Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of body fluids by mRNA analysis with minion nanopore sequencing

The identification of body fluids present on evidence items in a criminal investigation can be vital to understanding the nature of a crime, particularly in cases of sexual assault. Although crime labs can confirm the presence of body fluids like semen and blood on a piece of evidence using traditional serological techniques, they cannot confirm the presence of saliva or vaginal fluid or differentiate peripheral blood from menstrual blood. Due to the unique patterns of gene expression in different cell types, different body fluids contain distinct messenger RNA (mRNA) molecules, which can be analyzed to generate mRNA profiles for confirmatory identification of body fluids. High throughput sequencing methods present the opportunity to generate large amounts of data from low level forensic samples that is not achievable with more traditional techniques. In this study, the MinION sequencer by Oxford Nanopore Technologies (ONT), which is a small and affordable high throughput sequencer that generates data rapidly and in real time, was assessed to determine its ability to generate high quality data from forensic type samples. Semen, saliva, blood, vaginal fluid, and menstrual blood from eight different donors were collected on sterile swabs. Additionally, a tenfold dilution series was performed on semen, blood, and saliva samples from three donors each and decreasing volumes of semen were pipetted onto vaginal swabs from three sets of donors. DNA and RNA were co-extracted from half swabs. DNA fractions were taken forward for short tandem repeat (STR) analysis. For RNA fractions, cDNA was generated, and a multiplex PCR targeting two genes per body fluid was performed. Amplicons were sequenced on the MinION with the 1D Ligation Sequencing Kit by ONT. Full DNA profiles were obtained for all semen, blood, and menstrual blood samples, and most of the saliva and vaginal fluid samples. Profiles were obtained for diluted semen, saliva, and blood samples, and mixed profiles were obtained for the semen/vaginal fluid mixture samples. The multiplex PCR was highly specific for each body fluid, with little to no cross reactivity. The MinION was able to obtain at least 1,000X coverage of target genes and little to no off target reads, even for some diluted samples. Both vaginal fluid and semen genes were detected in mixture samples. Optimization of the MinION sequencing workflow to maximize read counts and minimize costs should be explored. Despite the high input requirements stated by ONT for sequencing, the MinION appears to be able to generate high quality data with lower DNA/RNA input.
High Resolution Air Quality Monitoring with IoT Intelligent Multisensor devices during COVID-19 Pandemic Phase 2 in Italy

COVID19 Pandemic impacts have been associated with Air Quality (AQ) with its mortality index being significantly affected by high pollution levels. Significant mobility limitations have contributed to slow down the pandemic in Italy having as a side effect a definite decrease of pollution levels. Phase 2 while easing those limits still see a significant reduction of commuting and schools related car traffic emissions. High resolution AQ monitoring can now allow to obtain a picture of AQ if smart cities will be capable to reach long sought emissions goals. Furthermore, it could support the identification and forecasting of possible future local SARS-COV-2 outbursts. In this work, we present the outcomes of a high resolution AQ monitoring opportunistic campaign. These have been achieved through the deployment of field data driven calibration trough machine learning and a smartphone centered IoT infrastructure, capable to store, visualize and give exposome feedback to monitoring volunteers.
Voltage • Frequency Control Switching Method Using V • f Plane of PV Inverter

In this paper, authors describe the control priority of the system support function installed in the photovoltaic power generator (PV) inverter. The PV inverter is equipped with a system support function as a measure against power quality problems. However, it is not clear which control has priority among these control functions. In particular, it is considered necessary to study in low voltage distribution systems where voltage and frequency problems can occur simultaneously. Therefore, in this paper, authors propose a method for switching the voltage control and frequency control installed in the PV inverter by using the V・f plane centered on the amount of change in voltage and system frequency in a low-voltage distribution system. As a result, it was confirmed that it is effective to control the voltage preferentially in the low voltage distribution system.
Block-chain Based Energy Trading in ADN with its probable impact on Aggregated Load Profile and Available Distribution Capability

Technological advancements are leading to new frontiers in power grid operation, monitoring, control, and commercialization. The conventional notion of unidirectional power flow from centralized generating stations to probable consumers through unidirectional distribution systems is now changing with the active participation of consumers as prosumers and distributed energy resources. In order to maintain security, privacy and avoid double-spending while performing energy trading through online mechanisms a strong and reliable mode of energy transaction platform is required. Blockchain technology has proven as a stable and reliable platform for maintaining the distributed ledger that could be utilized for hosting and managing the energy transactions of the grid. In this paper, a blockchain-based energy trading mechanism in presence of internet of things has been proposed and the impact of employing blockchain technology on the aggregated load profile and available distribution capability of the ADN has been performed in Modified IEEE 123 bus distribution feeder.
Modeling the COVID-19 Pandemic Using an SEIHR Model With Human Migration

