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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

The historical and cultural heritage has been damaged and destroyed for many years by catastrophic events. In Italy, the safeguarding of historic masonry buildings from seismic events has drawn the attention of researchers, often underestimating other external factors. This may be due to the fact that, over the centuries, earthquakes have frequently destroyed entire cities. In order to preserve and monitor historical and cultural heritage, structural health monitoring (SHM) systems based on the Internet of Things (IoT) paradigm, associated with maintenance and classification projects such as CARTIS, have been introduced in recent years. These systems guarantee not only the monitoring and maintenance of the single building but also a large-scale control of an urban neighbourhood. The purpose of this paper is to define a preliminary SHM system capable of preserving cities from catastrophic events other than earthquakes, focusing on the study of the phenomenon of river flooding. This is addressed by proposing a monitoring system based on smart nodes implementing new mathematical modelling to foreseen floods.

This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evaluate human pain intensity levels. This integration aims to enhance the recognition system’s performance and enable a more accurate assessment of pain intensity. Such a multimodal approach supports improved decision making in real-time patient care, addressing limitations inherent in unimodal systems for measuring pain sentiment. So, the primary contribution of this work lies in developing a multimodal pain sentiment analysis system that integrates the outcomes of image-based and audio-based pain sentiment analysis models. The system implementation contains five key phases. The first phase focuses on detecting the facial region from a video sequence, a crucial step for extracting facial patterns indicative of pain. In the second phase, the system extracts discriminant and divergent features from the facial region using deep learning techniques, utilizing some convolutional neural network (CNN) architectures, which are further refined through transfer learning and fine-tuning of parameters, alongside fusion techniques aimed at optimizing the model’s performance. The third phase performs the speech-audio recording preprocessing; the extraction of significant features is then performed through conventional methods followed by using the deep learning model to generate divergent features to recognize audio-based pain sentiments in the fourth phase. The final phase combines the outcomes from both image-based and audio-based pain sentiment analysis systems, improving the overall performance of the multimodal system. This fusion enables the system to accurately predict pain levels, including ‘high pain’, ‘mild pain’, and ‘no pain’. The performance of the proposed system is tested with the three image-based databases such as a 2D Face Set Database with Pain Expression, the UNBC-McMaster database (based on shoulder pain), and the BioVid database (based on heat pain), along with the VIVAE database for the audio-based dataset. Extensive experiments were performed using these datasets. Finally, the proposed system achieved accuracies of 76.23%, 84.27%, and 38.04% for two, three, and five pain classes, respectively, on the 2D Face Set Database with Pain Expression, UNBC, and BioVid datasets. The VIVAE audio-based system recorded a peak performance of 97.56% and 98.32% accuracy for varying training–testing protocols. These performances were compared with some state-of-the-art methods that show the superiority of the proposed system. By combining the outputs of both deep learning frameworks on image and audio datasets, the proposed multimodal pain sentiment analysis system achieves accuracies of 99.31% for the two-class, 99.54% for the three-class, and 87.41% for the five-class pain problems.

Structural Health Monitoring (SHM) systems play an important role in the defence of historical heritage. In fact, it is necessary to monitor and analyse the information that highlight the state of health of the structure. Unfortunately, in the realization of the monitoring system it is necessary to place several sensor nodes in place where it is not simple or possible the connection to the mains power supply. Moreover, it is necessary the fast detection of critical situation in which the state of conservation of an historical building can deteriorate. For these reasons in the paper is proposed the design of a low power consumption long range communication system. This design is proposed as an innovation of previous work, and the use of machine learning an algorithm to analyse the data.

The clinical spectrum of congenital anomalies of the kidney and urinary tract (CAKUT) encompasses a common birth defect in humans that has significant impact on long-term patient survival. Overall, data indicate that approximately 20% of patients may have a genetic disorder that is usually not detected based on standard clinical evaluation, implicating many different mutational mechanisms and pathogenic pathways. In particular, 10% to 15% of CAKUT patients harbor an unsuspected genomic disorder that increases risk of neurocognitive impairment and whose early recognition can impact clinical care. The emergence of high-throughput genomic technologies is expected to provide insight into the common and rare genetic determinants of diseases and offer opportunities for early diagnosis with genetic testing.

