Miracle-dz/UniFAR-FTUs huggingface dataset:
Invalid-Resource-Aware Spectrum Assignment for Advanced-Reservation Traffic in Elastic Optical Network

Elastic optical networks (EONs) can make service accommodation more flexible and precise by employing efficient routing and spectrum allocation (RSA) algorithms. In order to improve the efficiency of RSA algorithms, the advanced-reservation technique was introduced into designing RSA algorithms. However, few of these advanced-reservation-based RSA algorithms were focused on the unavailable spectrum resources in EONs. In this paper, we propose an Advanced-Reservation-based Invalid-Spectrum-Aware (AR-ISA) resource allocation algorithm to improve the networking performance and the resource alignment of EONs. By employing a new index, Invalid Spectrum Rate (ISR), to record the proportion of unavailable spectrum resources in EONs, the proposed AR-ISA algorithm set a network load threshold to trigger the postponement of an arriving service. Compared with the traditional slack-based AR mechanism, the proposed algorithm has more concerns about the current spectrum usage of the path designated by the service than the conflicts between AR services and other existing services. To further increase the networking performance, the proposed algorithm adopts defragmentation to increase the number of available spectrum resources when postponing a service. Theoretical analysis and simulation results show that the proposed AR-ISA algorithm has obvious effectiveness in reducing the service blocking rate and increasing the spectrum alignment rate.
Next generation 50G PON flexible transmitters based on directly modulated VCSELs

We consider long-wavelength vertical cavity surface emitting lasers (VCSELs) directly modulated with discrete multitone (DMT) signals as a very promising solution for implementing energy-efficient transmitters for 50G passive optical networks (PONs). Two scenarios based on single sideband and dual sideband modulation are taken into account. Preliminary experimentation with already available short-cavity VCSELs operating in the third window is presented to show the PON performance as a function of the accumulated chromatic dispersion and of the received power. Experimental results are also used to validate a simulation tool, which is further used to provide the performance evaluation of transmitters based on next-generation short cavity VCSELs with higher bandwidth (up to 20 GHz), operating in the O band. Thanks to its water-filling nature, DMT is demonstrated to enable PON flexibility: in fact, considering the statistics regarding a commercially deployed PON, a significant increase of the maximum aggregated capacity is provided, optimizing the PON resource usage with respect to the losses and dispersion impairments.
Role of advanced glycation end products and insulin resistance in diabetic nephropathy

Abstract Metabolic syndrome (MetS), i.e. a cluster of physiological and biochemical abnormalities can lead to diabetic nephropathy (DN). Insulin resistance, impaired fasting glucose are the main signs and symptoms of MetS. Excess sugar can induce various substantial structural changes like formation of advanced glycation end products (AGEs). AGEs are formed due to reaction of reducing sugars with amino groups of proteins, lipids and nucleic acids. AGEs when bound to the receptor for advanced glycation end products (RAGE) activate increased production of pro-inflammatory markers like interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α) along with induction of endoplasmic reticulum (ER) stress. Accumulation of AGEs, enhanced reactive oxygen species (ROS) generation and activation of protein kinase C (PKC), are considered to induce glomerular hypertrophy, podocyte apoptosis, therefore contributing to the development and progression of DN. In this review, we decipher different biochemical and physiological factors that link AGEs and DN.
Tongue Identification for Small Samples Based on Meta Learning

With the development of computer vision, deep learning has obtained certain achievements. Deep learning networks are usually trained with a large amount of tongue images. However, in reality, there are few tongue images in some categories, which make deep learning impossible to use. Besides, collecting mass images of tongue is too expensive and hard to apply. Therefore, a tongue identification method based on meta learning is proposed. According to the characteristics of tongue image, the feature embedding with domain knowledge and mixed loss function are designed. They are used to train the classifier based on prototypical network. Experiments show that the accuracy of this method is improved and has good generalization.
Continuous estimation of emotional change usingmultimodal affective responses

