bluuebunny/crossref_metadata_2025 huggingface dataset:
Modulating Nucleus Oxygen Concentration by Altering Intramembrane Cholesterol Levels: Creating Hypoxic Nucleus in Oxic Conditions
Authors: Seco Joao , King Clarence C. , Camazzola Gianmarco , Jansen Jeannette , Tirinato Luca , Marafioti Maria G. , Hanley Rachel , Pagliari Francesca , Beckman Scott P.
Published: May 2022

We propose a novel mechanism by which cancer cells can modulate the oxygen concentration within the nucleus, potentially creating low nuclear oxygen conditions without the need of an hypoxic micro-environment and suited for allowing cancer cells to resist chemo- and radio-therapy. The cells ability to alter intra-cellular oxygen conditions depends on the amount of cholesterol present within the cellular membranes, where high levels of cholesterol can yield rigid membranes that slow oxygen diffusion. The proposed mechanism centers on the competition between (1) the diffusion of oxygen within the cell and across cellular membranes that replenishes any consumed oxygen and (2) the consumption of oxygen in the mitochondria, peroxisomes, endoplasmic reticulum (ER), etc. The novelty of our work centers around the assumption that the cholesterol content of a membrane can affect the oxygen diffusion across the membrane, reducing the cell ability to replenish the oxygen consumed within the cell. For these conditions, the effective diffusion rate of oxygen becomes of the same order as the oxygen consumption rate, allowing the cell to reduce the oxygen concentration of the nucleus, with implications to the Warburg Effect. The cellular and nucleus oxygen content is indirectly evaluated experimentally for bladder (T24) cancer cells and during the cell cycle, where the cells are initially synchronized using hydroxeaurea (HU) at the late G1-phase/early S-phase. The analysis of cellular and nucleus oxygen concentration during cell cycle is performed via (i) RT-qPCR gene analysis of hypoxia inducible transcription factors (HIF) and prolyl hydroxylases (PHD) and (ii) radiation clonogenic assay every 2 h, after release from synchronization. The HIF/PHD genes allowed us to correlate cellular oxygen with oxygen concentration in the nucleus that is obtained from the cells radiation response, where the amount DNA damage due to radiation is directly related to the amount of oxygen present in the nucleus. We demonstrate that during the S-phase cells can become hypoxic in the late S-phase/early G2-phase and therefore the radiation resistance increases 2- to 3-fold.
Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
Authors: Miettinen Teemu , Nieminen Anni I. , Mäntyselkä Pekka , Kalso Eija , Lötsch Jörn
Published: May 2022

Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.
A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine
Authors: Beltran-Perez Carlos , Serrano Andrés A. A. , Solís-Rosas Gilberto , Martínez-Jiménez Anatolio , Orozco-Cruz Ricardo , Espinoza-Vázquez Araceli , Miralrio Alan
Published: May 2022

A study of 250 commercial drugs to act as corrosion inhibitors on steel has been developed by applying the quantitative structure-activity relationship (QSAR) paradigm. Hard-soft acid-base (HSAB) descriptors were used to establish a mathematical model to predict the corrosion inhibition efficiency (IE%) of several commercial drugs on steel surfaces. These descriptors were calculated through third-order density-functional tight binding (DFTB) methods. The mathematical modeling was carried out through autoregressive with exogenous inputs (ARX) framework and tested by fivefold cross-validation. Another set of drugs was used as an external validation, obtaining SD, RMSE, and MSE, obtaining 6.76%, 3.89%, 7.03%, and 49.47%, respectively. With a predicted value of IE% = 87.51%, lidocaine was selected to perform a final comparison with experimental results. By the first time, this drug obtained a maximum IE%, determined experimentally by electrochemical impedance spectroscopy measurements at 100 ppm concentration, of about 92.5%, which stands within limits of 1 SD from the predicted ARX model value. From the qualitative perspective, several potential trends have emerged from the estimated values. Among them, macrolides, alkaloids from Rauwolfia species, cephalosporin, and rifamycin antibiotics are expected to exhibit high IE% on steel surfaces. Additionally, IE% increases as the energy of HOMO decreases. The highest efficiency is obtained in case of the molecules with the highest ω and ΔN values. The most efficient drugs are found with pKa ranging from 1.70 to 9.46. The drugs recurrently exhibit aromatic rings, carbonyl, and hydroxyl groups with the highest IE% values.
Factors Influencing Students’ Intention to Use E-Textbooks and Their Impact on Academic Achievement in Bilingual Environment: An Empirical Study Jordan
Authors: Masa’deh Ra’ed , AlHadid Issam , Abu-Taieh Evon , Khwaldeh Sufian , Alrowwad Ala’aldin , Alkhawaldeh Rami S.
Published: May 2022

