Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection Algorithm for Chest Compressions Signal with Classification Algorithm

Transthoracic impedance (TTI) has been demonstrated to be a potential indicator to monitor the quality of chest compressions (CCs) during cardiopulmonary resuscitation (CPR). However, TTI signals are challenged by noise artifact from multiple sources, such as ventilations and baseline drift. Practically, it is very essential to accurately detect the peak-to-trough of the complicated TTI signals. However, nowadays, there is no method to solve the problem. In this paper, Extrima search with niche technology was used to search the peak-to-trough of TTI signal. We select 2 features to judge the potential peaks and troughs in order to remove the false ones. Besides, we designed a LDA classifier for recognizing the compression and ventilation waves. The experimental results show that this method in this paper can precisely recognize the real peaks and troughs of TTI signals which include some false ones.
DEEP-LEARNED CROSS-DOMAIN SENTIMENT CLASSIFICATION USING INTEGRATED POLARITY SCORE PATTERN EMBEDDING ON TRI MODEL ATTENTION NETWORK

Classification of sentiments is a challenging task when it is done across multiple domains. The dissimilarity between the domains, reflected in their sentiments makes the classification tougher. This work proposes a deep learned novel architecture named Integrated Polarity Score based Pattern Embedding on Tri Model Attention (IPSPE_TMA) Network which is a model based on Bidirectional Long-short Term Memory (BiLSTM), Bi-Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) with three stage novel embedding architecture and attention network. The information is extracted by incorporating attention in word and sentence level. In order to generate robust vectors, a novel polarity score based embedding is proposed and used along with Glove and Fast Text in this paper. Integration of three models with three kinds of embeddings and attention mechanism strengthens this model. This network implementation for sentiment analysis on cross domain dataset gives better performance than many of the previous works.
Noise Pollution and Human Health: A Case Study of Municipal Corporation of Delhi

Noise pollution, an urban territorial phenomenon, is assuming serious proportions in Delhi, the third largest urban centre of India. The frequency and intensity of pollution has been increasing day by day. The present study reviews the data on sources, intensity and health implications of pollutants collected through household survey using questionnaires from two contrasting density areas; one from high-density area and another from low-density area. The analysis reveals that tremendous increase in population, industrial activities, unchecked growth in vehicular traffic and rapidly changing life style are the major factors that have created and aggravated the problem of pollution in the study area. The major health implications include annoyance, disturbance in sleep, interference with communication and other harmful effects. With the help of regression analysis, the cause and effect relationship between the intensity of pollution and occurrence of diseases among sampled households of Delhi has been established by this study. Some suggestions such as land use and transportation planning are also given in this paper for mitigating and managing the noise pollution problem in the sustainable urban development perspectives.
LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA-disease associations, but few models are available to further prioritize causal miRNA-disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA-disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA-disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA-disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA-disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA-disease associations from general miRNA-disease associations.
Serviços de governo eletrônico no Brasil: uma análise sobre fatores de impacto na decisão de uso do cidadão

Resumo Este artigo trata dos fatores que influenciam a utilização de serviços de governo eletrônico pelos cidadãos. A literatura acadêmica tem promovido reflexões sobre a influência de diversos fatores, e este artigo tem como objetivo avaliar se aspectos sociodemográficos têm influência sobre a decisão de uso de serviços de governo eletrônico pelos cidadãos brasileiros. O estudo utilizou uma abordagem metodológica quantitativa, valendo-se de testes estatísticos e modelo de regressão logística baseados em microdados da pesquisa TIC Domicílios, coordenada pelo Centro Regional de Estudos para o Desenvolvimento da Sociedade da Informação (Cetic.br), referente ao ano-base de 2019. Os resultados desta análise indicam que há influência significativa de fatores como idade, renda familiar, condição de atividade, classe econômica, grau de instrução, tipo de dispositivo de acesso e uso de serviços de e-commerce sobre a probabilidade de uso de serviços de governo eletrônico. O artigo contribui para o entendimento mais abrangente dos fatores que impactam a demanda e se propõe a servir de insumo para discussões entre gestores públicos sobre a implementação desses serviços.
Uniplanar tunable bandpass filter using centrally‐loaded slot‐line resonator