Emotions have a significant effect on our daily behavior, such as perception, memory, and decision making. For this reason, interest in considering the emotions of the user in a human-computer interface has recently increased. This is important for future interface applications, which are expected to operate in harmony with humans. In this paper, we present our approach to instantaneously detecting the emotions of video viewers from remote measurement using an RGB camera. Facial expression and physiological responses, such as heart rate and pupil diameter, were measured by analyzing facial videos. We also verified the effectiveness of the contactless measurement by acquiring electroencephalogram signals using a contact-type electroencephalograph. By combining the measured responses into multimodal features and using machine learning, we showed that the results of emotion estimation were better than estimates made from only single-mode features.
Development and validation of the short-form Ryff's psychological well-being scale for clinical nurses in Taiwan

Background: Research into clinical nurses' psychological well-being (PWB) is limited and fragmented. A reliable and valid measure is needed to advance the field of nurses' mental health. Objective: To examine the psychometric validity of Short-Form PWB Scale (PWBSs) among clinical nurses. Methods: This study adopted a cross-sectional design. Randomized cluster sampling was used to recruit clinical nurses from a medical center in Taipei, Taiwan, from July to October 2015. Nurses were recruited if they worked for more than 3 months in the medical center. The 84-item PWBS was used to assess PWB. Based on exploratory and confirmatory factor analysis in this study, an 18-item version of Ryff's PWBS was developed using 474 respondents. The Short-Form PWBS was tested for internal consistency, construct validity, and criterion-related validity. Results: Cronbach's alpha was 0.88, with aggregated subscale alphas of 0.72–0.88, except 0.57 for autonomy. Good construct validity and criterion-related validity of the Short-Form PWBS were found. Conclusions: The study results showed that the Short-Form PWBS is suitable and recommended for Taiwanese clinical nurses.
Non-invasive stimulation of the social brain: the methodological challenges

Abstract Use of non-invasive brain stimulation methods (NIBS) has become a common approach to study social processing in addition to behavioural, imaging and lesion studies. However, research using NIBS to investigate social processing faces challenges. Overcoming these is important to allow valid and reliable interpretation of findings in neurotypical cohorts, but also to allow us to tailor NIBS protocols to atypical groups with social difficulties. In this review, we consider the utility of brain stimulation as a technique to study and modulate social processing. We also discuss challenges that face researchers using NIBS to study social processing in neurotypical adults with a view to highlighting potential solutions. Finally, we discuss additional challenges that face researchers using NIBS to study and modulate social processing in atypical groups. These are important to consider given that NIBS protocols are rarely tailored to atypical groups before use. Instead, many rely on protocols designed for neurotypical adults despite differences in brain function that are likely to impact response to NIBS.
System Dynamics in Tourism Planning and Development

ABSTRACT System dynamics (SD) is a method that has the ability to capture the dynamic behavior of a complex system over time. The tourism industry, due to the myriad of interactions among its sectors, can be considered as a complex system. Therefore, SD has drawn the attention of tourism researchers over the last two decades. The goal of this study is to assess the application of the SD method in planning and development of the tourism industry. For this purpose, a systematic literature review was performed and a set of 27 papers was selected. The analysis of the papers shows the applicability of the SD method to address a multitude of different problems. Overall, however, it can be concluded that whilst the SD method has shown considerable potential to provide tourism decision-makers and regulators with tools for strategic and operational policy development at many different levels of analysis, the number of applications in this sector is still limited. Therefore, it is recommended that the use of SD modeling in the tourism industry be extended in order to promote a holistic understanding of the complex issues faced by this industry and to assist in the development of more effective policies.
Partially Explainable Big Data Driven Deep Reinforcement Learning for Green 5G UAV