E-textbooks are becoming increasingly important in the learning and teaching environments as the globe shifts to online learning. The key topic is what elements influence students’ behavioral desire to use e-textbooks, and how the whole operation affects academic achievement when using e-textbooks. This research aims to investigate the various factors that influence the behavioral intention to use an e-textbook, which in turn influences academic achievement in a bilingual academic environment. The research model was empirically validated using survey data from 625 e-textbook users from bilingual academic institutes from Jordan. Structural equation modeling (SEM) analysis was employed to test the research hypotheses by using Amos 20. To validate the results, artificial intelligence (AI) was employed via five machine learning (ML) techniques: artificial neural network (ANN), linear regression, and sequential minimal optimization algorithm for support vector machine (SMO), bagging with REFTree model, and random forest. The empirical results offer several key findings. First, the behavioral intention of using an e-textbook positively influences academic achievement. Second, attitude toward e-textbooks, subjective norms toward e-textbooks, and perceived behavior control toward e-textbooks positively influence behavioral intention toward using e-textbooks. Attitude toward using e-textbooks and perceived behavioral control both are positively influenced by independent factors. This study contributes to the literature by theorizing and empirically testing the impacts of e-textbooks on the academic achievement of university students in a bilingual environment in Jordan.
Influence of the Reaction Injection Moulding Process on the Thermomechanical Behaviour of Fast Curing Polyurethane
Authors: Lehmenkühler Peter , Stommel Markus
Published: May 2022

In this contribution, the influence of the reaction injection moulding process on the thermomechanical material behaviour of aliphatic hexamethylene diisocyanate (HDI) based fast curing polyurethane is demonstrated. Uniaxial tensile tests, temperature-frequency dependent dynamic mechanical thermal analysis (DMTA) and Differential Scanning Calorimetry (DSC) are used to show the differences in properties for ten different sets of process parameters. The mould and resin components temperature, the mass flow during the filling process and the residence time during the reaction process of the polyurethane are varied in several stages. Further experiments to determine the molar mass of the molecular chain between two crosslinking points of the polyurethane are used to explain the process influences on the thermomechanical properties. Thus, a direct correlation between manufacturing and material properties is shown. In addition, the mutual effect of the different parameters and their overall influence on the material behaviour is presented.
Long-Term Behavior of PBO FRCM and Comparison with Other Inorganic-Matrix Composites
Authors: Calabrese Angelo Savio , D’Antino Tommaso , Colombi Pierluigi , Poggi Carlo
Published: May 2022

Fabric-reinforced cementitious matrix (FRCM) composites, comprising high-strength fiber textiles embedded within inorganic matrices, represent an effective, cost-efficient, and low-invasive solution for strengthening and retrofitting existing masonry and reinforced concrete structures. Among different textiles employed in FRCM composites, polyparaphenylene benzo-bisoxazole (PBO) textiles are adopted due to their high tensile strength and good adhesion with the matrix. Although several experimental, numerical, and analytical works were performed to investigate the mechanical properties of PBO FRCM composites, limited information is available on their long-term behavior, as well as in the case of exposure to aggressive environments. This paper presents and discusses the results of a wide experimental campaign aimed at investigating the effect of different environmental conditions on the long-term tensile behavior of a PBO FRCM composite. Tests are performed using a clamping-grip tensile test set-up. The effect of various aggressive environments on the composite matrix cracking stress, composite tensile strength, ultimate strain, and fully cracked stage slope is investigated by comparing the results of nominally equal conditioned and unconditioned (control) specimens. These results are also compared with those of other FRCM composites comprising glass and carbon textiles subjected to the same conditionings, collected from the literature. The results show only limited reductions in the tensile properties, even after long exposure to aggressive environments.
Chasing Chances in a Changing Sea
Authors: Mollo Ernesto
Published: May 2022

