Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Types and Coping Mechanisms for Ethical Tourism Entrepreneurs: A New Conceptual Framework

Risk is a widely accepted entrepreneurial construct and entrepreneurship is a key feature of the tourism industry. Yet, investigating types of risks and calls for research on ethical entrepreneurship in tourism have largely been neglected. This research provides an original contribution to academia about risk-types and subsequent coping mechanisms as faced by ethical tourism entrepreneurs. Using methods from Personal Construct Theory, 15 in-depth interviews with self-defined ethical tourism entrepreneurs were conducted. An existing consumer risk framework (monetary, functional, social, and psychological risk) provided a priori themes for analysis. Through constant comparison of data, different forms of intelligence (survival, system, emotional, and spiritual) have emerged as coping mechanisms. These in vivo themes have been paired with risk-types to develop an original conceptual framework for risk faced by ethical tourism entrepreneurs. The implications of this framework are significant in providing support to nascent entrepreneurs, government start-up initiatives, and entrepreneurial incubator programs.
Artificial Intelligence-Based Drug Design and Discovery

The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practi-cal applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field.
Evaluation of Counselling Partnership’s ‘Survivors of Suicide’ service to inform future provision and commissioning

This is the final report of the evaluation of the Counselling Partnership - Survivors of Suicide service (SoS). SoS is based within the organisation Sussex Community Development Association (SCDA, Newhaven) and works in partnership with Counselling Plus (CP, Hastings); SCDA are fund holders and project managers, including for project development. SoS provides counselling services for those bereaved or affected by suicide. The evaluation was commissioned by East Sussex County Council (ESCC) Public Health; it is part of a programme of work funded by ESCC Public Health to reduce the high rate of suicide in East Sussex, with a particular focus on reducing deaths at the cliffs surrounding Beachy Head. An evaluation of Grassroots Training was undertaken alongside the evaluation of this project, with some shared methods and the findings of this evaluation are available in a separate report1 The evaluation was undertaken between September and December 2016. It assessed the following key areas: referrals, intake and assessment, experiences of the counselling process for 1-1 and group services, and outcomes. This report presents the findings.
Oxford Handbook of Clinical Skills in Adult Nursing

1. Principles 2. Communication skills 3. Assessment of the patient 4. Infection control 5. Pain management and 'last offices' 6. Drug administration 7. Peripheral intravenous access and care 8. Care of the theatre patient 9. Emergency and high dependency care 10. Respiratory system 11. Cardiovascular system 12. Gastrointestinal system 13. Genito-urinary system 14. Musculoskeletal system 15. Wound care 16. Neurological system 17. ENT System 18. Ophthalmology 19. Haematology
Estimation of Orientation and Position of the Manipulator Using the Extended Kalman Filter Based on Measurements From the Accelerometers

Abstract: Authors present the kinematic structure of measurement arm along with its construction for efficient estimation of orientation and position of the manipulator using extended Kalman filter. The major innovation of the arm is that it only uses accelerometers as gravity sensors for determining relative positions of the links. This article presents the problem of position estimation based on measurements with high noise and the use of the extended Kalman filter to limit the impact of noise on the measurement. Repeatability tests were performed using custom made test stand.
Multiagent Systems and Potential Fields to Smoke Dispersion Applied to Evacuation Simulations: The Case of Kiss Nightclub

ABSTRACT We often visit places with a large concentration of people, such as malls, football stadiums, restaurants or nightclubs. In the media, there are often reports of emergency situations in these places. It is known that in a fire situation, one of the main causes of death is the inhalation of smoke. Therefore, it is essential that at the start of emergency situations, people leave quickly to avoid possible injuries. We have investigated the dispersion of smoke in closed places and simulated the crowd behavior in these situations. This paper aims to present a new proposal to model smoke dispersion in closed environments using the concept of potential fields joined with cellular automata. To validate the work, we have tested our behavioral model in a real scenario, the Kiss nightclub tragedy.
Joint Source-Channel Coding and Digital Modulation Technique with Index Assignment Optimization

