bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feature Specific Hybrid Framework on composition of Deep learning architecture for speech emotion recognition
Authors: Hussain Mansoor , Abishek S , Ashwanth K P , Bharanidharan C , Girish S 
Published: May 2021

Abstract
Speech cues may be used to identify human emotions using deep learning model of speech emotion recognition using supervised learning or unsupervised learning as machine learning concepts, and then it build the speech emotion databases for test data prediction. Despite of many advantageous, still it suffers from accuracy and other aspects. In order to mitigate those issues, we propose a new feature specific hybrid framework on composition of deep learning architecture such as recurrent neural network and convolution neural network for speech emotion recognition. It analyses different characteristics to make a better description of speech emotion. Initially it uses feature extraction technique using bag-of-Audio-word model to Mel-frequency cepstral factor characteristics and a pack of acoustic words composed of emotion features to feed the hybrid deep learning architecture to result in high classification and prediction accuracy. In addition, the proposed hybrid networks’ output is concatenated and loaded into this layer of softmax, which produces a for speech recognition, a categorical classification statistic is used. The proposed model is based on the Ryerson Audio-Visual Database of Emotional Speech and Song audio (RAVDESS) dataset, which comprises eight emotional groups. Experimental results on dataset prove that proposed framework performs better in terms of 89.5% recognition rate and 98% accuracy against state of art approaches.
Solar Powered Real Time Transformer Health Monitoring System Using Internet of Things (IoT)
Authors: Jeevanandham A , Nitin B , Maheshkumar M , kumar B Jeevith , Monish B 
Published: May 2021

Abstract
Distribution transformer are primary and most important segment in a distribution system. As there is no efficient protocols to monitor the vital parameters of the transformer, the transformers health declines rapidly, thereby resulting in complete failure or partial malfunction in the distribution transformer. So we give a system to detect the health of the transformer. Using the help of the sensors we can sense that variations in current, voltage, temperature in the transformer. Before this changes cause any damage in transformer we will cut off the supply. Also it will store in cloud for the future analysis. The system will also detect the abnormalities in the measured parameters and then sends an alert message to the authorized personnel about the situation. Then the personnel can assess the situation by viewing the real time data that is made available in the IoT platform and can download for any report.
Administered Machine Learning Models for Covid-19 Future Forecasting
Authors: Atchaya K , Darshinii M , Harini R , Suganya T 
Published: May 2021

Abstract
The spread of COVID-19 in the entire world has put the human kind in danger. The assets of probably the biggest economies are worried because of the enormous infectivity and contagiousness of this illness. The ability of ML models to conjecture the quantity of forthcoming patients influenced by COVID-19 which is by and by considered as a likely danger to humanity. Specifically, four standard estimating models linear regression (LR), least total shrinkage and determination administrator (LASSO) Support vector Machine (SVM) have been utilized in this examination to figure the undermining components of COVID-19. Three sorts of expectations are made by every one of the models, for example, the quantity of recently tainted cases, the quantity of passing, and the quantity of recuperations But in the can’t foresee the precise outcome for the patients. To defeat the issue, proposed strategy utilizing the exponential smoothing (ES) anticipate the quantity of COVID-19 cases in next 30 days ahead and impact of preventive estimates like social seclusion and lockdown on the spread of COVID-19.
IOT Based Tool Garbage Management System
Authors: Sathyabama S , Bharath Raaj A , Cibi Saamraat S S , Girimurugan T 
Published: May 2021

Abstract
These days, safe and hygiene disposal of waste via dustbins in smart cities needs a centralized and monitored system. This system monitors various bins used for rubbish and garbage collectionextent is also informed inside these garbage bins via centralized online Application. For this the system the bins are fitted specially placed ultrasonic sensors to determine the level and depth of rubbish. It incorporates an AVR based microcontroller, GSM Module, Wi-Fi modem, a buzzer for alert and LCD screen for viewing data. The voltage support is given by a 12V transformer. To determine the display status and the extent to which the rubbish bin is loaded an LCD screen is used. These LCD screens are used inside the rubbish bins. To point out the condition however the web application is designed which is used to monitor the garbage or rubbish bins. The geographical view of rubbish bins can be obtained by the web page and also these web pages indicate the colored rubbish that they can highlight
Fine Grained Sentimental Analysis of Social Network Chat Using R
Authors: Kiruthika J K , Janani A P , Sudha M , Yawanikha T 
Published: May 2021