Bioactive marine natural products (BMNPs) of interest for applications as drugs, antimicrobials, cosmetics, nutraceuticals, or antifoulants, are often present in traces in producer organisms and often occur in threatened or endangered species, or in organisms playing key ecological roles [...]
Microstructure and Performance of Antibiofouling Coatings on High-Strength Steel Substrates Immersed in the Marine Environment
Authors: Falara Pinelopi P. , Papadopoulos Nikolaos D. , Vourna Polyxeni
Published: May 2022

High-strength naval steel panels were coated by spraying with five commercial antifouling paints. The first set of coated specimens was subjected to electrochemical measurements and the anticorrosion properties of the paints were evaluated under controlled laboratory conditions. A second series of coated samples was statically exposed for nine months in thirteen different harbors, located in the Mediterranean Sea and the Atlantic Ocean, where their in-field antifouling efficiency was determined. The corrosion performance obtained by the electrochemical measurements provides predictions that best match the fouling protection observed at the field sites. The results indicate that the corrosion’s resistivity values determined in the laboratory were in a good agreement with the fouling level observed by examining the sea samples. The reported complementary data show that the laboratory measurements are representative of the sea-field conditions and confirm the originality of the proposed approach, which might lead to the development of innovative low-drag antifouling coatings for the hulls of ships, vessels, and speed crafts.
Diavik Waste Rock Project: Geostatistical Analysis of Sulfur, Carbon, and Hydraulic Conductivity Distribution in a Large-Scale Experimental Waste Rock Pile
Authors: Wilson David , Smith Leslie , Atherton Colleen , Smith Lianna J. D. , Amos Richard T. , Barsi David R. , Sego David C. , Blowes David W.
Published: May 2022

One of the large-scale field waste rock experiments (test piles) conducted as part of the Diavik Waste Rock Project was deconstructed, providing a spatially located set of geochemical, mineralogical, and particle-size distribution samples. Geostatistical analyses were conducted for sulfur and carbon content and saturated hydraulic conductivity, which affect the geochemical evolution of waste rock, to investigate the spatial dependence of these parameters. Analyses included population statistics, experimental semi-variogram estimation, and theoretical semi-variogram fitting. Population statistics were calculated for additional data sets from samples collected during the construction of the test piles. The population statistical analyses indicated that log-normal distribution provided the best fit for all investigated data sets. Experimental semi-variograms were estimated for the spatially located data set (test pile deconstruction) using the classical estimator, and theoretical semi-variograms were fitted. This investigation showed that the spatial distribution of sulfur, carbon, and hydraulic conductivity within the core of the test-pile experiments can be approximated using a log-normal distribution with a mean and standard deviation calculated using the samples collected during construction of the piles, and that little to no spatial relationship was present for these parameters at the scale of sampling. That the saturated hydraulic conductivity of the matrix material can be represented by the same statistical distribution throughout the test pile is significant because water flow, as well as mineral surface area and reactivity are dominantly controlled within the matrix portion of the test pile. Reactive transport simulations are included to demonstrate the influence of the matrix material on effluent geochemistry.
Wave Packet Approach to Adiabatic and Nonadiabatic Dynamics of Cold Inelastic Scatterings
Authors: Buren Bayaer , Chen Maodu
Published: May 2022

