Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical antennas driven by quantum tunneling: a key issues review

Analogous to radio- and microwave antennas, optical nanoantennas are devices that receive and emit radiation at optical frequencies. Until recently, the realization of electrically driven optical antennas was an outstanding challenge in nanophotonics. In this review we discuss and analyze recent reports in which quantum tunneling—specifically inelastic electron tunneling—is harnessed as a means to convert electrical energy into photons, mediated by optical antennas. To aid this analysis we introduce the fundamentals of optical antennas and inelastic electron tunneling. Our discussion is focused on recent progress in the field and on future directions and opportunities.
Cost, Gain and Health: Theoretical Clarification and Psychometric Validation of a Work Stress Model with Data from Two National Studies.

OBJECTIVE To test non-symmetric effects of cost/gain imbalance at work on depression, based on the effort-reward imbalance (ERI) model. METHODS Study participants were derived from two large national studies from Germany and Sweden. Associations between the ERI scales, including the effort-reward (E-R) ratio, in 2016 and depression (in 2016 for German sample, and in 2018 for Swedish sample) were examined by multivariable logistic regression. RESULTS In both samples, high cost/low gain, but not low cost/high gain is associated with depression, with a 3-to 5-fold elevated risk in the highest decile of the E-R ratio. CONCLUSIONS The short version of the ERI questionnaire is a psychometrically useful tool for epidemiological research. The finding demonstrating non-symmetric effects of cost/gain imbalance contributes to a theoretical clarification of this stress-theoretical model.
Hippotherapy: a systematic mapping review of peer-reviewed research, 1980 to 2018

Abstract Purpose Comprehensively and systematically map peer-reviewed studies of hippotherapy published over 30 years, from 1980 through 2018, from the perspective of a phased scientific approach to developing complex interventions as a guide to future research and practice. Methods A systematic mapping review of research of hippotherapy was conducted. Searches of nine databases produced 3,528 unique records; 78 full-text, English-written studies were reviewed, the earliest of which was published in 1998. Data relevant to study aims were extracted electronically from these studies and analyzed using queries and pivot tables. Results Children with cerebral palsy and physical therapists were most prevalent as participants and providers. Equine movement was hippotherapy’s core component and mechanism. Early-phase outcomes-oriented research predominated. “Hippotherapy” was ambiguously defined as treatment strategies and comprehensive professional services, even as interventions grew more distinctive and complex. A treatment theory and proof of concept related to motor outcomes were established, and efficacy research with comparison conditions emerged. Conclusions Continuing research of complex interventions that integrate hippotherapy, equine movement as a therapy tool, is warranted. Attention to gaps in foundational scientific work concurrent with continued piloting and efficacy work will help to identify the most promising interventions worthy of replication, evaluation and widespread adoption. IMPLICATIONS FOR REHABILITATION To advance the evidence base of complex interventions that incorporate hippotherapy: •Proponents of hippotherapy need to define and represent hippotherapy to the public and in practice and research contexts as a therapy tool involving the use of the movement of horses by qualified professionals, rather than simply as a generic therapy with the help of a horse or simulated horse; •Providers of hippotherapy need to identify their professional degrees and certifications, and explicate their disciplinary perspectives that influence their selected components of intervention, related methods, and outcomes; and •Providers and researchers need to partner to develop an enablement theory of hippotherapy that links improved body functions with improved participation in everyday life and quality of life.
Evaluation of different detector types in measurement of ATP bioluminescence compared to colony counting method for measuring bacterial burden of hospital surfaces

The ATP bioluminescence method has been increasingly employed as a rapid, on-site detection method in nosocomial infections control. In this study, we used a paired design of monitoring methods, the colony counting method (C) and the ATP bioluminescence method, to evaluate environmental surfaces after disinfection. The ATP bioluminescence method included three detector types (B, P, and N). Every surface after disinfection was performed by combining two types of monitoring methods or detectors. There was no statistically significant difference in theATP content per surface siteamong samples from intensive care units (ICUs)and internal medicine wards using B (p = 0.435) and P (p = 0.260). According to the Spearman’s rank correlation coefficients, with the exception of the correlation between the ATP content values detected by B and P, the correlation between the values generated by the remaining methods/detectors was weak or lacking, whereasthe differences between the detectors were statistically significant. Therefore, there are differences between the ATP bioluminescence method and the colony counting method, also between different detectors.
The relationship between early life urbanicity and depression in late adulthood: evidence from the Survey of Health, Ageing and Retirement in Europe