UAV enabled terrestrial wireless networks enables targeted user-centric service provisioning to en-richen both deep urban coverage and target various rural challenge areas. However, UAVs have to balance the energy consumption of flight with the benefits of wireless capacity delivery via a high dimensional optimisation problem. Classic reinforcement learning (RL) cannot meet this challenge and here, we propose to use deep reinforcement learning (DRL) to optimise both aggregate and minimum service provisioning. In order to achieve a trusted autonomy, the DRL agents have to be able to explain its actions for transparent human-machine interrogation.We design a Double Dueling Deep Q-learning Neural Network (DDDQN) with Prioritised Experience Replay (PER) and fixed Q-targets to achieve stable performance and avoid over-fitting, offering performance gains over naive DQN algorithms. We then use a big data driven case study and found that UAVs battery size determines the nature of its autonomous mission, ranging from an efficient exploiter of one hotspot (100% reward gain) to a stochastic explorer of many hotspots (60-150% reward gain). Using a variety of telecom and social media data, we infer driving Quality-of-Experience (QoE) and Quality-of-Service (QoS) metrics that are in contention with UAV power and communication constraints. Our greener UAVs (30-40% energy saved) address both quantitative QoS and qualitative QoE issues. Partial interpretability in the reinforcement learning is achieved using data features extracted in the hidden layers, offering an initial step for explainable AI (XAI) connecting machine intelligence with human expertise.
Cooperative Edge Caching: A Multi-Agent Deep Learning Based Approach

Ubiquitous Internet of Things (IoT) devices have fueled plenty of innovations in the emerging network paradigms. Among them, IoT edge caching has emerged as a promising technique to cope with the explosive growth in network data traffic, with Quality of Service (QoS) improved and energy saved. However, the intrinsic storage limitations of the edge servers poses a critical challenge for the IoT edge caching system. Enabling edge servers to cooperate with each other can provide a potential perspective to improve the edge storage utilization widely discussed. Nevertheless, it also incurs an additional communication overhead, eventually making the caching system more complex. As a result, how to perform an efficient cooperative caching becomes a critical issue. Thus, in this paper, we propose a deep reinforcement learning-based cooperative edge caching approach, which allows the distributed edge servers to learn to cooperate with each other. Specifically, edge servers determine their cache actions based on the local caching state. After that, the centralized remote server evaluates these actions and feeds back the evaluation results to edge servers for subsequent caching actions optimization. We show that, by designing an appropriate reward function, our approach promotes cooperation between edge servers as well as improving the system hit rate. On this basis, we consider a practical and reasonable scenario with inconsistent data item size and propose a novel multi-agent actor-critic caching algorithm. Extensive simulation results demonstrate the performance improvement using our proposed solution over three other caching algorithms.
DBA Algorithm for Cooperative Resource Sharing among EPON Customers

Infrastructure service providers (InPs) can employ bandwidth sharing to offer new services and business models to their customers. In this paper, we introduce a dynamic bandwidth allocation (DBA) algorithm which allows cooperation among Ethernet PON (EPON) customers so that they can share unused bandwidth among themselves without affecting their guaranteed bandwidth. Simulation results show that our proposal increases the throughput and decreases the delay for cooperative customers.
Craniofacial exoskeleton: cyborg realm or new age reality?

From the science fiction movies of the 80s to getting an FDA approval in 2018, exoskeletons have sure come a long way from being merely a subject of fantasy and wonder. Exoskeletons are electromechanical devices worn by a human operator and designed to increase physical performance of the wearer. Within the last few years, exoskeletons have been found to be of immense use in multiple aspects of healthcare. The most common being musculoskeletal rehabilitation post stroke/paresis/degenerative muscular diseases [1]. Static exoskeletons have also found use in form of 3D casts and stabilization splints in orthopaedics [2]. As the understanding and technological support is increasing, the niche for exoskeleton use increases manifold, every passing day. Encountering an article recently on APOSEMA [3], a soft robotic mask that uses biometric sensors and an augmented reality digital layer to exaggerate and translate human expressions, our curiosity took a few leaps ahead to wonder how exoskeletons could turn abound the application of augmented physical power in craniofacial region. The potential areas that could benefit from craniofacial exoskeleton are static/3D exoskeletons for jaw immobilization in cases of fractures. Another area of interest could be powered exoskeleton for jaw exercises and mobilization. Many patients lack compliance with these exercises despite adequate pain control. A powered exoskeleton with an inbuilt automated exercise protocol could obviate the need of compliance and avoid jerky painful mouth opening by existing devices. The assisted opening could be continuous and calibrated much more finely for patients requiring exercises like post-surgery oral submucous fibrosis, temporomandibular joint ankylosis, radiation fibrosis or paediatric patients. Selectively restrictive exoskeleton assisted opening devices could be used for non-surgical candidates of recurrent condylar dislocation. Similar exoskeleton devices with digital interface could be a boon for maxillofacial prosthetics requiring bionic eye/ lip/nasal prosthesis. Another possible area could be powered exoskeleton assisted dentofacial orthopaedics, orthodontic tooth movements, palatal expansions and distraction osteogenesis. The feasibility and application of exoskeletons in the unexplored niche of craniofacial region, whether static or powered, cannot be comprehended at present, but it is definitely much ahead of being a meagre cyborg realm fantasy.
Hibernation Site Selection by Eastern Massasauga Rattlesnakes (Sistrurus catenatus catenatus) near Their Northern Range Limit