The 2019 novel coronavirus disease (COVID-19) outbreak has become a worldwide problem. Due to globalization and the proliferation of international travel, many countries are now facing local epidemics. The existence of asymptomatic and pre-symptomatic transmissions makes it more difficult to control disease transmission by isolating infectious individuals. To accurately describe and represent the spread of COVID-19, we suggest a susceptible-exposed-infected-hospitalized-removed (SEIHR) model with human migrations, where the “exposed” (asymptomatic) individuals are contagious. From this model, we derive the basic reproduction number of the disease and its relationship with the model parameters. We find that, for highly contagious diseases like COVID-19, when the adjacent region’s epidemic is not severe, a large migration rate can reduce the speed of local epidemic spreading at the price of infecting the neighboring regions. In addition, since “infected” (symptomatic) patients are isolated almost immediately, the transmission rate of the epidemic is more sensitive to that of the “exposed” (asymptomatic) individuals. Furthermore, we investigate the impact of various interventions, e.g. isolation and border control, on the speed of disease propagation and the resultant demand on medical facilities, and find that a strict intervention measure can be more effective than closing the borders. Finally, we use some real historical data of COVID-19 caseloads from different regions, including Hong Kong, to validate the modified SEIHR model, and make an accurate prediction for the third wave of the outbreak in Hong Kong.
A Novel and Scalable Home Appliances Electrical Signature Database for Smart Home Energy Management

This paper proposes a comprehensive framework named HAESD designed under the software environment LabVIEW to build up a database for NonIntrusive Load Monitoring (NILM) purposes. It provides the central component of a scalable architecture for the storage of current and voltage measurements related to real-world Home Electrical Appliances (HEAs) and the extraction of their electrical features. A connection is established between the two softwares LabVIEW and MySQL, where the latter is used as a Relational Database Management System (RDBMS) to generate and to manage a database of HEAs’ current and voltage measurements. This connection is performed thanks to the ODBC standards. The goal behind HAESD development is to store/load electrical signatures data in/from the designed database. These electrical signatures are computed under the LabVIEW environment starting from HEAs’ supply current and voltage measurements. The supply current and voltage of each HEA is acquired thanks to National Instrument (NI) current and voltage acquisition and data transfer modules. An experimental setup has allowed testing and validating the HAESD framework implementation. Experimental results are given considering a microwave as an example of real-world HEA. This framework will help to establish scalable and large database of real-world HEAs with the view to identify them and manage their electricity consumption.
LASSO Based Similarity Learning of Near-Infrared Spectra for Quality Control

This paper presents a novel method for quality control with similarity learning of near-infrared spectra (NIRS). The product quality is rated by measuring the similarity between the tested products and the standard. To automatic this process, we use mahalanobis distance(MahalD) as the metric to estimate the difference of the segmented NIR spectra that are measured from tobacco product samples and standard references. The features used to score the quality rate are then generated by least absolute shrinkage and selection operator(LASSO), which selects the most related wavelengths from MahalD map. The dimension-reduced MahalD map is then loaded into a more advanced regression learning, like SparseNet, Support Vector Machine(SVM) or Random Forrest(RF) to build a model for quality rate prediction. Results from these regression methods are tested and compared, which show this method is effective and LASSO feature selection can improve the prediction accuracy.
Influence of the Mission Profile on The Lifetime Modelling of the Wind Turbine Power Converter – A Review

Offshore wind energy is currently the leading offshore renewable energy technology and plays an essential role in achieving a low carbon economy in Europe. Offshore wind turbines, as a result of the harsher offshore environment, suffer from high O&M costs, and as such a high overall LCOE. These higher O&M costs can be reduced by focusing on the most critical subassemblies of the OWT, by either improving the reliability of the subassembly or by better understanding the failure mechanism, and thus optimizing the O&M strategy. The power converter is identified as one of the most critical subassemblies as risk of the operation of the OWT in terms of maintainability and availability. The reliability of the power converter is heavily influenced by both its steady-state and its dynamic thermal behavior, and as such great attention will be paid to the literature related to the thermal behaviour of the power converter. This paper seeks to provide a comprehensive overview on how the typical mission profile of an OWT influences the thermal loading. The paper seeks to propose a comprehensive and up-to-date literature review related to the lifetime modelling of the power converter, and to highlight some of the main challenges yet to be tackled in the reliability analysis of the power converter, suggesting future areas of research.
Air Quality Visual Analytics with Kibana