A novel uniplanar tunable bandpass filter (BPF) is designed by using the centrally loaded slot‐line resonator. From the voltage‐wave distribution along the resonator, the proper location for the loading element is determined to be the center of the slot‐line resonator, where the voltages of the fundamental signal and second harmonic are maximum and zero, respectively. As a result, the fundamental frequency can be tuned, whereas the second harmonic remains almost unchanged. For the first time, the properties of the centrally loaded slot‐line resonator are analyzed by using the even‐ and odd‐mode method, and their respective resonant frequencies are derived. The demonstrated tunable BPF can give >30% frequency tuning range with acceptable insertion loss when a varactor is used as the loading element. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52:2805–2807, 2010; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25617
Peripheral Infection after Traumatic Brain Injury Augments Excitability in the Perilesional Cortex and Dentate Gyrus

Peripheral infections occur in up to 28% of patients with traumatic brain injury (TBI), which is a major etiology for structural epilepsies. We hypothesized that infection occurring after TBI acts as a “second hit” and facilitates post-traumatic epileptogenesis. Adult male Sprague–Dawley rats were subjected to lateral fluid-percussion injury or sham-operation. At 8 weeks post-injury, rats were treated with lipopolysaccharide (LPS, 5 mg/kg) to mimic Gram-negative peripheral infection. T2-weighted magnetic resonance imaging was used to detect the cortical lesion type (small focal inflammatory [TBIFI] vs. large cavity-forming [TBICF]). Spontaneous seizures were detected with video-electroencephalography, and seizure susceptibility was determined by the pentylenetetrazole (PTZ) test. Post-PTZ neuronal activation was assessed using c-Fos immunohistochemistry. LPS treatment increased the percentage of rats with PTZ-induced seizures among animals with TBIFI lesions (p < 0.05). It also increased the cumulative duration of PTZ-induced seizures (p < 0.01), particularly in the TBIFI group (p < 0.05). The number of c-Fos immunopositive cells was higher in the perilesional cortex of injured animals compared with sham-operated animals (p < 0.05), particularly in the TBI-LPS group (p < 0.05). LPS treatment increased the percentage of injured rats with bilateral c-Fos staining in the dentate gyrus (p < 0.05), particularly in the TBIFI group (p < 0.05). Our findings demonstrate that peripheral infection after TBI increases PTZ-induced seizure susceptibility and neuronal activation in the perilesional cortex and bilaterally in the dentate gyrus, particularly in animals with prolonged perilesional T2 enhancement. Our data suggest that treatment of infections and reduction of post-injury neuro-inflammation are important components of the treatment regimen aiming at preventing epileptogenesis after TBI.
isoformant: A visual toolkit for reference-free long-read isoform analysis at single-read resolution

isoformant is an analytical toolkit for isoform characterization of Oxford Nanopore Technologies (ONT) long-transcript sequencing data (i.e. direct RNA and cDNA). Deployment of these tools using Jupyter Notebook enables interactive analysis of user-defined region-of-interest (ROI), typically a gene. The core module of isoformant clusters sequencing reads by k-mer density to generate isoform consensus sequences without the requirement for a reference genome or prior annotations. The inclusion of differential isoform usage hypothesis testing based on read distribution among clusters enables comparison across multiple samples. Here, as proof-of-principle, we demonstrate the utility of isoformant for analyzing isoform diversity of commercially-available isoform standard mixtures. isoformant is available here: https://github.com/danledinh/isoformant.
Stacked rectangular ring antenna for GPS mobile receiver

A microstrip patch antenna array for use with the GPS receiver has been developed. The paper presents microstrip stacked rectangular-ring patches with low cross polarisation over an extremely wide beamwidth (close to hemisphere). The desegmentation technique (multiport network modelling coupled with cavity model), providing a detailed equivalent network representation of microstrip patches, mutual coupling among them, and the probe feed of an array has been used. It is a convenient framework for CAD of microstrip patch antennas composed of regular (rectangular, circular, elliptical, etc.) shape parts. The antenna operates at the two frequencies of the GPS signal with an axial ratio less than 3 dB over and angle 140 degrees and VSWR less than 1:1.5 over 20 MHz bandwidth centred on each band.
A Computational Analysis for Active Flow and Pressure Control Using Moving Roller Peristalsis