Due to the extremely large de Broglie wavelength of cold molecules, cold inelastic scattering is always characterized by the time-independent close-coupling (TICC) method. However, the TICC method is difficult to apply to collisions of large molecular systems. Here, we present a new strategy for characterizing cold inelastic scattering using wave packet (WP) method. In order to deal with the long de Broglie wavelength of cold molecules, the total wave function is divided into interaction, asymptotic and long-range regions (IALR). The three regions use different numbers of ro-vibrational basis functions, especially the long-range region, which uses only one function corresponding to the initial ro-vibrational state. Thus, a very large grid range can be used to characterize long de Broglie wavelengths in scattering coordinates. Due to its better numerical scaling law, the IALR-WP method has great potential in studying the inelastic scatterings of larger collision systems at cold and ultracold regimes.
Opportunities and Challenges in Targeting the Proofreading Activity of SARS-CoV-2 Polymerase Complex
Authors: Deval Jerome , Gurard-Levin Zachary A.
Published: May 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the COVID-19 pandemic. While the development of vaccines and the emergence of antiviral therapeutics is promising, alternative strategies to combat COVID-19 (and potential future pandemics) remain an unmet need. Coronaviruses feature a unique mechanism that may present opportunities for therapeutic intervention: the RNA polymerase complex of coronaviruses is distinct in its ability to proofread and remove mismatched nucleotides during genome replication and transcription. The proofreading activity has been linked to the exonuclease (ExoN) activity of non-structural protein 14 (NSP14). Here, we review the role of NSP14, and other NSPs, in SARS-CoV-2 replication and describe the assays that have been developed to assess the ExoN function. We also review the nucleoside analogs and non-nucleoside inhibitors known to interfere with the proofreading activity of NSP14. Although not yet validated, the potential use of non-nucleoside proofreading inhibitors in combination with chain-terminating nucleosides may be a promising avenue for the development of anti-CoV agents.
Magnesium—A Potential Key Player in Inflammatory Bowel Diseases?
Authors: Gilca-Blanariu Georgiana-Emmanuela , Trifan Anca , Ciocoiu Manuela , Popa Iolanda Valentina , Burlacu Alexandru , Balan Gheorghe G. , Olteanu Andrei Vasile , Stefanescu Gabriela
Published: May 2022

The altered magnesium status in inflammatory bowel disease (IBD) patients may have a significant clinical imprint considering its role in cell signaling and genomic stability, as well as its involvement in IBD patients’ fatigue. Our study pioneers the investigation of magnesium hair concentration patterns in an adult population of IBD patients. The hair magnesium concentration in IBD patients is compared to healthy controls in order to identify correlations between the magnesium status and relevant parameters related to disease activity, psychological status, and sleep quality. We report a significantly lower hair magnesium concentration within the IBD group compared to healthy controls (95%CI: 0.006–0.062; p = 0.017) and lower levels in CD compared to UC (95%CI: −0.061–−0.002; p = 0.038). We identified a borderline statistical significance between the hair magnesium concentration and UC disease activity (95%CI; −0.679–0.008; p = 0.055) and a significantly lower magnesium concentration in patients who reported increased sleep latency (95%CI −0.65–−0.102; p = 0.011) or decreased sleep duration (95%CI −0.613–−0.041; p = 0.028). Our results advance several hypotheses with substantial clinical impact to be confirmed in future studies. Magnesium levels appear to be modified in IBD patients, which suggests it either plays a primary role in disease pathophysiology or a is result of the disease’s evolution. Magnesium could be used in predictive models for clinical/subclinical disease activity. Moreover, magnesium supplementation may improve IBD evolution and sleep quality for patients with a deficit of this mineral. However, confirmatory evidence-based studies are needed to generate specific dosing, time of supplementation, and optimum monitoring of magnesium status in IBD patients.
Antiretroviral Drug-Resistance Mutations on the Gag Gene: Mutation Dynamics during Analytic Treatment Interruption among Individuals Experiencing Virologic Failure
Authors: Hunter James R. , dos Santos Domingos E. Matos , Munerato Patricia , Janini Luiz Mario , Castelo Adauto , Sucupira Maria Cecilia , Truong Hong-Ha M. , Diaz Ricardo Sobhie
Published: May 2022

We describe drug-resistance mutation dynamics of the gag gene among individuals under antiretroviral virologic failure who underwent analytical treatment interruption (ATI). These mutations occur in and around the cleavage sites that form the particles that become the mature HIV-1 virus. The study involved a 12-week interruption in antiretroviral therapy (ART) and sequencing of the gag gene in 38 individuals experiencing virologic failure and harboring triple-class resistant HIV strains. Regions of the gag gene surrounding the NC-p2 and p1-p6 cleavage sites were sequenced at baseline before ATI and after 12 weeks from plasma HIV RNA using population-based Sanger sequencing. Fourteen of the sixteen patients sequenced presented at least one mutation in the gag gene at baseline, with an average of 4.93 mutations per patient. All the mutations had reverted to the wild type by the end of the study. Mutations in the gag gene complement mutations in the pol gene to restore HIV fitness. Those mutations around cleavage sites and within substrates contribute to protease inhibitor resistance and difficulty in re-establishing effective virologic suppression. ART interruption in the presence of antiretroviral resistant HIV strains was used here as a practical measure for more adapted HIV profiles in the absence of ART selective pressure.
Transient Introgression of Wolbachia into Aedes aegypti Populations Does Not Elicit an Antibody Response to Wolbachia Surface Protein in Community Members
Authors: Lee Elvina , Hien Nguyen Tran , Yen Nguyen Thu , Nam Vu Sinh , Duong Tran Nhu , Trung Nghia Le , Mai Vien Quang , Dong Nguyen Thanh , Kriiger Loterio Robson , Iturbe-Ormaetxe Iñaki , Flores Heather A. , O’Neill Scott L. , Anh Dang Duc , Simmons Cameron P. , Fraser Johanna E.
Published: May 2022