The recent studies report that the short-term fluctuations of air pollution levels are directly related to the hospital admissions of patients with pneumonia and bronchitis. In addition, the long-term exposure to air pollution causes significant health problems, including cardiovascular disease, lung cancer and respiratory disease such as emphysema. At the same time, a huge amount of air quality data is collected by public air quality monitoring systems in different parts of the world. Official reporting from government is one of reliable sources of data. The European Environment Agency for Europe’s Air Quality and Clean Air Asia databases, the Global Burden of Disease epidemiological study, and peer-reviewed journal articles are other sources. Multidimensional visualization of such big amounts of data, including temporal granularities and spatial distribution, is a challenging question. In order to address this challenge, the paper proposes a software solution for visual analytics of air quality data using the potential of Big Data technologies. Its architecture, implementation with Elasticsearch and Kibana, and actual results from data visualization are presented. The findings of the paper show that the proposed solution provides more intuitive perception and valuable insight through multi-perspective air pollution graphs.
Articulation correctness measurement of Parkinson's disease using low resource-intensitve segmentation methods

The paper is about speech fluency measurements and classification of patients with Parkinson's disease. Speech is a process that is controlled by a complex system in humans. Parkinson's disease also affects speech production. In this work, we examine the speech fluency of PD patients using two language-independent segmentation method: forward-backward divergence segmentation (FBDS) and transient-stationary segmentation (TSS). Significance tests show that most features are different among the two groups. Support vector machines were applied performing automatic classification tests. The highest achieved accuracy was 0.76 with 0.72 F1-score. This implies that the features calculated may help in differentiating PD from healthy speech and aid a decision support tool without the need of a complex, language-dependent ASR system.
Key Rate Enhancement by Using the Interval Approach in Symmetric Key Extraction Mechanism

Wireless security is confronted with the complexity of the secret key distribution process, which is difficult to implement on an Ad Hoc network without a key management infrastructure. The symmetric key extraction mechanism from a response channel in a wireless environment is a very promising alternative solution with the simplicity of the key distribution process. Various mechanisms have been proposed for extracting the symmetric key, but many mechanisms produce low rates of the symmetric key due to the high bit differences that occur. This led to the fact that the reconciliation phase was unable to make corrections, as a result of which many key bits were lost, and the time required to obtain a symmetric key was increased. In this paper, we propose the use of an interval approach that divides the response channel into segments at specific intervals to reduce the key bit difference and increase the key rates. The results of tests conducted in the wireless environment show that the use of these mechanisms can increase the rate of the keys up to 35% compared to existing mechanisms
Improved Non-Contact Mental Stress Detection via Bioradar

Chronic mental stress may lead to different physiological and psychological disorders, such as hypertension, depression, diabetes, obesity, cardiovascular diseases. At present there are no reliable accurate and non-subjective methods applicable for routine everyday stress assessment. In present work we proposed an improved unobtrusive machine-learning based method for remote mental stress detection by a bioradar. It was validates using experimental data of 34 practically healthy volunteers of different fitness state and stress tolerance. A CatBoost Classifier used for classify between relaxed and stress states on the volunteers allows detecting a mental stress with an accuracy of 89 %, and Fl-score of 88 %. The achieved results are comparable with ones for bioradar-based state-of-the-art techniques in terms of accuracy. Thus, the proposed method can be positioned as an easy to use unobtrusive prolonged stress detection technique suitable for people with different level of fitness and stress tolerance.
A Combined First Principles and Kinetic Monte Carlo study of Polyoxometalate based Molecular Memory Devices

In this paper, we combine Density Functional Theory with Kinetic Monte Carlo methodology to study the fundamental transport properties of a type of polyoxometalate (POM) and its behaviour in a potential flash memory device. DFT simulations on POM molecular junctions helps us demonstrate the link between underlying electronic structure of the molecule and its transport properties. Furthermore, we show how various electrode-molecule contact configurations determine the electron transport through the POM. Also, our work reveals that the orientation of the molecule to the electrodes plays a key role in the transport properties of the junction. With Kinetic Monte Carlo we extend this investigation by simulating the retention time of a POM-based flash memory device. Our results show that a POM based flash memory could potentially show multi-bit storage and retain charge for up to 10 years.
Environment-friendliness in Maritime Transport: Designing Smart Recharging Stations in North Adriatic Sea