Channel optimized Index Assignment (IA) of source codeword is an approach to improve the transmission error resilience without the addition of extra bits and coding/decoding complexity. In this paper, we study the IA scheme with consideration to the given modulation technique. This study also presents an IA scheme for joint source channel coding and modulation technique for the general case when the codeword is not required to correspond to a modulation symbol and to occupy consecutive bits of the transmitted bitstream as some prior works. Besides the improvement in channel error robustness for the traditional IA approach, the proposed scheme can be combined with other techniques of mitigating or controlling the channel error when needed (in systems with high reliability or transmission of important codeword) such as Cyclic Redundancy Check (CRC), Forward Error Correction (FEC) coding and interleaving. We also consider the combination of the IA approach and FEC coding and the effectiveness of the proposed scheme is confirmed by experiments.
RBPF-MSIS: Toward Rao-Blackwellized Particle Filter SLAM for Autonomous Underwater Vehicle With Slow Mechanical Scanning Imaging Sonar

Simultaneous localization and mapping (SLAM) has the potential to play a fundamental and significant role in achieving full autonomy for autonomous underwater vehicles (AUV). This article proposes a Rao-Blackwellized particle filter (RBPF) SLAM algorithm for an AUV equipped with a mechanically scanning imaging sonar (MSIS) that has a very slow scanning frequency. To tackle the issues of scan distortion and sonar data sparseness caused by the slow-sampling MSIS, the core of the algorithm is a carefully designed sliding window-based scan forming module. Then the formed scans are fed into the modified RBPF to build a consistent grid-based map thus localizing the AUV accurately. Extensive simulation and experiments are carried out to verify the proposed algorithm. The results show that the proposed algorithm outperforms existing ones in terms of the level of map consistency with the environment as well as the accuracy of pose estimation.
Continuous Emotion Recognition in Speech - Do We Need Recurrence?

Emotion recognition in speech is a meaningful task in affective computing and human-computer interaction. As human emotion is a frequently changing state, it is usually represented as a densely sampled time series of emotional dimensions, typically arousal and valence. For this, recurrent neural network (RNN) architectures are employed by default when it comes to modelling the contours with deep learning approaches. However, the amount of temporal context required is questionable, and it has not yet been clariﬁed whether the consideration of long-term dependencies is actually beneﬁcial. In this contribution, we demonstrate that RNNs are not necessary to accomplish the task of time-continuous emotion recognition. Indeed, results gained indicate that deep neural networks incorporating less complex convolutional layers can provide more accurate models. We highlight the pros and cons of recurrent and non-recurrent approaches and evaluate our methods on the public SEWA database, which was used as a benchmark in the 2017 and 2018 editions of the Audio-Visual Emotion Challenge.
Using Prosody to Discover Word Order Alternations in a Novel Language

Native speakers of a language can use prosodic phrasing to disambiguate syntactically ambiguous sentences [1]. The current paper explores whether prosodic phrasing can help learners determine within-clause word order differences in a new language. Unlike many previous studies, we did not train participants in an artificial language, but exploited word order differences that occur in German. Native English speakers with no knowledge of German were trained with simple main clause sentences as well as complex sentences containing a subordinate clause. During training, prosodic phrasing of complex sentences either aligned or did not align with the sentences’ clause structure. The results from two experiments showed that the non-aligned prosodic phrasing helps learners discover clause internal word order differences in German, but only if syntactic variability in the test sessions is low. Overall, the results suggest that learners can exploit prosodic structure to learn word order alternations in certain contexts.
Investment decisions in an oil refinery in Brazil under a real option approach

Goal: The objective of this article is twofold: (i) analyze the investment in a new refinery in Brazil and identify the optimal moment to invest; and (ii) model the crack spread adjusted to the Brazilian market. Design / Methodology / Approach: The main uncertainties given by the crack spread and the foreign exchange rate were modeled as a continuous mean reversion model and geometric Brownian motion, respectively. The project was valued based on a real-option approach, including the option to postpone and the option to temporarily shut down. The first was assessed from analytical solution of the differential equation, while the latter was obtained from Monte Carlo simulation. Results: The investment decision changes depending on the expiration date of the postponement option and the stochastic treatment of the initial investment. The temporary shutdown option increases the value of the refinery and may change the decision of postponement. Limitations of the investigation: The crack spread was modeled based on international market because of limited availability of data from the Brazilian market. Additionally, results are dependent on the uncertainties and flexibilities modeled. Practical implications: The analysis comprising both options is especially relevant because there are refinery projects discontinued in Brazil, and the country is an oil products’ importer. Originality / Value: The paper contributes with an analysis in the refining industry considering the optimal moment to invest based on the interaction of two different options. Especially original is the crack spread modeling adapted to the Brazilian market.
Rate distortion trade-off in human memory