Peristaltic motion arises in many physiological, medical, pharmaceutical and industrial processes. Control of the fluid volume rate and pressure is crucial for pumping applications, such as the infusion of intravenous liquid drugs, blood transportation, etc. In this study, a simulation of peristaltic flow is presented in which occlusion is imposed by pairs of circular rollers that squeeze a deformable channel connected to a reservoir with constant fluid pressure. Naturally, this kind of flow is laminar; hence, the computation occurred in this context. The effect of the number and speed of the pairs of rollers, as well as that of the intrapair roller gap, is investigated. Non-Newtonian fluids are considered, and the effect of the shear-thinning behavior degree is examined. The volumetric flow rate is found to increase with an increase in the number of rollers or in the relative occlusion. A reduction in the Bird–Carreau power index resulted in a small reduction in transport efficiency. The characteristic of the pumping was computed, i.e., the induced pressure as a function of the fluid volume rate. A strong positive correlation exists between relative occlusion and induced pressure. Shear-thinning behavior significantly decreases the developed pressure compared to Newtonian fluids. The immersed boundary method on curvilinear coordinates is adapted and validated for non-Newtonian fluids.
A System for Video Surveillance and Monitoring CMU VSAM Final Report

Abstract : Under the three-year Video Surveillance and Monitoring (VSAM) project, the Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation have developed a system for autonomous Video Surveillance and Monitoring. The technical approach uses multiple, cooperative video sensors to provide continuous coverage of people and vehicles in a cluttered environment. This final report presents an overview of the system, and of the technical accomplishments that have been achieved. Details can be found in a set of previously published papers that together comprise Appendix A.
A Study of URL Redirection Indicating Spam

The use of URL redirections has been recently studied to filter spam as email and web spammers use redirection to camouflage their web pages. However, many web sites also employ redirection for legitimate reasons such as logging, localization, and load-balancing. While a majority of the studies on URL redirection focused on spam redirection, we provide a holistic view of the use of URL redirections in the Internet. We performed a redirection study on various sets of known legitimate URLs and spam URLs. We observed that URL redirections are widely used with more than 40% of legitimate URLs redirecting for various reasons. We also observed that server side redirection is prominent in both legitimate and spam redirection. Differing from legitimate URL redirection, JavaScript redirection is detected more often in spam URLs. Furthermore, a very high percentage of spam redirections lead to an external domain. We expect that our measurement results and classifications provide a better understanding of the usage of URL redirection, which could help improve spam filtering and other applications that rely on URLs.
New Directions in Neural Networks: 18th Italian Workshop on Neural Networks WIRN 2008 - Volume 193 Frontiers in Artificial Intelligence and Applications

The book is a collection of selected papers from the 18th WIRN workshop, the annual meeting of the Italian Neural Networks Society (SIREN). As the number 18 marks the year young people come of age in Italy, the society invited two generations of researchers to participate in a discussion on neural networks: those new to the field and those with extensive familiarity with the neural paradigm. The challenge laid in understanding what remains of the revolutionary ideas from which neural networks stemmed in the eighties, how these networks have evolved and influenced other research fields, and ultimately, what the new conceptual/methodological frontiers are that need to be trespassed for a better exploitation of the information carried by data. This book presents the outcome of this discussion. New Directions in Neural Networks is divided in two general subjects, models and applications and two specific ones, economy and complexity and remote sensing image processing. The editors of this book have set out to publish a scientific contribution to the discovery of new forms of cooperative work that are necessary today for the invention of efficient computational systems and new social paradigms.IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in:-Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences
Data Flow Analysis on 3D Printing for Distributed Manufacturing Information System

3D printing is a new kind technology with great future. As a new processing technology in the intelligent manufacturing, it will change the way of mass production assembly line as the representative of the second industrial revolution. At the same time, distributed production need new data flow strategy and new network for big data. In this article, we introduced the research for the distributed manufacture network and data flow analysis.
Automating IoT Data Ingestion Enabling Visual Representation

The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to extract and generate the IoT device model, as well as to create the instances of the device and generate eventual time series. The whole process allows data for visual analytics and dashboarding to be prepared in a few clicks. The proposed IoT device model is compliant with FIWARE NGSI and is supported by a formal definition of data characterization in terms of value type, value unit, and data type. The resulting data model has been enforced into the Snap4City dashboard wizard and tool, which is a GDPR-compliant multitenant architecture. The solution has been developed and validated by considering six different pilots in Europe for collecting big data to monitor and reason people flows and tourism with the aim of improving quality of service; it has been developed in the context of the HERIT-DATA Interreg project and on top of Snap4City infrastructure and tools. The model turned out to be capable of meeting all the requirements of HERIT-DATA, while some of the visual representation tools still need to be updated and furtherly developed to add a few features.
Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data