Wolbachia is an endosymbiotic bacterium that can restrict the transmission of human pathogenic viruses by Aedes aegypti mosquitoes. Recent field trials have shown that dengue incidence is significantly reduced when Wolbachia is introgressed into the local Ae. aegypti population. Female Ae. aegypti are anautogenous and feed on human blood to produce viable eggs. Herein, we tested whether people who reside on Tri Nguyen Island (TNI), Vietnam developed antibodies to Wolbachia Surface Protein (WSP) following release of Wolbachia-infected Ae. aegypti, as a measure of exposure to Wolbachia. Paired blood samples were collected from 105 participants before and after mosquito releases and anti-WSP titres were measured by ELISA. We determined no change in anti-WSP titres after ~30 weeks of high levels of Wolbachia-Ae. aegypti on TNI. These data suggest that humans are not exposed to the major Wolbachia surface antigen, WSP, following introgression of Wolbachia-infected Ae. aegypti mosquitoes.
Spatial Surface Reflectance Retrievals for Visible/Shortwave Infrared Remote Sensing via Gaussian Process Priors
Authors: Zilber Daniel , Thompson David R. , Katzfuss Matthias , Natraj Vijay , Hobbs Jonathan , Braverman Amy
Published: May 2022

Remote Visible/Shortwave Infrared (VSWIR) imaging spectroscopy is a powerful tool for measuring the composition of Earth’s surface over wide areas. This compositional information is captured by the spectral surface reflectance, where distinct shapes and absorption features indicate the chemical, bio- and geophysical properties of the materials in the scene. Estimating this surface reflectance requires removing the influence of atmospheric distortions caused by water vapor and particles. Traditionally reflectance is estimated by considering one location at a time, disentangling atmospheric and surface effects independently at all locations in a scene. However, this approach does not take advantage of spatial correlations between contiguous pixels. We propose an extension to a common Bayesian approach, Optimal Estimation, by introducing atmospheric correlations into the multivariate Gaussian prior. We show how this approach can be implemented as a small change to the traditional estimation procedure, thus limiting the additional computational burden. We demonstrate a simple version of the technique using simulations and multiple airborne radiance data sets. Our results show that the predicted atmospheric fields are smoother and more realistic than independent inversions given the assumption of spatial correlation and may reduce bias in the surface reflectance retrievals compared to post-process smoothing.
Collision Localization and Classification on the End-Effector of a Cable-Driven Manipulator Applied to EV Auto-Charging Based on DCNN–SVM
Authors: Lin Haoyu , Quan Pengkun , Liang Zhuo , Lou Ya’nan , Wei Dongbo , Di Shichun
Published: May 2022

With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle–robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.
Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images
Authors: Yamanakkanavar Nagaraj , Choi Jae Young , Lee Bumshik
Published: May 2022