Abstract
In current years, sentiment examination has expanded much consideration in, analyzing and monitoring data of social media, public relations, data mining, market research, medical data, political analysis, cyberbullying etc., in all areas extended the utilization of sentiment analysis. Sentiment exploration are classified into three categories in which Rule-based frameworks accomplish sentiment analysis based on a set of physically created procedures. Programmed frameworks rest on machine learning procedures to learn from information though Hybrid frameworks combine both procedure based and programmed approaches. In this paper we apply rule based characteristic language processing and content analysis procedures to recognize and extricate subjective data from content of social media and centering on extremity such as positive, negative, unbiased conjointly on sentiments and feelings as irate, upbeat, pitiful.. By periodically sorting the assumption behind surveys, social media conversations and more ready to make quicker and more precise decisions.
Detection of Leukemia Using Machine Learning Algorithms
Authors: Jayavikash K P , Gopinath P , Nandheeswaran U , Sivabalan R , Divya Bharathi T 
Published: May 2021

Abstract
Detection of disorders of Blood usually include visual inspection through a microscope. This may help to classify various conditions related to human blood. The paper details about evolving a system that could help in diagnosis and detection of Leukemia at an early stage. Only then controlling and prevention can be done. The proposed system focuses on White Blood Cells as they are the affected ones in case of Leukemia. The affected cells differ in shape and color features, which are used as classifier inputs.
Using FDA-approved drugs as off-label fluorescent dyes for optical biopsies: from in silico design to ex vivo proof-of-concept
Authors: Larson Michael C , Gmitro Arthur F , Utzinger Urs , Rouse Andrew R , Woodhead Gregory J , Carlson Quinlan , Hennemeyer Charles T , Barton Jennifer K 
Published: May 2021

Abstract
Optical biopsies bring the microscope to the patient rather than the tissue to the microscope, and may complement or replace the tissue-harvesting component of the traditional biopsy process with its associated risks. In general, optical biopsies are limited by the lack of endogenous tissue contrast and the small number of clinically approved in vivo dyes. This study tests multiple FDA-approved drugs that have structural similarity to research dyes as off-label in situ fluorescent alternatives to standard ex vivo hematoxylin & eosin tissue stain. Numerous drug-dye combinations shown here may facilitate relatively safe and fast in situ or possibly in vivo staining of tissue, enabling real-time optical biopsies and other advanced microscopy technologies, which have implications for the speed and performance of tissue- and cellular-level diagnostics.
Microstructure, erosion and tribological behaviour of thick DLC coatings by the filtered cathodic vacuum arc combined with a high-voltage pulse power
Authors: Shen Yongqing , Luo Jun , Liao Bin , Zhang Xu , Zhao Yuanyuan , Chen Lin , Pang Pan , Zeng Xinmiao 
Published: May 2021

Abstract
Hydrogen-free diamond-like carbon (DLC) films with thickness of 36 μm and 50 μm have been prepared on Si and AISI 304L stainless steel substrates, respectively, by filtered cathodic vacuum arc (FCVA) together with a high-voltage pulse power. The structure, chemical bonding state, mechanical properties and wear, corrosion resistance and anti-erosion performance were evaluated. The thick DLC films with compressive stress of 0.71 GPa and hardness of 3160 HV were obtained. The preparation of the low stress but high hardness coating mainly due to the appropriate high negative bias and short pulse duration, which could result in an alternate ion bombardment and deposition. The 50 μm-thick coating showed corrosion resistance with high polarization resistance of 4.69 × 108 Ω·cm2 and low corrosion current density of 5.47 × 10−8 A·cm−2. Thick coating exhibited outstanding sand particle erosion resistance with low average mass loss rate of 0.035 mg g−1. Under high-speed sand erosion conditions, the coating remains unbroken, without peeling or cracking were observed on the erosion center. In addition, DLC coatings showed good tribological performance under dry, water- and oil-lubricated condition.
Can a risk of harm itself be a harm?
Authors: Rowe Thomas 
Published: May 2021

