bluuebunny/crossref_metadata_2025 huggingface dataset:
TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments
Authors: Hettiarachchi Hansi , Al-Turkey Doaa , Adedoyin-Olowe Mariam , Bhogal Jagdev , Gaber Mohamed Medhat
Published: July 2022

Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of our knowledge, no available social media dataset covers continuous periods with event and sentiment labels together except for events or sentiments. Datasets without time gaps are huge due to high data generation and require extensive effort for manual labelling. Different approaches, ranging from unsupervised to supervised, have been proposed by previous research targeting such datasets. However, their generic nature mainly fails to capture event-specific sentiment expressions, making them inappropriate for labelling event sentiments. Filling this gap, we propose a novel data annotation approach in this paper involving several neural networks. Our approach outperforms the commonly used sentiment annotation models such as VADER and TextBlob. Also, it generates probability values for all sentiment categories besides providing a single category per tweet, supporting aggregated sentiment analyses. Using this approach, we annotate and release a dataset named TED-S, covering two diverse domains, sports and politics. TED-S has complete subsets of Twitter data streams with both sub-event and sentiment labels, providing the ability to support event sentiment-based research.
Nonlinear and Linear Measures in the Differentiation of Postural Control in Patients after Total Hip or Knee Replacement and Healthy Controls
Authors: Hadamus Anna , Błażkiewicz Michalina , Kowalska Aleksandra J. , Wydra Kamil T. , Grabowicz Marta , Łukowicz Małgorzata , Białoszewski Dariusz , Marczyński Wojciech
Published: July 2022

Primary osteoarthritis treatments such as a total hip (THR) or knee (TKR) replacement lead to postural control changes reinforced by age. Balance tests such as standing with eyes open (EO) or closed (EC) give a possibility to calculate both linear and nonlinear indicators. This study aimed to find the group of linear and/or nonlinear measures that can differentiate healthy people and patients with TKR or THR from each other. This study enrolled 49 THR patients, 53 TKR patients, and 16 healthy controls. The center of pressure (CoP) path length, sample entropy (SampEn), fractal dimension (FD), and the largest Lyapunov exponent (LyE) were calculated separately for AP and ML directions from standing with EO/EC. Cluster analysis did not result in correct allocation to the groups according to all variables. The discriminant model included LyE (ML-EO, ML-EC, AP-EC), FD (AP-EO, ML-EC, AP-EC), CoP-path AP-EC, and SampEn AP-EC. Regression analysis showed that all nonlinear variables depend on the group. The CoP path length is different only in THR patients. It was concluded that standing with EC is a better way to assess the amount of regularity of CoP movement and attention paid to maintain balance. Nonlinear measures better differentiate TKR and THR patients from healthy controls.
Mean Platelet Volume in the Diagnosis of Acute Appendicitis in the Pediatric Population: A Systematic Review and Meta-Analysis
Authors: Krishnan Nellai , Anand Sachit , Pakkasjärvi Niklas , Bajpai Minu , Dhua Anjan Kumar , Yadav Devendra Kumar
Published: July 2022

Background: Mean Platelet Volume (MPV) has been suggested as a biomarker for acute appendicitis (AA) in the adult population. The utility of MPV in pediatric AA remains vague. This systematic review and meta-analysis aimed to systematically summarize and compare all relevant data on MPV as a diagnostic biomarker for AA in children. Methods: Databases were systematically searched using keywords ((mean platelet volume) OR mpv) AND (appendicitis). The inclusion criteria were all comparative studies of MPV in children aged less than 18 years and diagnosed with AA. Two authors independently assessed the methodological quality using the Downs and Black scale. Results: We included fourteen studies in the final meta-analysis; most were retrospective. Eight studies compared the MPV values between AA and non-AA; four studies compared the same between AA and healthy controls; two studies compared the MPV values among all three groups. The estimated heterogeneity among the studies for all outcomes was high and statistically significant. The pooling the data showed no statistically significant difference in MPV (weighted mean difference (WMD) = −0.42, 95% CI = (−1.04, 0.20), p = 0.19) between AA and healthy controls or AA and non-AA cases (WMD = 0.01, 95% CI = (−0.15, 0.17), p = 0.90). Conclusion: While MPV levels seem to have some utility in adult AA diagnosis, MPV levels should not dictate treatment options in pediatric AA.
Comparison of Simple RNA Extraction Methods for Molecular Diagnosis of Hepatitis C Virus in Plasma
Authors: Hongjaisee Sayamon , Jabjainai Yosita , Sakset Suthasinee , Preechasuth Kanya , Ngo-Giang-Huong Nicole , Khamduang Woottichai
Published: July 2022