Objectives We aimed to study the association of childhood urbanicity with depressive symptoms in late adulthood. Design, setting and participants We used linear and logistic regressions to analyse data drawn from 20 400 respondents from the Survey of Health, Ageing and Retirement in Europe, a panel dataset incorporating a representative sample of the 50+ population in 13 European countries. Outcomes and analysis Childhood urbanicity was determined using self-reports of the respondents’ circumstances at age 10, and late-adulthood depression using the EURO-D scale. We conditioned on circumstances early in life as well as later in life, most importantly late-adulthood urbanicity. We estimated the associations using linear regression models and limited dependent variable models. Results A pooled regression of both men and women suggested that childhood urbanicity is associated non-monotonically with depression in late adulthood and is particularly apparent for those spending their childhoods in suburban settings. We found that individuals who spend the longest time in their childhood in a suburban home exhibit an average increase in probability of 3.4 (CI 1.1 to 5.7) percentage points in reporting four or more depressive symptoms. The association was robust to the inclusion of a host of household characteristics associated with childhood urbanicity and was independent of current urbanicity and current income. When broken down by gender, we found some evidence of associations between depressive outcomes and urban living for men, and stronger evidence of such associations with urban and suburban living for women who exhibit an increase of 5.6 (CI 2.2 to 9.0) percentage points in reporting four or more depressive symptoms. Conclusions Our analysis reveals a relationship between childhood urbanicity and depression in late adulthood. The evidence presented on the nature of this relationship is not straightforward but is broadly suggestive of a link, differing by gender, between greater urbanicity and higher levels of depressive symptoms. The life-long nature of this association may potentially inform policy agendas aimed at improving urban and suburban living conditions.
Effects of Acute Exercise and Learning Strategy Implementation on Memory Function

Background and Objectives: Long-term potentiation (LTP), the functional connectivity among neurons, is considered a mechanism of episodic memory. Both acute exercise and learning are thought to influence memory via an LTP-related mechanism. Limited research has evaluated the individual and combined effects of acute exercise and learning strategy implementation (e.g., 3-R technique, cue-integration) on memory, which was the purpose of this study. Materials and Methods: For Experiment 1, participants (n = 80; Mage = 20.9 years) were randomized into one of four experimental groups, including Exercise + Learning (E + L), Learning Only (L), Exercise Only (E), and Control Group (C; no exercise and no learning strategy implementation). The exercise stimulus involved an acute 15-min bout of lower-intensity (60% of heart rate max) walking exercise and the learning strategy involved the implementation of the 3-R technique. Experiment 2 (n = 77; Mage = 21.1 years) replicated Experiment 1 but addressed limitations (e.g., exposure level of the memory task) from Experiment 1 and employed a higher-intensity bout of exercise (77% of heart rate max). Experiment 3 (n = 80; Mage = 21.0 years) evaluated these same four experimental conditions but employed a cue-integration learning strategy and a moderate-intensity bout of acute exercise (64% of heart rate max). Results: These three experiments demonstrate that both learning techniques were effective in enhancing memory and we also provided evidence of a main effect for acute exercise (Experiment 3). However, we did not observe consistent evidence of a learning by exercise interaction effect. Conclusions: We demonstrate that both acute exercise and different learning techniques are effective in enhancing long-term memory function.
Metabolic Unhealthiness Increases the Likelihood of Having Metabolic Syndrome Components in Normoweight Young Adults

Findings on risk detection for having metabolic syndrome (MetS) components, each of which may individually increase the risk of disease and mortality, are limited in young adults. In this study, we aimed to calculate the likelihood of having ≥1 MetS component in normoweight young adults using two different metabolic health criteria. We recruited 1182 normoweight young adults from the Taiwan Survey on the Prevalence of Hypertension, Hyperglycemia, and Hyperlipidemia and the National Health Interview Survey (aged 16–45 years, 39% male, body mass index = 18.5–22.99, all without MetS) and followed them for 5 years. Metabolic health criteria were derived from the Harmonized criteria (unhealthy if showing abnormality in one or two MetS components) and the triglyceride-glucose index (TyG-i; unhealthy if TyG-i was in the >75th percentile). Odds ratio (OR) and 95% confidence interval (CI) estimations for having ≥1 MetS component and for having each MetS component in 5 years were assessed using multivariable-adjusted logistic regression. We observed a significantly increased likelihood of the presence of ≥1 MetS component in the unhealthy group by using the Harmonized criteria and TyG-i (adjusted OR (aOR); 95%CI: 2.64; 2.02–3.45 and 2.1; 1.57–2.82, respectively). The areas under the receiver-operating characteristics curves were 0.679 and 0.652 for the final models using Harmonized and TyG-i criteria, respectively. These findings support the recommendation of treating any metabolic component abnormality, even in young adults without a MetS diagnosis.
Action Recognition Using Indoor Radar Systems