We propose an encoder–decoder architecture using wide and deep convolutional layers combined with different aggregation modules for the segmentation of medical images. Initially, we obtain a rich representation of features that span from low to high levels and from small to large scales by stacking multiple k × k kernels, where each k × k kernel operation is split into k × 1 and 1 × k convolutions. In addition, we introduce two feature-aggregation modules—multiscale feature aggregation (MFA) and hierarchical feature aggregation (HFA)—to better fuse information across end-to-end network layers. The MFA module progressively aggregates features and enriches feature representation, whereas the HFA module merges the features iteratively and hierarchically to learn richer combinations of the feature hierarchy. Furthermore, because residual connections are advantageous for assembling very deep networks, we employ an MFA-based long residual connections to avoid vanishing gradients along the aggregation paths. In addition, a guided block with multilevel convolution provides effective attention to the features that were copied from the encoder to the decoder to recover spatial information. Thus, the proposed method using feature-aggregation modules combined with a guided skip connection improves the segmentation accuracy, achieving a high similarity index for ground-truth segmentation maps. Experimental results indicate that the proposed model achieves a superior segmentation performance to that obtained by conventional methods for skin-lesion segmentation, with an average accuracy score of 0.97 on the ISIC-2018, PH2, and UFBA-UESC datasets.
Towards Personalization of Indoor Air Quality: Review of Sensing Requirements and Field Deployments
Authors: Xu Qian , Goh Hui Ci , Mousavi Ehsan , Nabizadeh Rafsanjani Hamed , Varghese Zubin , Pandit Yogesh , Ghahramani Ali
Published: May 2022

As humans spend more time indoors, ensuring acceptable indoor air quality (IAQ) through ubiquitous sensing systems has become a necessity. Although extensive studies have been conducted on the IAQ sensing systems, a holistic review of the performance and deployment of Ubiquitous IAQ Sensing (UIAQS) systems with associated requirements in IAQ sensing standards is still lacking. In this study, we first reviewed IAQ pollutants and other IAQ-related factors and the associated requirements in the prominent IAQ sensing standards. We found that while non-pollutant factors are influential on occupants’ perception of IAQ and their satisfaction, they do not have evaluation metrics in the IAQ standards. Then, we systematically reviewed field studies on UIAQS technologies in the literature. Specific classes of information were recorded and analyzed further. We found that the majority of the UIAQS systems did not meet the requirements of the prominent IAQ sensing standards and identified four primary research gaps. We concluded that a new holistic and personalized approach that incorporates UIAQS measurements and subjective feedback is needed. This study provides valuable insights for researchers and policymakers to better improve UIAQS technologies by developing personalized IAQ sensors and sensing standards.
Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification
Authors: Chee Kai Jye , Ramli Dzati Athiar
Published: May 2022

The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this research, we propose the sequence pair feature extractor, inspired by Bidirectional Encoder Representations from Transformers (BERT)’s sentence pair task, to obtain a dynamic representation of a pair of ECGs. We also propose using the self-attention mechanism of the transformer to draw an inter-identity relationship when performing ECG identification tasks. The model was trained once with datasets built from 10 ECG databases, and then, it was applied to six other ECG databases without retraining. We emphasize the significance of the time separation between enrollment and classification when presenting the results. The model scored 96.20%, 100.0%, 99.91%, 96.09%, 96.35%, and 98.10% identification accuracy on MIT-BIH Atrial Fibrillation Database (AFDB), Combined measurement of ECG, Breathing and Seismocardiograms (CEBSDB), MIT-BIH Normal Sinus Rhythm Database (NSRDB), MIT-BIH ST Change Database (STDB), ECG-ID Database (ECGIDDB), and PTB Diagnostic ECG Database (PTBDB), respectively, over a short time separation. The model scored 92.70% and 64.16% identification accuracy on ECGIDDB and PTBDB, respectively, over a long time separation, which is a significant improvement compared to state-of-the-art methods.
An Efficient Lightweight SAR Ship Target Detection Network with Improved Regression Loss Function and Enhanced Feature Information Expression
Authors: Yu Jimin , Wu Tao , Zhang Xin , Zhang Wei
Published: May 2022

It is difficult to identify the ship images obtained by a synthetic aperture radar (SAR) due to the influence of dense ships, complex background and small target size, so a deep learning-based target detection algorithm was introduced to obtain better detection performance. However, in order to achieve excellent performance, most of the current target detection algorithms focus on building deep and high-width neural networks, resulting in bloated network structure and reduced detection speed, which is not conducive to the practical application of target detection algorithms. Thereby, an efficient lightweight network Efficient-YOLO for ship detection in complex situations is proposed in the present work. Firstly, a new regression loss function ECIOU is proposed to enhance the detection boxes localization accuracy and model convergence speed. Secondly, We propose the SCUPA module to enhance the multiplexing of picture feature information and the model generalization performance. Thirdly, The GCHE module is proposed to strengthen the network’s ability to extract feature information. At last, the effectiveness of our method is tested on the specialized ship dataset: SSDD and HRSID datasets. The results show that Efficient-YOLO outperforms other state-of-the-art algorithms in accuracy, recall and detection speed, with smaller model complexity and model size.
Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
Authors: Kazi Abdul Karim , Khan Shariq Mahmood , Waseemullah Waseemullah , Farooq Umer , Hina Saman
Published: May 2022

