Miracle-dz/UniFAR-FTUs huggingface dataset:
From Virtual Reality to Augmented Reality: Devices, Bodies, Places and Relationships*

We can consider augmented reality (AR) a true evolution of virtual reality (VR). AR is an interesting synthesis of those socially structured processes that have led, in a short time, to a maturation of the digital world. In light of all this, we intend analyse the materiality of the devices and tools (Tirino 2017) necessary to create augmented environments in order to shed light on the sociocultural processes that shape the media. We investigate how AR has led to the reaffirmation of the centrality of bodies in the experience (Damasio 1994, 1999). Plural bodies are called to interact in environments increasingly similar to hyperplaces (Bolognari, 2012). In hyperplaces the same body/mind dichotomy tends to blur, opening up to new dynamics of structural coupling with the environment (Maturana, Varela 1972). We are talking about dynamics with obvious consequences on the self and hetero perception of identity. In the light of those new expressive techniques (Montani 2020) and forms of convergent and participatory culture (Jenkins 2006, 2010), we reflect on how AR devices and applications could affect in-person sociability.
Photovoltaic Plant Inspection by means of UAV: current practices and future perspectives

Recent trends show the importance of photovoltaic (PV) system inspection: in fact, it allows the increase in their performance and profitability. Moreover, it represents an important step for the safety and asset management of PV plants. Among all the possible inspection techniques, the use of Unmanned Aerial Vehicles (UAVs) has proven to be very effective in obtaining excellent results with a competitive cost: in particular, this type of inspection allows to drastically reduce inspection times with good quality of the results obtained. Finally, this inspection technique has been well combined with the development of image processing techniques capable of analyzing and processing the data acquired in the field quickly and reliably. The aim of this paper is to review the current practice in UAV-based PV plant inspection and to analyse future perspectives.
Automatic Voice Synthesis System using Long Short-Term Memory and Google Mediapipe

Lip reading is the process of visual speech recognition that has many applications in various fields. Among these applications, visual speech recognition is an essential task in communication for people who experience hearing disorders, or for those who have lost their voice due to damages to their vocal cords. To address these problems, advancements have been made in the field of machine learning to decode speech from human lip movements and promise patients a chance at restoring their communicative functions. This paper focuses on a visual speech recognition technique that first employs a facial landmark-based feature extraction method using Google's Mediapipe library, and subsequently trains a model using a Long-Short-Term Memory (LSTM) network. The library is used to capture 40 keypoints from each detected face and a single-layer LSTM network is used to train the model with these extracted features. The model achieved an accuracy of 86.5% on the test set, gathered from the Oxford-BBC Lip Reading in the Wild (LRW) dataset.
Artificial Intelligence based Fake News Classification System

In today's world, news information is spreading across all social media platforms rapidly, and most of the pieces of news are fake, and it affects everyday people's lives. Sometimes they may lose their life too due to this. Moreover, many people spread rumors purposely; hence there is a need for a system that can tell the user whether the news is correct and they can trust it or not. Using machine learning and natural language processing, it can be achievable Where a machine learning model can be trained that will be able to predict whether the news is fake or accurate based on the historical data which contains the unique patterns. So using this model system can indicate whether the information is fake or real. Also, natural language processing can be used to improve the performance of the model.
Real-Time Herb Leaves Localization and Classification Using YOLO

Herb leaves plays a significant role to cure various human disease throughout the world. Furthermore, it has had a great impact on clinical science. People have less information about herbs accordingly it turns into an issue to recognize them. Besides, utilizing harmful plants as a drug would genuinely expand the danger of life. As a result, it becomes a significant issue to recognize the useful herb leaves. Very few works have been done to localize and classify the herb leaves in real-time for helping people. Gradually the utilization of artificial intelligence makes our life simpler and user-friendly. Complex problems can be effectively solved with the help of artificial intelligence. In this paper, we will classify the five types of herbs respectively Mehndi, Betel, Mint, Basil, and Aloe Vera. We have proposed a neural network model using YOLO which will use for the classification of the herb leaves. In the future, we will carry out the prepared model into a versatile application that will assist the client with finding out about homegrown cures. The Classification accuracy of our model is about 95% which is promising enough compared to other relevant works.
Post Growth Fashion and the Craft of Users