Activities of Daily Living (ADL) is essential part of elderly care not only in the event of detecting fall, but also for evaluating the pattern of life of an individual, such as food intake and personal hygiene. Continuous monitoring in healthcare are starting to use mobile wireless technologies, optical cameras and biometric sensors. However, those methods suffer from an integration problem where a lot parts has to come together before becoming a practical solution. Furthermore they require technological breakthroughs before implementation. On-the-shelf indoor radar system is a great alternative without legal issues regarding image rights [1][2]. In this work the radar sensing as one of healthcare modality is explored. The most common method to classify activities using radar is based on micro-Doppler radar signatures. The relative motion of structural components of an object/body generates unique patterns in the time-frequency domain of the radar returns. Therefore, different activities are generating unique distinctive features in micro-Doppler signatures that can be used for classification[3]. A limitation of this approach is that radar data for training and testing are typically captured as an image corresponding to a fixed time duration, a sort of “snapshot” spectrogram where only one activity is performed. This is unsuitable for analysing long sequences of realistic data, where a continuous flow of activities is performed by people, with more or less significant transitions between them. Therefore, learning the temporal relationship in a sequence of physical actions is important for human activity classification in 7/24 fashion. Although the idea of interpreting radar data as temporal sequences rather than images for classification was mentioned in late 90s, this approach is still seldom explored in the open literature, and even more so for raw radar data. In this work, we propose using Long Short Term Memory (LSTM) units in a recurrent neural network to classify six different activities, expanding from the preliminary results of simpler binary classification in our previous work [4]. Our most recent work studied a binary classification problem (differentiating between 2 actions) treating the data as time-sequences. I&Q raw radar data and range maps were used separately to determine which one would offer greater classification accuracy using Long Short-Term Memory Neural Networks. We achieved 99.56% accuracy from the raw data and 97.58% accuracy from the range maps with 5 participants. Further experiments for multiple activity classification were carried out with promising results. Furthermore this method can be used to provide persuasive feedback to end-users to advise and influence behaviours for safer and better practice, when anomalies in their routine are identified (prevention & assistance). This will transform the current paradigm of technologies from “reactive” to “proactive” monitoring systems.
Modelling perceived risks to personal privacy from location disclosure on online social networks

ABSTRACT As users increasingly rely on online social networks for their communication activities, personal location data processing through such networks poses significant risks to users’ privacy. Location tracks can be mined with other shared information to extract rich personal profiles. To protect users’ privacy, online social networks face the challenge of ensuring transparent communication to users of how their data are processed, and explicitly obtaining users’ informed consent for the use of this data. In this paper, we explore the complex nature of the location disclosure problem and its risks to personal privacy. We evaluate, with an experiment involving 715 participants, the contributing factors to the perception of such risks with scenarios that mimic (a) realistic modes of interaction, where users are not fully aware of the extent of their location-related data being processed, and (b) with devised scenarios that deliberately inform users of the data they are sharing and its visibility to others. The results are used to represent the users’ perception of privacy risks when sharing their location information online and to derive a possible model of privacy risks associated with this sharing behaviour. Such a model can inform the design of privacy-aware online social networks to improve users’ trust and to ensure compliance with legal frameworks for personal privacy.
Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Advances in the Internet of Things (IoT) are helping to make water management smarter and optimizing consumption in the smart agriculture industry. This article proposes a new topology of sensor nodes based on the use of inexpensive and highly efficient components, such as water level, soil moisture, temperature, humidity, and rain sensors. Additionally, to guarantee good performance of the system, the used transmission module is based on LoRa LPWAN technology. The design of the main circuit board of the system is optimized by combining two layers and implementing software optimization. The overall sensor network is developed and tested in the research lab, and real farms can be controlled by users manually or automatically using the mobile application. Experimental results are produced by testing sensor and communication link effectiveness, and are subsequently validated in the field through a one-week measurement campaign.
AutoRhythm: A Music Game With Automatic Hit-Timing Generation and Percussion Identification

This article describes a music rhythm game called AutoRhythm, which can automatically generate the hit timing as game contents from a given piece of music, and identify user-defined percussion of real objects in real time for gameplay. More specifically, AutoRhythm can generate the hit timing of a piece of music based on onset detection, so the user can use any music from their own collection for the rhythm game. Moreover, to make the game more realistic, AutoRhythm also allows the user to interact with the game via any object that can produce percussion sounds, such as a pen or a chopstick hitting against a table. AutoRhythm can identify the percussions in real time to replace tapping on the screen. This real-time user percussion identification is achieved based on the frame-based power spectrum of the filtered recording after background music reduction, which is performed based on the concept of active noise cancellation, with the estimated noisy playback music being subtracted from the original recording. Based on a test data set of 100 recordings, our experiment indicates that our system can achieve an F-measure of 78.22%, which outperforms other well-known classifiers and is quite satisfactory for the purpose of gameplay.
Scalable Network Design and Management with Decentralized Software-defined Networking