Vehicular ad hoc network (VANET) is a specialized form of wireless network that is solely intended for collaboration between vehicles. Several studies have shown that standard routing protocols cannot be implemented in VANETs because of their unique characteristics such as their significant count of vehicles on the network and the rapid evolution of the network’s design. Because VANET communication links are broken very frequently, it is necessary to address the routing consistency of these highly dynamic networks. The transmission of VANET data may result in a substantial amount of overhead in the routing process; thus, it is vital to address the issue of overhead to enhance the overall network performance. The proposed protocol named compacted area with effective links (CAEL) is designed to focus on decreasing overhead to achieve an enhancement in PDR performance inside the network. The communication between selected nodes that have been judged to be dependable in terms of geographical location and appropriate existing links between vehicles is focused on achieving this goal. With the inclusion of the reliability factor, it is possible to complete the important step of removing extraneous nodes, with the selection of the trustworthy nodes being made based on the link expiration time during the whole routing procedure. When compared to our previously published protocols, i.e., Dynamic Trilateral Enrollment (DyTE) and Reliable Group of Vehicles (RGoV), the results of the simulations demonstrate that CAEL has achieved an overall improvement in the performance of the network.
A Novel Blockchain-Based Healthcare System Design and Performance Benchmarking on a Multi-Hosted Testbed
Authors: Pradhan Nihar Ranjan , Singh Akhilendra Pratap , Verma Sahil , Kavita  , Kaur Navneet , Roy Diptendu Sinha , Shafi Jana , Wozniak Marcin , Ijaz Muhammad Fazal
Published: May 2022

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.
Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System
Authors: Noh Byeongjoon , Park Hansaem , Lee Sungju , Nam Seung-Hee
Published: May 2022

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.
Validation of Running Gait Event Detection Algorithms in a Semi-Uncontrolled Environment
Authors: Donahue Seth R. , Hahn Michael E.
Published: May 2022

The development of lightweight portable sensors and algorithms for the identification of gait events at steady-state running speeds can be translated into the real-world environment. However, the output of these algorithms needs to be validated. The purpose of this study was to validate the identification of running gait events using data from Inertial Measurement Units (IMUs) in a semi-uncontrolled environment. Fifteen healthy runners were recruited for this study, with varied running experience and age. Force-sensing insoles measured normal foot-shoe forces and provided a standard for identification of gait events. Three IMUs were mounted to the participant, two bilaterally on the dorsal aspect of the foot and one clipped to the back of each participant’s waistband, approximating their sacrum. The identification of gait events from the foot-mounted IMU was more accurate than from the sacral-mounted IMU. At running speeds <3.57 m s−1, the sacral-mounted IMU identified contact duration as well as the foot-mounted IMU. However, at speeds >3.57 m s−1, the sacral-mounted IMU overestimated foot contact duration. This study demonstrates that at controlled paces over level ground, we can identify gait events and measure contact time across a range of running skill levels.
Multifunctional Modified Chitosan Biopolymers for Dual Applications in Biomedical and Industrial Field: Synthesis and Evaluation of Thermal, Chemical, Morphological, Structural, In Vitro Drug-Release Rate, Swelling and Metal Uptake Studies
Authors: Chopra Lalita , Chohan Jasgurpreet Singh , Sharma Shubham , Pelc Mariusz , Kawala-Sterniuk Aleksandra
Published: May 2022