Soil degradation due to global warming, water scarcity and diminishing natural resources negatively impacts food security. Soil fertility deterioration, particularly phosphorus (P) deficiency, remains a challenge in the arid and semi-arid regions. In this study, field experiments were conducted in different geographical locations to investigate the effects of organic amendments coupled with P fertilization and irrigation on soil physical-chemical properties, and the growth, yield and quality of wheat. Application of P fertilizers combined with organic amendments mitigated soil salinity, increased organic matter content, available water, hydraulic conductivity and available macronutrients, but decreased soil bulk density. Application of organic amendments slightly increased total Cd, Ni and Pb in soil, but Cd and Ni concentration was below allowable limits whilst Pb reached a hazardous level. Soil P fractions were significantly increased with the combined application of mineral P and organic amendments irrespective of salinity and irrigation. Crop growth yield and quality of wheat improved significantly in response to the integrated application of mineral P and organic amendments. In conclusion, the combination of mineral P sources with organic amendments could be successfully used as a cost-effective management practice to enhance soil fertility and crop production in the arid and semi-arid regions stressed with water scarcity and natural resource constraints.

The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS–DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.

Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease. Polygenic factors may partially explain the observed variability in the penetrance of monogenic diseases. Here, the authors show that a polygenic risk score for chronic kidney disease is significantly associated with a higher risk of renal dysfunction in the two most common monogenic forms of kidney disease, suggesting that accounting for polygenic factors improves risk stratification in monogenic kidney disease.

We present the physical and orbital parameters of four short-period eclipsing W UMa systems: Z T F J 000030.44 + 391106.9 (referred to as S1), Z T F J 000817.08 + 402532.1 (referred to as S2), Z T F J 002158.44 + 252934.04 (referred to as S3), and Z T F J 003357.62 + 415747.8 (referred to as S4). The absolute parameters and evolutionary status of these systems are determined, and new times of minima are calculated. Additionally, we present the 3D fill-out configuration for each system. The four Systems exhibit moderate contact W UMa binary with a fill-out factor of 49%, 38%, 28%, and 51%, respectively. Comparing the systems’ periods, we observed a proportional relationship, where shorter periods correspond to lower fill-out factors, and longer periods were associated with higher fill-out factors. Based on the derived surface temperatures and mass ratios of the components, all systems are classified as A-type W UMa binaries. The obtained parameters in addition to a list of previously published data are then utilized to derive an updated Mass-Luminosity relation (M-L) for both A and W-type eclipsing W UMa systems. A comparison with previously published relations reveals that the majority of the EW systems lie between 0.2 and 2 M sun on the M-L diagram. Moreover, we discuss the dynamical evolutionary aspects and evolutionary status of the four components, along with their positions on the Zero Age Main Sequence (ZAMS) and Terminal Age Main Sequence (TAMS).

Technologies such as next-generation sequencing and chromosomal microarray have advanced the understanding of the molecular pathogenesis of a variety of renal disorders. Genetic findings are increasingly used to inform the clinical management of many nephropathies, enabling targeted disease surveillance, choice of therapy, and family counselling. Genetic analysis has excellent diagnostic utility in paediatric nephrology, as illustrated by sequencing studies of patients with congenital anomalies of the kidney and urinary tract and steroid-resistant nephrotic syndrome. Although additional investigation is needed, pilot studies suggest that genetic testing can also provide similar diagnostic insight among adult patients. Reaching a genetic diagnosis first involves choosing the appropriate testing modality, as guided by the clinical presentation of the patient and the number of potential genes associated with the suspected nephropathy. Genome-wide sequencing increases diagnostic sensitivity relative to targeted panels, but holds the challenges of identifying causal variants in the vast amount of data generated and interpreting secondary findings. In order to realize the promise of genomic medicine for kidney disease, many technical, logistical, and ethical questions that accompany the implementation of genetic testing in nephrology must be addressed. The creation of evidence-based guidelines for the utilization and implementation of genetic testing in nephrology will help to translate genetic knowledge into improved clinical outcomes for patients with kidney disease.