Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Shallow Subsurface of Mars with the Ma_MISS Spectrometer on the ExoMars Rover Rosalind Franklin

An essential part of the Exomars 2022 payload is the Mars Multispectral Imager for Subsurface Studies (Ma_MISS) experiment hosted by the drill system. Ma_MISS is a visible and near-infrared (0.4–2.3 μm) miniaturized spectrometer with an optical head inside the drill tip capable of observing the drill borehole with a spatial resolution of 120 μm. Here we report on how the Ma_MISS hyperspectral information provides in situ investigation of the subsurface at very fine resolution, prior to the collection of the samples that will be manipulated and crushed for further analysis by the analytical laboratory on the rover. Ma_MISS is the instrument that will closely investigate the subsurface mineralogical characteristics in its original geologic context at depths never reached before in Mars exploration. Ma_MISS recognizes all the major spectral features of the clays, basaltic, and minor phases expected at the ExoMars landing site, Oxia Planum. The high spatial resolution on the borehole wall is such that single grains of about 100 μm can be distinguishable in the assemblage of minerals observed by Ma_MISS. The spatial distribution of the mineralogies within the borehole walls is associated with the rocks and the processes that put these materials in place and possibly altered them with time, characterizing the habitats found in the stratigraphic record, indicating which ones are the most suitable to have held or to be holding nowadays traces of life.
All-HKMG-bounded SCR for advanced ESD protection in 14 nm FinFET technology

In this paper, a novel all-high-K metal gate (HKMG)-bounded silicon-controlled rectifier (AHBSCR) is proposed for advanced electrostatic discharge (ESD) protection in FinFET processes. Depart from its prior arts, in AHBSCR, both the main SCR path and the triggering diode path are placed along the Fin direction, and all active areas are bounded by HKMG, thus expressing superior conduction capability under various ESD stresses and a more compact silicon footprint. Experimental results show that the proposed AHBSCR owns a suitable snapback window of 1.1 V–1.6 V, a fast turn-on speed of only 400 ps, an extreme low leakage current of less than 100 pA@-1 V as well as a figure of merit of 24.3 mA fF−1, indicating it is competent to replace the conventional FinFET diodes in rail-based ESD network to further improve the efficiency of ESD protection.
Edinburgh Research Explorer Do regions matter in ALICE?

This study aims at investigating the impact of regional aﬃliations of centres on the organisation of collaborations within the Distributed Computing ALICE infrastructure, based on social networks meth-ods. A self-administered questionnaire was sent to all centre managers about support, email interactions and wished collaborations in the infrastructure. Several additional measures, stemming from technical observations were produced, such as bandwidth, data transfers and Internet Round Trip Time (RTT) were also included. Information for 50 centres were considered (60% response rate). Empirical analysis shows that despite the centralisation on CERN, the network is highly organised by regions. The results are discussed in the light of policy and eﬃciency issues.
Bio-Based Chemicals from Renewable Biomass for Integrated Biorefineries

The production of chemicals from biomass, a renewable feedstock, is highly desirable in replacing petrochemicals to make biorefineries more economical. The best approach to compete with fossil-based refineries is the upgradation of biomass in integrated biorefineries. The integrated biorefineries employed various biomass feedstocks and conversion technologies to produce biofuels and bio-based chemicals. Bio-based chemicals can help to replace a large fraction of industrial chemicals and materials from fossil resources. Biomass-derived chemicals, such as 5-hydroxymethylfurfural (5-HMF), levulinic acid, furfurals, sugar alcohols, lactic acid, succinic acid, and phenols, are considered platform chemicals. These platform chemicals can be further used for the production of a variety of important chemicals on an industrial scale. However, current industrial production relies on relatively old and inefficient strategies and low production yields, which have decreased their competitiveness with fossil-based alternatives. The aim of the presented review is to provide a survey of past and current strategies used to achieve a sustainable conversion of biomass to platform chemicals. This review provides an overview of the chemicals obtained, based on the major components of lignocellulosic biomass, sugars, and lignin. First, important platform chemicals derived from the catalytic conversion of biomass were outlined. Later, the targeted chemicals that can be potentially manufactured from the starting or platform materials were discussed in detail. Despite significant advances, however, low yields, complex multistep synthesis processes, difficulties in purification, high costs, and the deactivation of catalysts are still hurdles for large-scale competitive biorefineries. These challenges could be overcome by single-step catalytic conversions using highly efficient and selective catalysts and exploring purification and separation technologies.
Research on the full life cycle management system of smart electric energy meter