Abstract
Many activities impose risks of harm on other people. One such class of risks are those that individuals culpably impose on others, such as the risk arising from reckless driving. Do such risks in themselves constitute a harm, over and above any harm that actually eventuates? This paper considers three recent views that each answer in the affirmative. I argue that each fails to overcome what I call the ‘interference objection’. The risk of harm itself, whether taken as a subjective or an objective risk, is unable to interfere with the interests of victims in order to constitute a harm. This does not mean that a risk of harm cannot itself be wrongful, but the conclusion does weaken the moral objectionableness of impositions of risks of harm.
Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs
Authors: Zervou Michaela Areti , Doutsi Effrosyni , Pavlidis Pavlos , Tsakalides Panagiotis 
Published: May 2021

Abstract

Motivation
Protein structural class prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation along with time series analysis tools such as recurrence quantification analysis, complex networks, horizontal visibility graphs (HVG) and others. However, the majority of existing works involve a large amount of features and they require an exhaustive, time consuming search of the optimal parameters. To address the aforementioned problems, this work adopts the generalized multidimensional recurrence quantification analysis (GmdRQA) as an efficient tool that enables to process concurrently a multidimensional time series and reduce the number of features. In addition, two data-driven algorithms, namely average mutual information and false nearest neighbors, are utilized to define in a fast yet precise manner the optimal GmdRQA parameters.

Results
The classification accuracy is improved by the combination of GmdRQA with the HVG. Experimental evaluation on a real benchmark dataset demonstrates that our methods achieve similar performance with the state-of-the-art but with a smaller computational cost.

Availability and implementation
The code to reproduce all the results is available at https://github.com/aretiz/protein_structure_classification/tree/main.

Supplementary information
Supplementary data are available at Bioinformatics online.
A Refinement of Key Mismatch Attack on NewHope
Authors: Zhang Xue , Zheng Zhongxiang , Wang Anyu 
Published: May 2021

Abstract
NewHope cryptosystem is one of the second-round submissions of the National Institute of Standards and Technology post-quantum cryptography standardization process, which is a suite of two key encapsulation mechanisms based on the ring-learning with errors (LWE) problem. It has received much attention from the research community due to its small key size and high efficiency. Recently, three key mismatch attacks are proposed against NewHope under the condition of key reuse. They do not solve the ring-LWE instance directly but exploit the leakage of secret information. As far as we know, the best result is given by Okada et al. ((2020) Improving Key Mismatch Attack on NewHope with Fewer Queries. In Proc. of the 25th Australasian Conf. on Information Security and Privacy, Perth, WA, Australia, November 30–December 2, pp. 505–524. Springer Cham, Switzerland), which recovers the whole secret with a success probability of $97\%$ and $233,803$ average queries. In this paper, we further improve the key mismatch attack of NewHope by reducing the average queries to $106,577$ and raising the success probability to $100\%$. Moreover, we analyze the key mismatch attack without key reuse for the first time and we propose a combinatorial attack against NewHope1024. The total complexity of the combinatorial attack is $2^{253}$, which is lower than the complexity of primal attack and the claimed security strength of NewHope1024.
N07 A qualitative study of the impact of inflammatory bowel disease on partners
Authors: Thapwong P , Norton C , Terry H , Czuber-Dochan W 
Published: May 2021

Abstract

Background
Inflammatory Bowel Disease (IBD) not only impacts the quality of life of the patient, but also affects their family members. Studies to date have provided an understanding of impact of IBD and IBD implications for people with IBD. However, little is known about the impact of IBD on their family members. Therefore, the current study aimed to explore the lived experience of people with IBD and their family members regarding the impacts of IBD on family members and their coping methods.

Methods
Twelve participants, including six people with IBD with their six partners, were purposively selected, no other family members (parents / children, sibling) came forward to participate in the study. The in-depth, semi-structured online interviews were conducted via Skype, Zoom, or Microsoft Teams between February-June 2020. Interviews were audio-recorded, transcribed verbatim, and analysed using inductive thematic analysis by Braun and Clarke.

