enhance the capabilities of fiber-optics technologies, extending current functionalities to several emerging application areas. Such platforms rely on the integration of novel materials into the fiber core or cladding, thereby supporting hybrid modes with new characteristics. Here we present experiments that reveal hybrid mode interactions within a doped-core silica fiber containing a central high-index nanofluidic channel. Compared with a standard liquid-filled capillary, calculations predict modes with unique properties emerging as a result of the doped core/cladding interface, possessing a high power fraction inside and outside the nanofluidic channel. Our experiments directly reveal the beating pattern in the fluorescent liquid resulting from the excitation of the first two linearly polarized hybrid modes in this system, being in excellent agreement with theoretical predictions. The efficient excitation and beat of such modes in such an off-resonance situation distinguishes our device from regular directional mode couplers and can benefit applications that demand strong coupling between fundamental- and higher-order- modes, e.g. intermodal third-harmonic generation, bidirectional coupling, and nanofluidic sensing. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Abstract
eSports is a rapidly growing industry with increasing investment and large-scale international tournaments offering significant prizes. This has led to an increased focus on individual and team performance with factors such as communication, concentration, and team intelligence identified as important to success. Over a similar period of time, personal physiological monitoring technologies have become commonplace with clinical grade assessment available across a range of parameters that have evidenced utility. The use of physiological data to assess concentration is an area of growing interest in eSports. However, body-worn devices, typically used for physiological data collection, may constitute a distraction and/or discomfort for the subjects. To this end, in this work we devise a novel "invisible " sensing approach, exploring new materials, and proposing a proof-of-concept data collection system in the form of a keyboard armrest and mouse. These enable measurements as an extension of the interaction with the computer. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard device, involving 7 healthy subjects. A particularly advantageous characteristic of our setup is the use of conductive nappa leather, as it preserves the standard look and feel of the keyboard and mouse. According to the results obtained, this approach shows 3-15% signal loss, with a mean difference in heart rate between the reference and experimental device of -1.778 & PLUSMN; 4.654 beats per minute (BPM); in terms of ECG waveform morphology, the best cases show a Pearson correlation coefficient above 0.99.

Abstract
Achieving a balanced healthcare workforce requires health planners to adjust the supply of health human resources (HHR). Mathematical programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. This study proposes a new socio-technical methodology to factor in uncertainty over the future within mathematical programming modelling. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models. Health stakeholders and experts are engaged in the scenario building process. Causal map modelling and morphological analysis are adopted to digest stakeholders and experts' information about the future and give origin to contrasting and meaningful scenarios describing plausible future. These scenarios are then adjusted and validated by stakeholders and experts, who then elicit their best quantitative estimates for coherent combinations of input parameters for the mathematical programming model under each scenario. These sets of parameters for each scenario are then fed to the mathematical programming model to obtain optimal solutions that can be interpreted in light of the meaning of the scenario. The proposed methodology has been applied to a case study involving HHR planning in Portugal, but its scope far extends HHR planning, being especially suited for addressing strategic and policy planning problems that are sensitive to input parameters.

Abstract
Context The development of solutions to improve battery life in Android smartphones and the energy efficiency of apps running on them is hindered by diversity. There are more than 24k Android smartphone models in the world. Moreover, there are multiple active operating system versions, and a myriad application usage profiles. Objective In such a high-diversity scenario, profiling for energy has only limited applicability. One would need to obtain information about energy use in real usage scenarios to make informed, effective decisions about energy optimization. The goal of our work is to understand how Android usage, apps, operating systems, hardware, and user habits influence battery lifespan. Method We leverage crowdsourcing to collect information about energy in real-world usage scenarios. This data is collected by a mobile app, which we developed and made available to the public through Google Play store, and periodically uploaded to a centralized server and made publicly available to researchers, app developers, and smartphone manufacturers through multiple channels (SQL, REST API, zipped CSV/Parquet dump). Results This paper presents the results of a wide analysis of the tendency several smart-phone characteristics have on the battery charge/discharge rate, such as the different models, brands, networks, settings, applications, and even countries. Our analysis was performed over the crowdsourced data, and we have presented findings such as which applications tend to be around when battery consumption is the highest, do users from different countries have the same battery usage, and even showcase methods to help developers find and improve energy inefficient processes. The dataset we considered is sizable; it comprises 23+ million (anonymous) data samples stemming from a large number of installations of the mobile app. Moreover, it includes 700+ million data points pertaining to processes running on these devices. In addition, the dataset is diverse. It covers 1.6k+ device brands, 11.8k+ smartphone models, and more than 50 Android versions. We have been using this dataset to perform multiple analyses. For example, we studied what are the most common apps running on these smartphones and related the presence of those apps in memory with the battery discharge rate of these devices. We have also used this dataset in teaching, having had students practicing data analysis and machine learning techniques for relating energy consumption/charging rates with many other hardware and software qualities, attributes and user behaviors. Conclusions The dataset we considered can support studies with a wide range of research goals, be those energy efficiency or not. It opens the opportunity to inform and reshape user habits, and even influence the development of both hardware (manufacturers) and software (developers) for mobile devices. Our analysis also shows results which go outside of the common perception of what impacts battery consumption in real-world usage, while exposing new varied, complex, and promising research avenues.

Abstract
Peer-reviewing is considered the main mechanism for quality control of scientific publications. The editors of journals and conferences assign submitted papers to reviewers, who review them. Therefore, inconsistencies between reviewer recommendations and reviewer comments are a problem that the editor needs to handle. However, few studies have explored whether it is possible to predict the reviewer recommendation from review comments based on NLP techniques. This study aims to predict reviewer recommendation of the scientific papers they review (accept or reject) and predict reviewers' final scores. We used a dataset composed of 2,313 review texts from two computer science conferences to test our approach, based on seven ML algorithms on regression and classification tasks and VADER application. SVM and MLP Classifier achieved the best performance in the classification task. In the regression task, the best performance was achieved by Nearest Neighbors. One of the most interesting results is the positive classification of most reviews by VADER: reviewers present constructively written reviews without highly negative comments land; therefore, VADER cannot detect reviews with a negative score.

Abstract
Digital transformation is critical for the competitiveness of SMEs. Digital Innovation Hubs (DIHs) aim to regionally support companies in the development of new products, processes, or services, providing access to advanced technologies. Since DIHs have to be financially sustainable, it is important to discuss which business models are put forward in such complex arrangements. Our main goal is to analyse how DIHs, specialized in Industry 4.0 technologies and services, create, offer, and capture value. The research was conducted through a documentary analysis of reports about DIHs' Business Models, generated by three European initiatives (encompassing more than 300 DIHs). Results demonstrate that one Business Model does not fit all, since regional characteristics, which vary among differentDIH's, are themain drivers to define value creation, offer and capture. This work aims to provide DIH managers insights to help them develop sustainability strategies.