From a continuous stream of experience, how does the human brain determine what parts to remember and what to forget? It is extensively documented that humans are prone to systematic biases in these decisions. Such systematic biases are often construed as byproducts of adaptive processes. We argue that the computational resource constraints on memory can be formalised in the normative framework of lossy compression and that optimal adaptation to the constraints can be achieved by exploiting a generative model of the environment for compression. Recent advances in machine learning yielded powerful tools to approximate such solutions. In this study, we harness these advances to show that generative compression can explain a wide variety of memory phenomena including the effects of domain expertise on recall, gist based distortions in recalling lists of semantically related words and the influence of contextual cues in memory for hand drawn sketches.
Joint Student-Teacher Learning for Audio-Visual Scene-Aware Dialog

Multimodal fusion of audio, vision, and text has demonstrated significant benefits in advancing the performance of several tasks, including machine translation, video captioning, and video summarization. Audio-Visual Scene-aware Dialog (AVSD) is a new and more challenging task, proposed recently, that focuses on generating sentence responses to questions that are asked in a dialog about video content. While prior approaches designed to tackle this task have shown the need for multimodal fusion to improve response quality, the best-performing systems often rely heavily on human-generated summaries of the video content, which are unavailable when such systems are deployed in real-world. This paper investigates how to compensate for such information, which is missing in the inference phase but available during the training phase. To this end, we propose a novel AVSD system using studentteacher learning, in which a student network is (jointly) trained to mimic the teacher’s responses. Our experiments demonstrate that in addition to yielding state-of-the-art accuracy against the baseline DSTC7-AVSD system, the proposed approach (which does not use human-generated summaries at test time) performs competitively with methods that do use those summaries.
Detecting Communities with Multiplex Semantics by Distinguishing Background, General, and Specialized Topics

Finding semantic communities using network topology and contents together is a hot topic in community detection. Existing methods often use word attributes in an indiscriminate way to help finding communities. Through analysis we find that, words in networked contents often embody a hierarchical semantic structure. Some words reflect a background topic of the whole network with all communities, some imply the high-level general topic covering several topic-related communities, and some imply the high-resolution specialized topic to describe each community. Ignoring such semantic structures often leads to defects in depicting networked contents where deep semantics are not fully utilized. To solve this problem, we propose a new Bayesian probabilistic model. By distinguishing words from either a background topic or some two-level topics (i.e., general and specialized topics), this model not only better utilizes the networked contents to help finding communities, but also provides a clearer multiplex semantic community interpretation. We then give an efficient variational algorithm for model inference. The superiority of this new approach is demonstrated by comparing with ten state-of-the-art methods on nine real networks and an artificial benchmark. A case study is further provided to show its strong ability in deep semantic interpretation of communities.
Transforming multiple visual surveys of a natural environment into time-lapses

This article presents a new framework to help transform visual surveys of a natural environment into time-lapses. As data association across year-long variation in appearance continues to represent a formidable challenge, we present success with a map-centric approach, which builds on 3D vision for visual data association. We use a foundation of map point priors and geometric constraints within a dense correspondence image alignment optimization to align images and acquire loop closures between surveys. This framework produces many loop closures between sessions. Outlier loop closures are filtered in the frontend and in the backend to improve robustness. From the result map, the Reprojection Flow algorithm is applied to create time-lapses. The evaluation of our framework on the Symphony Lake Dataset, which has considerable variation in appearance, led to year-long time-lapses of many different scenes. In comparison with another approach based on using iterative closest point (ICP) plus a homography, our framework produced more and better-quality alignments. With many scenes of the 1.3 km environment consistently aligning well in random image pairs, we next produced 100 time-lapses across 37 surveys captured in a year. Approximately one-third had at least 20 (out of usually 33) well-aligned images, which spanned all four seasons. With promising results, we evaluated the pose error of misaligned image pairs and found that improving map consistency could lead to even better results.
A study on ECG Signal Analysis and ECG databases