In order to bring about change, sustainability has to be real to people. Yet we know very little about people’s everyday experiences of fashion and sustainability. For instance, how does it feel to wear a garment that connects us with others? What is our understanding of engaging with sustainability through the clothes we wear? Just as each of these personal experiences is different, so is each person experiencing them. Yet most of our experience of fashion has no space for this heterogeneity: instead what we face is a limited set of fashion encounters, reproduced in London, Tokyo or Sydney. This is but one of many flashpoints that signal the profound nature of the sustainability challenge for the fashion sector. A challenge that requires us to transform not only fashion products and manufacturing processes but also fashion’s context, its rules and goals, business models and methods of promotion. In this chapter I explore the interconnections between real, lived experiences of sustainability in fashion and what they suggest about the shape and structure of the industry that formed them. My interest is in amassing a collection of ideas that improve fashion qualitatively and not in quantitative scale. Together these ideas offer a tentative glimpse of prosperity in fashion that exists outside the economic and business model of growth.
Engineering Education, Is Problem-Based or Project-Based Learning the Answer

Dr Nathan Scott Dept. of Mechanical & Materials Engineering The University of Western Australia 35 Stirling Hwy, Crawley 6009 Western Australia nscott@mech.uwa. edu.au Assoc. Prof. Roger Hadgraft Teaching & Learning Director School of Civil and Chemical Engineering RMIT University Melbourne, Australia roger.hadgraft@rmit.edu.au Dr Vojislav (Vic) Ilic College of Science, Technology & Environment Uni of Western Sydney, Kingswood Campus Locked Bag 1797, S. Penrith DC NSW 1797 v.ilic@uws.edu.au
Assessment of Quality and Completeness of Archived ITS Sensor Data: TransGuide Case Study

Previous research has reported on the need to implement quality control programs for ITS data to address critical issues such as suspicious or erroneous data, nature and extent of missing data, and accuracy and comparability of ITS data to similar data sources. This paper summarizes the work completed to address quality control and completeness issues associated with a very large archived ITS data set composed of some 3.4 billion 20-second lane detector data records from San Antonio’s TransGuide. The paper includes a description of the quality control tests utilized, the results of the analysis conducted, and a discussion of ITS data completeness issues. The researchers also utilized GIS to prepare maps showing the spatial trends in the distribution of quality control flags. These maps showed cases where the spatial distribution was roughly uniform, but also cases where there were significant exceptions. An evaluation of temporal variations in the distribution of quality control flags showed that, in most cases, the highest concentration of flagged records occurred at night, when there was relatively little traffic and, consequently, there was a higher chance for time intervals without vehicles or for isolated detector readings producing abnormal data. Finally, the researchers evaluated the data completeness both at the aggregate level (by server) and a more detailed individual detector level. This paper not only provides a revised set of quality-control tests, it also describes an innovative spatial and temporal analysis of data quality for two-year worth of data. While the analysis described in this paper uses data from one jurisdiction (San Antonio, Texas), the methodology is sufficiently generic to enable implementation at other traffic management centers.
Volunteer-family conflict and turnover intentions: Exploring the role of psychological contract features

Volunteer turnover is a key issue for non-profit organizations and various reasons for turnover have been explored in the literature. I introduce a new concept that captures conflict between the volunteering and the family domains in life and test whether it can explain turnover of volunteers. Moreover, I test whether features of volunteers' psychological contracts can explain why volunteers experience conflict between these domains in life. These features capture the key characteristics of the exchange relationship and the mutual obligations between the volunteer and the non-profit organization. As such, the goal of this study is to test a model in which volunteer-family conflict mediates the relationship between psychological contract features and volunteer turnover. The results of the study cannot confirm that volunteer-family conflict mediates this relationship. However, the findings support that volunteers who experience conflict are more likely to quit, and that the time-frame and stability features of the psychological contract can explain why volunteers experience conflict. In particular, I show that a long-term time-frame in volunteers' psychological contracts is a double-edged sword for non-profit organizations: while it directly reduces the likelihood of volunteers quitting, it also increases the risk of volunteer-family conflict. Overall, this study shows that volunteer-family conflict can be a valuable concept to understand volunteers' behaviors.
Theoretical Analysis and Experimental Investigation of a Bidirectional Inductive Power Transfer System Applicable for Electric Vehicle Charging