The hydrogel materials are getting attention from the research due to their multidimensional usage in various fields. Chitosan is one of the most important hydrogels used in this regard. In this paper multifunctional binary graft copolymeric matrices of chitosan with monomer AA and various comonomers AAm and AN were prepared by performing free radical graft copolymerization in the presence of an initiator KPS. The binary grafting can be done at five different molar concentrations of binary comonomers at already optimized concentration of AA, KPS and other reaction conditions such as time, temperature, solvent amount, etc. Various optimum reaction conditions were investigated and presented in this work; the backbone as well as binary grafts Ch-graft-poly (AA-cop-AAm) and Ch-graft-poly (AA-cop-AN) were characterized via various physio-chemical techniques of analysis such as SEM analysis, Xray diffraction (XRD), TGA/DTA and FTIR. In the batch experiments, the binary grafts were investigated for the percent swelling with respect to pH (pH of 2.2, 7.0, 7.4 and 9.4) and time (contact time 1 to 24 h). Uploading and controllable in vitro release of the drug DS (anti-inflammatory) was examined with reverence to gastrointestinal pH and time. The binary grafts showed significantly better-controlled drug diffusion than the unmodified backbone. The kinetic study revealed that the diffusion of the drug occurred by the non-Fickian way. In the case of separation technologies, experiments (batch tests) were executed for the toxic bivalent metal ions Fe (II) and Pb (II) sorption from the aqueous media with respect to the parameters such as interaction period, concentration of fed metal ions in solution, pH and temperature. The binary grafted matrices showed superior results compared to chitosan. The kinetics study revealed that the matrices show pseudo-second order adsorption. The graft copolymer Ch-graft-poly (AA-cop-AAm) provided superior results in sustainable drug release as well as metal ion uptake. The study explored the potential of chitosan-based materials in the industry as well in the biomedical field. The results proved these to be excellent materials with a lot of potential as adsorbents.
Popularity-Aware Closeness Based Caching in NDN Edge Networks
Authors: Amadeo Marica , Campolo Claudia , Ruggeri Giuseppe , Molinaro Antonella
Published: May 2022

By enabling name-based routing and ubiquitous in-network caching, Named Data Networking (NDN) is a promising network architecture for sixth generation (6G) edge network infrastructures. However, the performance of content retrieval largely depends on the selected caching strategy, which is implemented in a distributed fashion by each NDN node. Previous research showed the effectiveness of caching decisions based on content popularity and network topology information. This paper presents a new distributed caching strategy for NDN edge networks based on a metric called popularity-aware closeness (PaC), which measures the proximity of the potential cacher to the majority of requesters of a certain content. After identifying the most popular contents, the strategy caches them in the available edge nodes that guarantee the higher PaC. Achieved simulation results show that the proposed strategy outperforms other benchmark schemes, in terms of reduced content retrieval delay and exchanged data traffic.
Iterative Learning-Based Negative Effect Compensation Control of Disturbance to Improve the Disturbance Isolation of System
Authors: Li Xiantao , Wang Lu , Xia Xianqi , Liu Yuzhang , Zhang Bao
Published: May 2022

At present, the cogging torque of permanent magnet synchronous motors (PMSM) seriously limits the Los pointing accuracy of aviation photoelectric stabilization platforms based on PMSM, which also restricts the requirements of ultra-long-distance and high-precision aviation reconnaissance and detection. For this problem, an off-line iterative learning control (ILC) was designed, and on this basis, a control method of negative effect compensation of disturbance (NECOD) is proposed. Firstly, the “dominant disturbance torque” in the system, that is, the cogging torque with the characteristics of position periodicity, was suppressed by off-line ILC according to different positions. Then, for the “residual disturbance” after compensation, NECOD was used to suppress it. In the constant speed scanning experiment of the aviation photoelectric stabilization platform, the method of combining the off-line iterative learning controller and the negative effect compensation of disturbance (NECOD + ILC) proposed in this paper significantly improved the Los control accuracy of the platform when compared with the classical active disturbance rejection control (ADRC) and ADRC + ILC methods, and the Los pointing error of the constant speed scanning process had only increased by less than 5% when the system had ±15% parameter perturbation. In addition, NECOD + ILC has fewer parameters and is easy to adjust, which is conducive to engineering application and promotion.
MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface
Authors: Guo Zexuan , Wang Chensheng , Yang Guang , Huang Zeyuan , Li Guo
Published: May 2022

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring.
Behavioral Change Prediction from Physiological Signals Using Deep Learned Features
Authors: Diraco Giovanni , Siciliano Pietro , Leone Alessandro
Published: May 2022

Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aimed to predict changes in one’s behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a temporal convolutional network, and the behavioral state was predicted through bidirectional long short-term memory auto-encoder, operating jointly. From the comparison with the state of the art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.