At present, China’s smart electric energy meter life management is started from the procurement and acceptance. The related monitoring and management of the manufacturing sector has not yet been carried out. This article applies RFID technology and network cloud platform to full life cycle management system of smart electric energy meters, builds this full life cycle management system including design and manufacturing, process control, measurement and calibration testing, storage management, user acceptance, site operation, maintenance scrap and other aspects. Exploring smart electric energy meters on-line and off-line communication by the application of active RFID communication functions, and the actual functional application such as local data exchange and instrument calibration. This system provides technical supports on power demand side management and the improvement of smart electric energy meter reliability evaluation system.
Multi-Terminal Nonhomogeneous Transmission Line Fault Location Utilizing Synchronized Data

This paper presents an innovative fault location method for multi-terminal nonhomogeneous transmission lines by using synchronized voltage and current phasors acquired from phasor measurement units or intelligent electronic devices. We extend the previously published two-terminal and three-terminal nonhomogeneous transmission line fault location techniques developed in our lab to multi-terminal nonhomogeneous transmission lines. Specifically, a graph theory based indices and an optimal calculation data window is applied to identify the faulty line branch and to mitigate the undesirable influence caused by dc decay and the arc phenomenon of circuit breaker. The high performance of the proposed fault location method is validated by simulation results and real world utility fault data.
FEATURE EXTRACTION USING REGULAR EXPRESSION IN DETECTING PROPER NOUN FOR MALAY NEWS ARTICLES BASED ON KNN ALGORITHM

The identification of proper nouns from text aims to classify named entities according to their respective groupings, an aspect included in Named Entity Recognition (NER). Proper noun disambiguation can adversely affect morphological analysis, a vital trait to improve the corpus availability via classification and new word assimilation. The occurrences of proper nouns can be annotated from the text resources using separate entity mapping from their fragments. The research was carried out to examine the impact of regex on text pattern identification sequence that queried and acquired proper nouns from a collection of unannotated Malay language news articles that envisions several techniques to improve text entities precision and accuracy such as pre-processing and data clustering. The results showed that the F-scores of the output tested on the unannotated news dataset were between 30% and 60%
3D Facial Action Units and Expression Recognition using a Crisp Logic

This work proposes a method for recognizing the main 13 Facial Action Units and the 6 basic emotions. The methodologies rely on Differential Geometry to extract relevant discriminant features from the query faces, and on some linear quantities used as measures: Euclidean, geodesic, and angles between 17 automatically extracted soft-tissue landmarks. A thresholding system which evaluates local properties of connected regions, selected through tailored geometrical descriptors, supports the identification of the AUs. Then, a technique based on crisp logic allows the identification of the global expression. The three-dimensional context has been preferred due to its invariance to different lightening/make-up/camouflage conditions.
Jacobian-based derivation of dynamics equations of elastic parallel manipulators

This article presents two approaches for the derivation of dynamics equations of elastic parallel manipulators. These are based on the standard Lagrange-D'Alembert formulation extendend by the consideration of (even redundant) elasticities. They start up from different points of view on the dynamics of the parallel manipulators. The first one reduces the parallel manipulator to a tree-structure, the second one deals with the manipulator as a compact structure. These different ways lead to the same equation of motion. In addition, a new method for the derivation of the Jacobian matrix of the elastic parallel manipulator will be presented. For verification an analytical model of the planar parallel elastic manipulator Five-Bar is derived with the presented approaches and compared with a numerical model in form of a DYMOLA-model.
Choreobot: A Reference Framework and Online Visual Dashboard for Supporting the Design of Intelligible Robotic Systems

As robots are equipped with software that makes them increasingly autonomous, it becomes harder for humans to understand and control these robots. Human users should be able to understand and, to a certain amount, predict what the robot will do. The software that drives a robotic system is often very complex, hard to understand for human users, and there is only limited support for ensuring robotic systems are also intelligible. Adding intelligibility to the behavior of a robotic system improves the predictability, trust, safety, usability, and acceptance of such autonomous robotic systems. Applying intelligibility to the interface design can be challenging for developers and designers of robotic systems, as they are expert users in robot programming but not necessarily experts on interaction design. We propose Choreobot, an interactive, online, and visual dashboard to use with our reference framework to help identify where and when adding intelligibility to the interface design is required, desired, or optional. The reference framework and accompanying input cards allow developers and designers of robotic systems to specify a usage scenario as a set of actions and, for each action, capture the context data that is indispensable for revealing when feedforward is required. The Choreobot interactive dashboard generates a visualization that presents this data on a timeline for the sequence of actions that make up the usage scenario. A set of heuristics and rules are included that highlight where and when feedforward is desired. Based on these insights, the developers and designers can adjust the interactions to improve the interaction for the human users working with the robotic system.
S EQUENCE C HARACTERIZATION OF G LUTAMATE R ECEPTOR G ENES OF R AT A VERTEBRATE AND A RABIDOPSIS THALIANA A PLANT