The utilization of IPT technology in electric vehicles (EVs) charging applications and in the microgrid restoration/islanding mode, has the potential to bring about a change in how EVs are perceived today. Firstly in consumer level, by making the charging process more convenient, safe and user-friendly, by removing the messy cords , and secondly in power system level by increasing the reliability and resilience of distribution networks, contributing into the micro grid healing procedure through bidirectional power flow capability. To this end, the interconnection of the EV with the low voltage grid ac-buss is examined. The interconnection is fulfilled in two stages. The first stage considers a boost converter for the conversion of the low frequency 50 Hz ac-buss to regulated dc-buss. The second stage utilizes the IPT technology and LCL compensation topology to charge wirelessly the battery of the EV. This thesis focuses on development of firm theory and experimental investigation of the above mentioned LCL-IPT topology. With the proposed topology bidirectional flow is achieved while, the efficiency of the system, considering Silicon Carbide MOSFETs, was calculated to be around ~93%. To verify the many analytically deduced theoretical considerations and developed models an experimental setup is built. For the experimental set-up, SiC-based MOSFETs have been utilized as the converters switches. Eventually, a high efficient voltage-controlled experimental prototype operated at 110 kHz, able to realize bidirectional inductive power transfer is constructed. The experimental results verified the developed theory and developed models. The efficiency of system was measured to be as high as 92.5%, even though the LCL filter losses are not optimized.
BULGARIAN ACADEMY OF SCIENCES

Recent Activity Daily Living (ADL) not only tackles simple activities, but also caters to a wide range of complex activities. Although the same activity has been carried out under the same environmental conditions, the acceleration signal obtained from each subject considerably differs. This happens due to the pattern of action generated for each subject is diverse based on several aspects such as subject age, gender, emotion and personality. This project therefore compares the accuracy of various machine learning models for ADL classification. On top of that, this research work also scrutinizes the effectiveness of various feature selection methods to identify the most relevant attribute for ADL classification. As a result, Random Forest was able to achieve the highest accuracy of 83.3% on subject independent matter in ADL classification. Meanwhile, CFS Subset Evaluator is considered to be a good feature selector as it successfully selected the 8 most relevant features compared with Correlation and Information Gain Evaluator.
A Value Focused Thinking Model for the Development and Selection of Electrical Energy Source Alternatives at Military Installations

Abstract : Electrical power on military installations is vital for mission accomplishment. Most installations obtain electrical power from a local commercial utility. Although commercial power service has a very low interruption rate, the threat of a sustained power outage resulting from a terrorist act or a natural disaster is of concern. The military should posture itself to prevent such power outages and prepare to mitigate the adverse affects associated with the loss of power. This thesis presents a Value Focused Thinking approach to the development of a decision analysis model to assist a decision maker at a military installation in the generation and selection of back-up energy alternatives. The model attempts to capture the value to be gained by implementing back-up power systems which utilize fossil fuel powered generators in combination with renewable energy resources and assist the decision maker in selecting an alternative which best suits the needs of the installation. The thesis also includes a case study involving the application of this model to the United States Marine Corps installation in Twentynine Palms, California.
A Survey on Big Data Analytics Model

Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.
Personalisation of a 3D Macroscopic Cardiac Electrophysiology Model for Simulation of Induced Ischemic Ventricular Tachycardia

Despite recent efforts in cardiac electrophysiology modelling [1], there is still a strong need to make macroscopic models usable in planning and assistance of the clinical procedures. This requires model personalisation i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Fast macroscopic models allow a quick estimation of the tissue conductivity, but are often unreliable in prediction of arrhythmias [2]. On the other side, complex biophysical models are quite expensive for the tissue conductivity estimation, but are well suited for arrhythmia predictions. Here we present a coupled personalisation framework, which combines the benefits of the two models. A fast Eikonal model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell-Schaeffer (MS) model. Additional parameters related to Action Potential Duration (APD) and APD restitution curves for the tissue are estimated for the MS model [3]. This framework is applied to a clinical dataset provided with an hybrid X-Ray/MR imaging on an ischemic patient shown in figure 1. The estimated parameters using the described framework are shown in figure 2. A qualitative comparison of measured data with model simulations is shown in figure 3. This personalised MS Model is then used for in silico simulation of clinical Ventricular Tachycardia (VT) stimulation protocol [4] to predict the induction of VT, as shown in figure 4. This proof of concept opens up possibilities of using VT induction modelling directly in the intervention room, in order to plan the radio-frequency ablation lines.
Utility and safety of cobalt chromium needles for endobronchial ultrasound-guided transbronchial needle aspiration: a retrospective single-center study