Abstract Hibernation sites at higher latitudes must protect snakes from colder conditions for longer periods of time. Because fewer locations are likely to be suitable, hibernation site availability may restrict the northern distribution of snakes. We considered overwinter mortality, hibernation site fidelity, and the abundance of suitable hibernation sites based on surface features to assess whether Massasauga Rattlesnakes are likely to be limited by hibernation site availability on the Bruce Peninsula, Ontario, Canada. We used three years of radio telemetry to locate 46 hibernation sites of 32 individual snakes. Snakes hibernated individually in old root systems, rodent burrows, and rock crevices in forested areas. Hibernation sites could be differentiated from forested areas generally available to snakes but not from locations with holes and crevices in the immediate vicinity of hibernation sites. Few snakes hibernated in the same location in consecutive years, although most (>70%) hibernated within 100 m of their previous location. Overwinter mortality over three years (23%) was similar to mortality during the active season (21%). Our results suggest that Massasauga Rattlesnakes may be limited by the availability of suitable hibernation sites, but sites of similar quality to those used by overwintering snakes are locally abundant. The location of hibernation sites within forests could not be predicted reliably based on surface features. Therefore, efforts to conserve habitat for this threatened species should consider all forested areas on the Bruce Peninsula as potential hibernation habitat.
Location of Optic Disk in the Fundus Image Based on Visual Attention

optic disk (OD) localization is a critical step before OD segmentation which is very important for computer-aided diagnosis. The research on deep learning promotes the development of intelligent medical diagnostic system, but medical images often need for a relatively large amount of calculation. The algorithm simulates the human visual attention to automatically detect the position of the OD for preprocessing of deep-learning method. In this paper, there are four steps:(a) extract some basic visual features from the fundus image;(b) integrate different features by their region co-variances;(c) include a boundary bias into the model according to the spatial information of optic disk;(d) locate the optic disk by saliency map optimized by morphological methods. The proposed method was tested on two benchmark datasets, containing 1600 fundus images of both normal and diseased retinas. The OD is detected correctly in 1195 out of 1200 images (99.58%) using the publicly available MESSIDOR dataset. In addition, the detection accuracy is 100% on DRIVE, which outperforms the state-of-art models. More importantly, the candidate OD region on two da-tasets are provided.
Brain microstructural changes and cognitive function in non-demented essential tremor patients: a diffusion tensor imaging study