— - iGluR gene family of a vertebrate, Rat and AtGLR gene family of a plant, Arabidopsis thaliana [4] perform some common functionalities in neuro-transmission, which have been compared quantitatively. Our attempt is based on the chemical properties of amino acids [6,7,8] comprising the primary protein sequences of the aforesaid genes. 19 AtGLR genes of length varying from 808 amino acid (aa) to 1039 aa and 16 iGluR genes length varying from 902aa to 1482 aa have been taken as data sets. Thus, we detected the commonalities (conserved elements) during the long evolution of plants and animals from a common ancestor [4]. Eight different conserved regions have been found based on individual amino acids. Two different conserved regions are also found, which are based on chemical groups of amino acids. We have tried too to find different possible patterns which are common throughout the data set taken. 9 such patterns have been found with size varying from 2 to 5 amino acids at different regions in each primary protein sequences. Phylogenetic trees of AtGLR and iGluR families have also been constructed. This approach is likely to shed light on the long course of evolution.
in patients with TB

TITLE: A novel high sensitivity bacteriophage-based assay identifies low level M. active and ABSTRACT: Haematogenous dissemination of M. tuberculosis (Mtb) is critical to pathogenesis of progressive tuberculous infection in animal models. Using a novel phage-based blood assay, we report the first concordant evidence in well-characterised immunocompetent human cohorts, demonstrating associations of Mtb bacteraemia with progressive phenotypes of latent infection and active pulmonary TB respectively. Although preliminary, this study demonstrates early promise with Actiphage TM as a blood-based diagnostic tool for infectious PTB to improve earlier diagnosis in patients unable to expectorate sputum, and requires investigation in other cohorts with an unmet need including extra-pulmonary, paediatric and miliary TB. Developments in the assay to simplify sample preparation and molecular detection are required to enhance accessibility for low-resource settings; but the advantages of a rapid, low-cost assay that offers a microbiological diagnosis in the absence of sputum, are clear.
End-to-End Neural Optical Music Recognition of Monophonic Scores

Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Music Scores dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.
The Water Buffalo: Design of a Portable Bicycle Powered Irrigation Pump for Small-Scale African Farmers

This thesis reports on the development a low cost and mass-producible bicycle powered irrigation pump for small-scale (100–1,000 m2 plot) African farmers. The pump is intended to be made and distributed by World Bicycle Relief’s Buffalo Bikes program. This project’s process is divided into three parts: an investigation into the user needs, quantitative testing of a prototype pump’s power and efficiency, and the redesign of the pump for manufacturing. In order to clarify the user needs, I conducted in-depth interviews and informal demonstrations with small-scale farmers in Kenya. I learned of additional user requirements; for example, that the pump must be usable by children. I also learned of critical problems with the early design; for example, that air leaks on the suction side of the pump will hobble its performance. Though imperfect, the limited success of the trials verified the viability of a bicycle irrigation pump in the target market. To quantify the effectiveness of a centrifugal bicycle powered pump, I measured the pump’s water flow at various input power levels and outlet heights (vertical heads). The efficiency of the pump system (η ≈ 55%) approached the claimed manufacturer’s maximum efficiency of η ≈ 66%. I then compared the bicycle pump to two other human powered pumps, both of which use a treadle pumping style. The bicycle system pumps water at similar or possibly higher flows than the tested treadle pumps. Friction calculations additionally revealed that at fast flow rates of 1 liter per second (16 gallons per minute), hoses with a 1 inch diameter (25.4 mm) have significant friction losses, while 1.5 inch (38.1 mm) hoses have negligible friction losses. Based on the interviews and efficiency tests, I redesigned the pump and gearbox to be made of plastic and to address the main issues identified in the field. The pump-to-hose attachments are designed to reduce air leaks, reduce tipping, and simplify the pump’s setup. The form of the gearbox is designed without overhangs so that it can be manufactured by casting. By creating an inexpensive irrigation method, I hope to empower small-scale African farmers to lift themselves into prosperity.
A novel mobile platform for stress prediction: application, protocol and preliminary results