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a standard method for obtaining specimens of mediastinal and hilar lesions, and several types of needle of various sizes and materials are available. This study aimed to compare the utility and safety of two needles, cobalt chromium (CC) and stainless steel (SS), for EBUS-TBNA. Methods: This retrospective study included data of patients who underwent EBUS-TBNA with a 22-gauge needle made from either SS (38 patients, 121 punctures) or CC (39 patients, 145 punctures), and procedure time, histological data, complication rates were compared. Results: There were no significant differences between the groups in the baseline characteristics of the patients or lesions or in the complication rates. Although diagnostic yields in each patient who underwent EBUS-TBNA with the two needle types were similar, significantly shorter procedure time (22 min vs. 26 min, p = 0.007), diagnostic histologic sampling yield in each sample (71.0% vs. 58.7%, p = 0.039), fewer samples with cartilage alone (1.4% vs. 6.6%, p = 0.047) and fewer samples containing cartilage (7.6% vs. 16.5%, p = 0.034) were seen in the CC group compared with that in SS needle group. Conclusion: Compared with SS needles, CC needle for EBUS-TBNA showed significant shorter procedure time and higher ratio of getting diagnostic histological specimens in each sample. This might be because of better ability of CC needle to puncture through the trachea and bronchial cartilage to get appropriate lymph node sampling.
Sonic Constructs: Robotics and the Residue of Sound

Sonic Constructs is an interactive sound installation that uses Lego Mindstorms? semi-automata musical robots. In Sonic Constructs, two robotic devices move and interact while performing trajectories that produce sound as a by-product of the movement itself. Direction, speed, acceleration, position, scratching and collision characterise an environment for kinetic and acoustic participation. This paper investigates a critical context relating to the use of robotics in the digital arts.
Individual Differences and Mindfulness

What makes some people more mindful than others? Previous research has indicated that dispositional mindfulness is related to both the five factor model (FFM) and the reinforcement sensitivity theory (RST). However, previous research has examined those associations in isolation. We examined the unique effects of RST and the FFM on mindfulness in a sample 399 participants. Overall, we found the individual facets of mindfulness were differentially correlated with RST and FFM dimensions. Specifically, RST (BIS) and FFM (Neuroticism) dimensions that draw attention to external stimuli negatively correlated with mindfulness except for Observing. In contrast, FFM dimensions Openness and Conscientiousness correlated positively with mindfulness, suggesting a pattern where individuals routinely allocate attention to internal stimuli (being mindful) in order to explore (Openness) or to regulate these experiences (Conscientiousness). Our findings provide new insights into the underlying individual difference structure of being mindfulness and implies that mindfulness may not be a unitary construct. We suggest that future research should investigate mindfulness correlates at the facet level instead of the higher level of overall mindfulness.
Cell targeting and imaging using magnetic nanoparticles

BACKGROUND AND AIMS. The success of stem cell therapies partly depends on the ability to deliver the cells to the site of injury. Circulating endothelial progenitor cells (EPCs) are involved in physiological processes such as vascular re-endothelialisation and post-ischaemic neovascularisation and have been utilised in several clinical trials. Superparamagnetic iron oxide particles have previously been used to label and track cells using magnetic resonance imaging (MRI), as well as to magnetically attract drugs and cells to desired sites. The aim of this PhD was to develop a methodology to magnetically attract EPCs, labelled with a clinically approved iron oxide agent, to a site of arterial injury using magnetic fields originating outside the body. METHODS AND RESULTS. Human EPCs were cultured in the presence of iron oxide superparamagnetic nanoparticles. A labelling method was developed that retained cell survival and differentiation, as indicated by metabolic activity and flow cytometry assays, as well as MRI visibility. Finite element modelling (FEM) computer simulations were performed to investigate the interaction of magnetic forces with hydrodynamic drag forces. FEM indicated successful external magnetic cell targeting from a vessel with flow rate similar to a rat common carotid artery; correspondingly there was a 6- fold increase in cell capture in an in vitro flow system. Angioplasty was performed on rat common carotid arteries to denude the endothelium and EPCs were administered with and without the presence of the external magnetic device during a 10 minute period of flow cessation. Targeting enhanced cell retention at the site of injury by 5-fold. CONCLUSIONS. Using an externally applied magnetic device, it is possible to enhance EPC localisation in a flowing sytem in vitro and to a flow-isolated site of common carotid artery injury in vivo, without affecting cell viability or differentiation in culture. This technology could be more widely adapted to localise and monitor cells in other organs and may provide a useful tool for systemic injection of cell therapies.