-The ECG (Electrocardiogram) signal is continuous in nature and abruptly changing. For taking intelligent health care decisions related with heart diseases such as paroxysmal of heart, arrhythmia diagnosing, ECG signal needs to be analyze accurately. The ECG signal basically corresponds to the electrical activity of the heart. In the literature, the ECG signal has been analyzed and utilized for various purposes, such as measuring the heart rate, examining the rhythm of heartbeats, diagnosing heart abnormalities, emotion recognition and biometric identification. ECG analysis can contain several steps, such as pre-processing, feature extraction, feature selection and classification. Performing each step is crucial for the sake of the related analysis. In this work, the literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above. Each step in ECG analysis is briefly described, and the related studies are provided. Similarly, we have focused on the ECG database used for analysis.
Quantitative Analysis of Dynamically Provisioned Heterogeneous Network Services

Services in Network Function Virtualization (NFV) can have a variety of requirements such as data rates, latencies, and cost that can change during the lifecycle of services. To meet these requirements, various hardware and software resources are suggested for implementing Virtualized Network Functions (VNFs). However, meeting all service requirements using one implementation option is not always possible. For example, to improve the performance of VNFs, using acceleration hardware is proposed. Although acceleration hardware can improve the performance of a network function, as they are expensive appliances, they increase the cost of services; this might not be desirable for a particular service user or load that can be handled by cheaper resources. Dynamically provisioning services can solve this problem in which different implementations of VNFs are switched on the fly as service requirements change. In this paper, we analyse this service provisioning approach in terms of performance, cost, and management overhead by experimenting an example VNF.
Cortical and medullary vascularity in renal allograft biopsies

Aim: To evaluate the relation between cortical and medullary peritubular capillaries (PTCs) and scarring. There are presently no studies about medullary PTCs in renal allograft biopsies. Materials and methods: Nonprotocol allograft biopsies were evaluated and 41 with adequate medullary and cortical tissues were selected. Vascular structures were counted separately at the medulla and cortex on anti-CD34 stained sections. Other histopathological and clinical findings were retrieved from the patient files. Results: A weak but positive correlation was found between the number of cortical vascular structures (nVC) and medullary vascular structures (nVM). The nVC and nVM were decreased in cases with increased renal scarring; however, this was not statistically significant. A moderate negative correlation was found between nVC and blood creatinine (Cr) at the time of biopsy (P = 0.045, r = -0.32), but not between nVM and Cr. A moderate negative correlation was detected between both nVC and nVM and the most recent Cr (P = 0.01, r = -0.54 and P = 0.03, r = -0.51). Conclusion: nVC might be a valuable prognostic marker, as described previously. The relation between the latest Cr and nVM suggests that nVM might also be of value, a novel finding requiring further investigation. The weak correlation between nVC and nVM, which is described here for the first time, may point to unequal responses of the cortex and medulla to injurious factors.
Measuring Global Financial Linkages: A Network Entropy Approach

This paper measures the diversity of highly-connected financial networks using network entropy, and policy-related findings emerge from this research. With respect to the time variation of network entropy, international diversification of the global financial network constructed from foreign claims of international banks has decreased since the financial crisis of 2007–2008, while foreign claims among 20 reporting countries have concentrated more on core countries, such as the US and UK, since 2009. This change is more vividly captured by network entropy due to an unprecedented drop in the measurement. The results suggest that network entropy has promising potential in the financial market.
Usikkerhed som følgesvend når børn dør på intensiv afdeling

Engelsk Title: Uncertainty as companion when children die in ICU. The study explores care for dying children, their parents and family in NICU and PICU. This care is dimly described in the scientific literature. The purpose of the study was to explore the phenomenon, how nurses in NICU and PICU experience end-of-life care to child, parents and family, after the decision of treatment completion has been taken, and the child dies. This study includes ten empirical articles from the time period of 1997-2010. These articles were selected based on a systematic search in the databases: PubMed, CINAHL, Scopus, PsycINFO and Swemed +. Max van Manens 4 eksistentials: Lived space, Lived body, Lived time and Lived human relation formed the 4 research questions which were used as a method to identify findings. 'Balancing with uncertainty as companion ' emerged as the essential meaning of the phenomenon and lack of human life discourse seams significant in relation to the perceived uncertainty.