Public health surveillance is typically done through self-report surveys. Personal smart devices that collect near real-time and zero-effort health data can support traditional surveillance efforts by providing novel and diverse data, which can be used to predict the prevalence of conditions in a population using advanced analytics. Apple Health is one of the most popular sources of personal health data, supporting a variety of devices that collect a wide range of information from heart rate to blood pressure and sleep. This paper introduces a mobile health platform that extracts Apple Health data to support public health monitoring based on personal devices, as well as a protocol for a study that utilizes this platform to predict stress in a population. Preliminary results are also presented: Random Forests and Support Vector Machines are used to predict the participant's stress levels and achieved an accuracy of 85% and 70%, respectively. Implications for public health, challenges, limitations, and future work are also discussed. The system described in this paper is one of the first works to leverage health data from consumer-level personal devices for public health.
A multi-sensor deep learning approach for complex daily living activity recognition

With the ever increasing elderly population, human activity trackers can help monitor the daily physical activities performed by the elderly in order to contribute towards improvements in independent living and quality of life. Little activity recognition research has explored the use of multiple Inertial Measurement Units (IMU) to capture both simple and complex human activities. Therefore, existing research may not benefit the monitoring of the elderly population wherein the complexity in the details of changes are not captured. This work proposes a multi-sensor approach measuring acceleration and quaternion values to recognise both simple and complex daily living activities using a deep learning approach. We compare and evaluate the performance of using 1, 3 and 5 on-body IMU sensors to train CNN and LSTM networks with both acceleration and quaternion values. The results show that the adoption of the quaternion values from 5 on-body sensors using the LSTM model outperforms all other models (F1-score=0.9606). This high performance provides many opportunities for the accurate monitoring of complex daily living activities.
Research Online Research Online A Matched Control Trial of a Mental Health Literacy Intervention for Parents A Matched Control Trial of a Mental Health Literacy Intervention for Parents in Community Sports Clubs in Community Sports Clubs

Abstract 2020, Springer Science+Business Media, LLC, part of Springer Nature. This controlled trial evaluated the effectiveness of a mental health literacy intervention for parents delivered through community sport clubs. In total, 540 parents (321 females, 219 males) of adolescent athletes participated in a brief educational workshop on youth mental health (n = 352) or a community-matched control group (n = 188). Generalised linear mixed models revealed no significant improvements in the intervention group compared to control in the primary mental health literacy outcomes, at 1 month follow-up. However, parents in the intervention group were more likely to seek formal help for themselves, had increased confidence and knowledge to help someone experiencing a mental health disorder, experienced reduced psychological distress, and perceived more support from other parents in their sport club, relative to the control group. Overall, the findings suggest that a brief educational intervention delivered through community sports clubs can positively affect some components of parents' mental health literacy. Abstract This controlled trial evaluated the effectiveness of a mental health literacy intervention for parents delivered through community sport clubs. In total, 540 parents (321 females, 219 males) of adolescent athletes participated in a brief educational workshop on youth mental health ( n = 352) or a community-matched control group ( n = 188). Generalised linear mixed models revealed no significant improvements in the intervention group compared to control in the primary mental health literacy outcomes, at one month follow-up. However, parents in the intervention group were more likely to seek formal help for themselves, had increased confidence and knowledge to help someone experiencing a mental health disorder, experienced reduced psychological distress, and perceived more support from other parents in their sport club, relative to the control group. Overall, the findings suggest that a brief educational intervention delivered through community sports clubs can positively affect some components of parents’ mental health literacy.
C ONSUMERS ’ B EHAVIOURAL I NTENTION TO A CCEPT OF THE M OBILE W ALLET IN M ALAYSIA

A mobile wallet is a part of financial technology products and it is still in the infant stage in Malaysia. This study applied 2 theories such as Theory of Reasoned Action and Theory of Acceptance Model. The research objectives of this paper are 1) to investigate the factors influencing consumers’ behavioural intention to accept of the mobile wallet in Malaysia, 2) to analyze the relationships between perceived usefulness, perceived ease of use, perceived security, social influence, price value, social media, brand image, and behavioural to accept the mobile wallet, and 3) to examine the relationship between the behavioural and intention to accept of the mobile wallet in Malaysia. Quantitative data analysis was employed in this study. The results implied that perceived usefulness, perceived ease of use, social influence, and brand image have a significant relationship on the consumers’ behavioural to accept of the mobile wallet, and also the consumers’ behavioural has a significant relationship on the intention to accept mobile wallet. Considerably, the mobile wallet companies can formulate appropriate marketing decisions in designing a more personalized mobile wallet to Malaysian consumers and protect the benefits of consumers’ secured digital payment to motivate mobile wallet acceptance.