With the emergence of machine learning for the classification of sleep and other human behaviors from accelerometer data, the need for correctly annotated data is higher than ever. We present and evaluate a novel method for the manual annotation of in-bed periods in accelerometer data using the open-source software Audacity®, and we compare the method to the EEG-based sleep monitoring device Zmachine® Insight+ and self-reported sleep diaries. For evaluating the manual annotation method, we calculated the inter- and intra-rater agreement and agreement with Zmachine and sleep diaries using interclass correlation coefficients and Bland–Altman analysis. Our results showed excellent inter- and intra-rater agreement and excellent agreement with Zmachine and sleep diaries. The Bland–Altman limits of agreement were generally around ±30 min for the comparison between the manual annotation and the Zmachine timestamps for the in-bed period. Moreover, the mean bias was minuscule. We conclude that the manual annotation method presented is a viable option for annotating in-bed periods in accelerometer data, which will further qualify datasets without labeling or sleep records.
How COVID-19 Has Affected Caregivers’ Burden of Patients with Dementia: An Exploratory Study Focusing on Coping Strategies and Quality of Life during the Lockdown

COVID-19 has caused a public and international health emergency, leading to isolation and social distancing. These restrictions have had a significant impact on the caregivers of people with dementia, increasing the burden of patient management. The purpose of this study was to investigate the stress perceived by caregivers of patients with Alzheimer’s disease (AD) during the pandemic. We used a cross-sectional survey design to evaluate the caregivers’ psychological responses and coping strategies. Eighty-four caregivers of patients with a diagnosis of AD were involved in this study by completing an online questionnaire. They presented a high perception of stress (the Perceived Stress Scale mean ± DS: 33.5 ± 4.5), and their high burden in caring was mainly related to physical difficulties (Caregiver Burden Inventory–Physical Burden mean ± DS: 15.0 ± 2.1) and perception of loss of time (Caregiver Burden Inventory–Time-dependence Burden mean ± DS: 16.5 ± 1.4). Moreover, caregivers perceived their quality of life as very low (Short Form-12 Health Survey Physical mean ± DS: 13.5 ± 2.7; Short Form-12 Health Survey Mental Health mean ± DS: 16.4 ± 4.2). Finally, we found that participants mostly used dysfunctional coping strategies, such as avoidance strategies (Coping Orientation to Problem Experiences–Avoidance Strategies mean ± DS: 39.5 ± 7.1), but these strategies did not affect the stress level of caregivers. Given that caregivers present a high burden and stress, innovative tools could be a valuable solution to investigate and support their emotional and behavioral status during difficult periods, such as the COVID-19 pandemic.
Run manager module for CORAL laboratory management

This thesis describes a new module, the Run Manager (RM), for Stanford Nanofabrication Facility’s Common Object Representation for Advanced Laboratories (CORAL). CORAL is the lab manager with which MIT’s Microsystems Technology Laboratories hopes to replace its outmoded, fifteen-year-old lab manager. RM will be used to collect and store parameterized information about each step in the device fabrication process, which labs will use to automatically generate accounting, maintenance, and lot history reports. RM consists of an XML (Extensible Markup Language)configurable Common Object Request Broker Architecture (CORBA) application server, database storage, and a Graphical User Interface (GUI) module which will be integrable with the existing CORAL Java client. Thesis Supervisor: Vicky Diadiuk Title: Principal Research Engineer, MTL
INNOVATING BACKWARDS: REVERSE R&D PROCESS IN SERVICE INNOVATION

Services have traditionally been perceived as less innovative than product, raising questions about why service firms are less able to apply best innovation practices than their product counterparts, and whether the process of service innovation is well understood. Based on the case-study research performed on four product-service providers, our findings reveal that conventional product-oriented thinking about the nature of the innovation process does not extend to services. While product innovation begins with research, continues with design/development, and is followed by production and use respectively, the innovation of a service starts with the initial design, while the actual research is