Abstract Background Essential tremor (ET) is disease with both motor and non-motor features. Notable among the non-motor features is cognitive impairment. While this impairment has been attributed to cortico-thalamo-cerebellar pathway pathology, it is likely that a more complicated involvement of brain structures underlies cognitive function in ET. Objective To evaluate the brain microstructural changes of both white matter and grey matter in ET using region of interest based diffusion tensor imaging (DTI), and to correlate these changes with cognitive function assessed during detailed neuropsychological testing. Method Thirty-five non-demented ET patients with a range of cognitive function (Clinical Dementia Rating = 0–0.5, mean age = 57.5 ± 16.7 years, age range = 23–76 years) underwent a comprehensive neuropsychological evaluation and brain magnetic resonance imaging, including DTI. DTI findings were reported as fractional anisotropy, average diffusion coefficient, these values were evaluated for 32 ROIs. Cognitive domains included attention, visuospatial functions, executive function, verbal memory, visual memory, and language. Domain Z-scores were calculated each cognitive domain and compared for each brain region. Results Microstructural changes in prefrontal cortical areas (dorsolateral, ventrolateral), paralimbic and limbic structures (posterior cingulate cortex, precuneus, hippocampus), basal ganglia (substantia nigra, putamen, caudate nucleus) and white matter bundles (corpus callosum, anterior thalamic radiation, longitudinal fasciculus, frontooccipital fasciculus, etc.) correlated with specific domains of cognitive function in ET patients. Conclusion These data suggest that not only the cerebello thalamocortical pathway, but numerous other brain structures are related to level of cognitive performance and possibly underlie cognitive dysfunction in ET.
Insecticidal and synergistic effects of Majorana hortensis essential oil and some of its major constituents

The essential oil from leaves of Majorana hortensis Moench (Lamiaceae) was isolated by hydrodistillation with a yield of 1.6% (wt/wt). The insecticidal activity of the oil was evaluated against fourth instars of Spodoptera littoralis Boisduval (Lepidoptera: Noctuidae) and adults of Aphis fabae L. (Hemiptera: Aphididae). The oil showed a remarkable toxic effect against S. littoralis in a topical application assay (LD50 = 2.48 μg per larva) and in a residual film assay (LC50 = 3.14 g/l). The oil of M. hortensis also exhibited a pronounced toxic effect against A. fabae adults with LC50 values of 1.86 and 2.27 g/l in rapid dipping and residual film assays, respectively. Gas chromatography‐mass spectrometry analyses of M. hortensis essential oils revealed the presence of 31 compounds and the main components were terpinen‐4‐ol (30.0%), γ‐terpinene (11.3%), and trans‐sabinene hydrate (10.8%). Repeated column chromatography of M. hortensis oil on silica gel led to the isolation of two major constituents, which were characterized based on 1H‐nuclear magnetic resonance and mass spectrometric data, as terpinen‐4‐ol and γ‐terpinene. These two components were examined for their insecticidal and synergistic activities towards S. littoralis and A. fabae. Terpinen‐4‐ol and γ‐terpinene exhibited a significant insecticidal activity against both insects, but γ‐terpinene was more toxic than terpinen‐4‐ol. When tested in a binary mixture with the synthetic insecticides profenofos and methomyl, it was found that both compounds enhanced the insecticidal activity of these insecticides by two‐ to threefold. These results show that terpinen‐4‐ol and γ‐terpinene have a synergistic effect on the insecticidal activities of synthetic insecticides profenofos and methomyl.
A Switched Systems Approach to Unknown Environment Exploration with Intermittent State Feedback for Nonholonomic Systems

A method is provided to enable a nonholonomic vehicle to explore an unknown environment with intermittent state feedback. A maximum dwell-time condition is determined via a Lyapunov-based switched systems approach to maintain overall system stability despite the intermittent loss of state feedback and the presence of external disturbances. A minimum dwell-time condition is determined via a Lyapunov-based switched systems approach to ensure the tracking error converges within a desired neighborhood of the desired trajectory. Utilizing the proposed maximum and minimum dwell-time conditions, a nonholonomic vehicle’s tracking error remains globally uniformly ultimately bounded, enabling the exploration of the feedback-denied region for a predetermined period of time, before acquiring state feedback.
Project Implementation Performance in Reward-Based Crowdfunding: An Emotion Perspective

Reward-based crowdfunding provides new financing channels for entrepreneurs. Based on the emotion perspective, this paper uses text analysis technology to extract emotions from the comments of crowdfunding projects, and analyzes the predictive effect of project implementation performance. The main conclusions are as follows: Sadness and trust emotions are negatively correlated with implementation performance, and joy emotions can positively predict project implementation performance.