Network softwarization is among the most significant innovations of computer networks in the last few decades. The lack of uniform and programmable interfaces for network management led to the design of OpenFlow protocol for the university campuses and enterprise networks. This breakthrough coupled with other similar efforts led to an emergence of two complementary but independent paradigms called software-defined networking (SDN) and network function virtualization (NFV). As of this writing, these paradigms are becoming the de-facto norms of wired and wireless networks alike. This dissertation mainly addresses the scalability aspect of SDN for multiple network types. Although centralized control and separation of control and data planes play a pivotal role for ease of network management, these concepts bring in many challenges as well. Scalability is among the most crucial challenges due to the unprecedented growth of computer networks in the past few years. Therefore, we strive to grapple with this problem in diverse networking scenarios and propose novel solutions by harnessing capabilities provided by SDN and other related technologies. Specifically, we present the techniques to deploy SDN at the Internet scale and to extend the concepts of softwarization for mobile access networks and vehicular networks. Multiple optimizations are employed to mitigate latency and other overheads that contribute to achieve performance gains. Additionally, by taking care of sparse connectivity and high mobility, the intrinsic constraints of centralization for wireless ad-hoc networks are addressed in a systematic manner. The state-of-the-art virtualization techniques are coupled with cloud computing methods to exploit the potential of softwarization in general and SDN in particular. Finally, by tapping into the capabilities of machine learning techniques, an SDN-based solution is proposed that inches closer towards the longstanding goal of self-driving networks. Extensive experiments performed on a large-scale testbed corroborates effectiveness of our approaches. iii
Multi-objective Tabu Search 2: first technical report

The purpose of this document is to describe Multi-Objective Tabu Search 2 (MOTS2), which is a native mutli-objective optimiser. It has been developed to tackle a variety of real-world problems of engineering interest. The design and implementation are presented, followed by verification, validation and user instructions. At a glance, it involves introduction to the algorithm, explains configuration settings and structure, and results interpretation. Then, the optimiser is tested against a series of mathematical test functions in order to verify its functionality. The main goal is to demonstrate and assess the performance and applicability of the optimiser. The next step is to use MOTS2 on a real-world case, where the performance of optimising a 2D airfoil is validated and illustrated.
Design and application of a KNX‐based home automation simulator for smart home system education

Nowadays, automation systems have a wide use in buildings to make smart applications. Hence, many automation protocols such as 1‐wire, BACnet, C‐Bus, CC‐link, DALI, and KNX are developed. Among these protocols, the KNX protocol is preferred thanks to its some specifications such as easy and fast installation, user‐friendly software, and adaptation to the existing system. With increasing demand to construct KNX‐based smart home systems, developing the KNX‐based automation system simulator for the training of engineering students is required. A KNX simulator should be modular and user‐friendly. In addition, this simulator should have an expansion slot to use it together with different devices compatible with KNX protocol. In this study, a KNX simulator for engineering students training on smart home systems is designed and installed. The modules of simulator and KNX software programming tool ETS are explained. Besides, sample scenarios for engineering students to enhance and realize their projects are given.
Assessment of Camera Pose Estimation Using Geo-Located Images from Simultaneous Localization and Mapping

There is a need for a robust and easy to use indoor truthing system based on camera pose estimation. This research proposes a method for camera pose estimation using a one-time use of Simultaneous Localization and Mapping (SLAM) with high Size, Weight, Power and Cost (SWAP-C) sensors in order to enable future robust localization of low SWAP-C cameras. Determining position and orientation requires an accurate map of the environment. However, creating maps requires known positioning. Because indoor localization is difficult, creating indoor maps is also difficult. SLAM is a common method for resolving this paradox, solving for the position and map simultaneously. Using low SWAP-C cameras alone presents an additional set of problems. Errors stemming from repetitive patterns, featureless environments, or incorrect loop closures can all result in unstable, or worse, completely divergent solutions. The pivotal concept for this research is to combine the low SWAP-C cameras with an easy-to-use high SWAP-C mapping method. This truthing solution becomes feasible by making the initial SLAM pass as successful as possible, instead of trying to perfect vision-only SLAM with every low SWAP-C camera during every test. The problem of correct data association is solved by using uniquely-identifiable ArUco markers, resulting in optimal loop closures and landmark identification. Since the SLAM step is only performed once, additional sensors, in this case Light Detection and Ranging (LIDAR), can be used to improve accuracy. With an accurate map of images, the indoor navigation paradox is resolved. Vision navigation using the mapped images can be done for any camera by recovering a relative translation and rotation between images. This enables position truthing without