Today, the electrification plays a crucial role in the challenge towards the energy sustainability. Focusing on the transportation field, the integration of carbon-free electric vehicles (cars, ships, trains and so on) and smart charging infrastructures based on renewable resources can strongly reduce the pollutants emissions of human mobility, while at the same time promoting the Smart City paradigm. In this context, the INTERREG project METRO (Maritime Environment-friendly TRanspOrt systems) is aimed at proposing innovative solutions for the improvement of the environmental sustainability in the North Adriatic maritime transport. The goal is to be reached by means of an integrated strategy, focusing on tackling both ships and ports. On one hand, by using electrical propulsion and energy storage systems onboard the ship, it is possible to decrease the vessel’s environmental impact while sailing. On the other hand, a well-designed recharging infrastructure in the port can recharge the ship’s energy storage system while at berth, possibly exploiting renewable carbon-free resources.
Multiple Features Fusion Attention Mechanism Enhanced Deep Knowledge Tracing for Student Performance Prediction

Student performance prediction is a fundamental task in online learning systems, which aims to provide students with access to active learning. Generally, student performance prediction is achieved by tracing the evolution of each student’s knowledge states via a series of learning activities. Every learning activity record has two types of feature data: student behavior and exercise features. However, most methods use features that are related to exercises, such as correctness and concepts, while other student behavior features are usually ignored. The few studies that have focused on student behavior features through subjective manual selection argue that different student behavior features can be used in an equivalent manner to predict student performance. In this paper, we assume that the integration of student behavior features and exercise features is crucial to improve the precision of prediction, and each feature has a different impact on student performance. Therefore, this paper proposes a novel framework for student performance prediction by making full use of both student behavior features and exercise features and combining the attention mechanism with the knowledge tracing model. Specifically, we first exploit machine learning to capture feature representation automatically. Then, a fusion attention mechanism based on recurrent neural network architecture is used for student performance prediction. Extensive experiments on a real-world dataset show the effectiveness and practicability of our approach. The accuracy of our method is up to 98%, which is superior to previous methods.
HUD-Oden: A Practical Evaluation Environment for Analyzing Hot-Water Cooled Processors

Liquid cooling has been rapidly becoming the de facto standard cooling method for high performance/density racks of modern HPC Data Centers. Semiconductor technology development has made it possible to operate processors (CPU, GPU, and Accelerators) at more higher temperature ranges without compromising the reliability and static power consumption, and these contributed in part to increase the attention over the “hot-water cooling” as one of the main approaches for energy efficient system design. The 2011 ASHRAE Class W4 allows water supply temperature up to 45°C, and even higher temperature for the Class W5. A clear understanding of the temperature impact on the processors would be valuable for assisting the HPC operational staffs for their strategic planning and decision makings. In this short paper, we present our experience in using a simple and cost effective bench testing environment for analyzing the operational behavior of the processors in such high temperature conditions. Although it is far from ideal, since we are not using the same building blocks of the current running HPC system, we consider a valuable alternative for observing the operational behavior of the processors in such temperature environment, and may obtain supportive evidence for assisting strategic planning and decision makings. Index Terms-Energy efficiency, liquid cooling, hot-water cooling, power consumption, frequency throttling
Propagation of Voltage Sag Considering Different Winding Connections: Impact on the Healthiness of Transformers

This article investigates the effect of voltage sag propagation through the transformer due to various symmetrical and asymmetrical faults in a chronological manner. A unique and precise explanation has been presented for the propagation of voltage sag through different transformer connections. The effect of voltage sag has been studied in determining the healthiness of the transformer by analyzing the parameters of insulating oil obtained through dissolved gas analysis. For this purpose, different transformer winding connections have been considered, and the analysis has been performed considering the occurrence of all major faults. In order to justify and strengthen the proposal, a real-time case study has been deliberated for depicting the correlation of propagation of fault and its effect on transformer remaining in-service period through dissolved gas analysis. The study presented in this article will help utilities to understand the voltage sag propagation along with realizing its effect on transformer healthiness, thereby ensuring total system reliability and better economical service.