Results
Four main themes emerged during the analysis under the central theme “our relationship with IBD, for better or worse”. IBD affected the partners in terms of their own relationship, relationship with others, everyday life, and emotional and mental well-being. The theme “our relationship” showed the impact of IBD on the relationship between a couple, including the intimate relationship, family planning, role change as partner and carer, and the importance of honest communication. IBD also affected wider relationships with family, children, and social life, but teamwork could mediate negative impact in relationships. Emotional well-being was impacted by living in constant fear and guilt. Humour and knowledge of IBD reduced negative impacts. IBD impacts on everyday life (diet, finances, and travel) for both patients and partners. Planning for uncertain situations was helpful to reduce restrictions.

Conclusion
The study provides an understanding of IBD impact on partners and the coping strategies from patients and partners’ perspectives. There are wide-ranging implications for health and social care professionals caring for people with IBD and their families. Social support has been recognised as a vital buffering mechanism in facilitating an individual’s adjustment to IBD. Healthcare professionals and researchers may integrate a bio-psycho-social approach into their work with IBD family members. There is a need to develop interventions to help family members of IBD patients to better cope with the illness and to have a more fulfilling life.
OP22 Factors independently associated with fatigue in IBD: Results from the baseline dataset of the PREdiCCt study
Authors: Derikx L , Siakavellas S , Derr L , Williams L , Nikolas P , Jenkinson P , Lucaciu L , Constantine-Cooke N , Covil K , Murdoch L , Jones G R , Lees C 
Published: May 2021

Abstract

Background
Fatigue is one of the most common symptoms in IBD resulting in decreased quality of life, impaired work productivity, and higher societal costs. However, little is known about its etiology and pathophysiology. We aimed to estimate the prevalence of fatigue and to identify predictive factors for fatigue.

Methods
The PREdiCCt study (https://www.predicct.co.uk) is the largest prospective study of the causes of IBD flare. 2629 patients in clinical remission were recruited from 48 UK sites. 1946 (74%) patients completed the baseline questionnaires. We assessed the prevalence of fatigue at baseline using a single item from the IBD Control questionnaire. To identify predictors for fatigue, we performed univariable and multivariable analyses including demographic, biochemical, environmental and psychosocial factors such as anxiety and depression [HADS], sleep quality [PSQI] and physical exercise [GPAQ]).

Results
759/1919 IBD patients in clinical remission (39.6%) reported fatigue in the past 2 weeks, while 1034 patients (53.9%) did not report fatigue. Patients who reported fatigue were more frequently female, had more frequently CD, and were more frequently smokers (Table 1). Univariable comparisons showed higher inflammatory markers in the fatigued group, with fewer patients in clinical remission. Multivariable analyses identified female sex (OR 2.4), CRP&gt;5 (OR 2.1), bad sleep quality (OR 2.5), anxiety (OR 1.8) and depression (OR 6.2) as independent factors associated with fatigue (Table 2).

Conclusion
We show the significant burden of fatigue in IBD patients and describe putative causes which demonstrate both the impact of residual gut inflammation and the relationship between fatigue and psychological well-being. The impact of environmental and dietary factors on fatigue is being further investigated with ongoing longitudinal data collection in the PREdiCCt study.
P092 Development and evaluation of i-Tracker Ustekinumab and i-Tracker Anti-ustekinumab kits: fast and innovative chemiluminescent assays for the monitoring of patients treated with ustekinumab
Authors: Noguier G , Montaillier C , Daviere S , Yang Y , Colombeau L , Parussini E 
Published: May 2021

Abstract

Background
Ustekinumab, a monoclonal antibody directed against IL12/23, is a drug widely used for the treatment of Chronic Inflammatory Diseases (Psoriasis, Crohn’s disease, etc.). Therapeutic Drug Monitoring is currently proposed to provide useful information to clinicians to improve the efficacy of the treatment. Theradiag has just developed the innovative i-Tracker ustekinumab and i-Tracker Anti-ustekinumab kits: fast quantification of ustekinumab and Anti-ustekinumab antibodies fully automated on the random access i-Track10 chemiluminescent analyzer.

Methods
Analytical performances were assessed using 2 types of serum samples: human serum spiked with ustekinumab or Anti-ustekinumab antibodies, and samples from Inflammatory Bowel Diseases patients treated with ustekinumab (n=32). For drug measurement, ustekinumab from serum sample was captured by anti-idiotypic antibody coupled magnetic microparticles and anti-ustekinumab polyclonal antibodies conjugated to acridinium ester were used for the detection of ustekinumab. For anti-drug antibodies measurement, Anti-ustekinumab antibodies were captured according to Ustekinumab coupled magnetic microparticles and detected with the use of ustekinumab conjugated to acridinium ester. Light emission was linked to the quantity of ustekinumab, or anti-ustekinumab antibodies presents in the sample.

Results
Ustekinumab measurement showed high accuracy (recovery was comprised between 98% and 120%). High precision weas reached for both assays (intra-precision CV were below 9.1% and 5.3% for ustekinumab and Anti-ustekinumab assays; inter-precision CV were below 4.6% and 10.6% for ustekinumab and Anti-ustekinumab assays) and no interference was seen with biologic agents (bilirubin, hemoglobin, lipids, biotin and rheumatoid factors). The dynamic ranges of the assays were 100ng/ml to 10 000ng/ml for ustekinumab quantification and 1 AU/ml to 250 AU/ml for anti-ustekinumab antibodies quantification. i-Tracker ustekinumab and i-Tracker anti-ustekinumab assays were compared to respective ELISA based LISA-TRACKER assays and showed excellent correlation (R² = 0.96, Slope = 0.93 for ustekinumab assay; Spearman’s coefficient correlation was 0.95 for Anti-ustekinumab assay).

Conclusion
i-Tracker kits are innovative assays which exhibit fast (time to results &lt; 40min), accurate and reproducible results for the quantification of ustekinumab and Anti-ustekinumab antibodies. Excellent agreements were observed with respective LISA-TRACKER assays. i-Tracker kits are valuable tools for the monitoring of patients treated with ustekinumab.
P112 Impact of the SARS-COV2 pandemic on patients with inflammatory bowel disease - Preliminary results of a prospective, single-center study
Authors: Martins Figueiredo L , Correia F , Rafael M A , Lourenço L , Oliveira A M , Branco J C , Santos L , Martins A 
Published: May 2021

Abstract

Background
The real impact of COVID-19 infection on patients with Inflammatory Bowel Disease (IBD) is unknown. It was speculated that this population could be a risk group. The aim of this study is to evaluate the incidence of SARS-CoV2 infection, the impact of initiation/change of IBD therapy and its morbidity and mortality, during the COVID-19 pandemic in Portugal.

Methods
Prospective cross-sectional study. Patients with IBD followed at a Gastroenterology Department in an area with a high incidence rate of SARS-CoV2 were included, from 01/03/2020 to 31/08/2020. Data was obtained through telephonic appointments, hospital inpatients admissions, Emergency Department Service and Day Hospital episodes.

Results
335 patients were included, 194 female, with a mean age of 47.4 years (18–88). 200 had Crohn’s disease (CD), 132 Ulcerative Colitis, 3 unclassified colitis. 320 were on therapy (95.5%): salicylates n=230 (71.9%), systemic corticosteroids n=34 (10.6%) (18 started during the pandemic), thiopurines/methotrexate n=117 (36.6%) (8 started in this period), biological n=148 (46.3%) (14 started in this period). 7 patients (2.2%) triple immunosuppressed. 75 patients had disease in remission, 232 mild/moderate, 28 severe disease (requiring hospitalization). SARS-CoV2 infection was observed in 3 patients (incidence rate: 0.89%), treated as an outpatient basis. 2 male, mean age 58 years, 2 with CD. Comorbidities: 0: n = 1; 2: n = 1; 4: n = 1. 2 patients were on salicylates and one on adalimumab (monotherapy, before the pandemic). There were no deaths.

Conclusion
All patients started or maintained their IBD therapy according to current international guidelines. A significantly higher incidence of COVID-19 infection than that of the local and Portuguese population in general has not been documented. According to our preliminary results, the population with IBD does not appear to be a risk group for acquiring infection or having a severe course of the disease.