Nucleic acid extraction from biological samples is an important step for hepatitis C virus (HCV) diagnosis. However, such extractions are mostly based on silica-based column methodologies, which may limit their application for on-site diagnosis. A simple, rapid, and field-deployable method for RNA extraction is still needed. In this study, we evaluated the efficacy of four simple RNA extraction methods for the detection of HCV in plasma samples: a silica-membrane-based method, a magnetic-beads-based method, boiling with diethyl pyrocarbonate (DEPC)-treated distilled water, and using a commercial lysis buffer. HCV RNA was detected using both real-time reverse transcription polymerase chain reaction (RT-PCR) and reverse transcription loop-mediated isothermal amplification (RT-LAMP). Using real-time RT-PCR, extracted RNA from the silica-membrane-based and magnetic-beads-based methods had a 100% detection rate for RNA extraction from plasma. Using RT-LAMP, extracted RNA from the silica-membrane-based method showed a 66% detection rate, while the magnetic-beads-based method had a 62% detection rate. In summary, magnetic-beads-based extraction can be used as an alternative RNA extraction method for on-site HCV detection. Boiling with DEPC-treated distilled water was not appropriate for low HCV load samples, and boiling with a lysis buffer was not recommended.
Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy
Authors: Wichtmann Barbara D. , Albert Steffen , Zhao Wenzhao , Maurer Angelika , Rödel Claus , Hofheinz Ralf-Dieter , Hesser Jürgen , Zöllner Frank G. , Attenberger Ulrike I.
Published: July 2022

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.
A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction
Authors: Syed Asif Hassan , Khan Tabrej , Alromema Nashwan
Published: July 2022

The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku.
Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN
Authors: Wang Yaping , Wang Jinbao , Zhang Sheng , Xu Di , Ge Jianghua
Published: July 2022

Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) is proposed. The original signal of the rolling bearing was extracted and decomposed by resonance sparse decomposition to obtain the high-resonance and low-resonance components. The multiscale permutation entropy of the low-resonance component was calculated. Moreover, the locally linear-embedding algorithm was used for dimensionality reduction to remove redundant information. The multiscale convolution module was constructed to learn the feature information at different time scales. The attention module was used to fuse the feature information and input it into the remaining useful life prediction module for evaluation. The appropriate network structure and parameter configuration were determined, and a multiscale convolutional attention neural network was designed to determine the remaining useful life prediction model. The results show that the method demonstrates effectiveness and superiority in degrading the feature information representation and improving the remaining useful life prediction accuracy compared with other models.
Non-Linear Observer Design with Laguerre Polynomials
Authors: Trigka Maria , Dritsas Elias
Published: July 2022

In this paper, a methodology for a non-linear system state estimation is demonstrated, exploiting the input and parameter observability. For this purpose, the initial system is transformed into the canonical observability form, and the function that aggregates the non-linear dynamics of the system, which may be unknown or difficult to be computed, is approximated by a linear combination of Laguerre polynomials. Hence, the system identification translates into the estimation of the parameters involved in the linear combination in order for the system to be observable. For the validation of the elaborated observer, we consider a biological model from the literature, investigating whether it is practically possible to infer its states, taking into account the new coordinates to design the appropriate observer of the system states. Through simulations, we investigate the parameter settings under which the new observer can identify the state of the system. More specifically, as the parameter θ increases, the system converges more quickly to the steady-state, decreasing the respective distance from the system’s initial state. As for the first state, the estimation error is in the order of 10−2 for θ=15, and assuming c0={0,1},c1=1. Under the same conditions, the estimation error of the system’s second state is in the order of 10−1, setting a performance difference of 10−1 in relation to the first state. The outcomes show that the proposed observer’s performance can be further improved by selecting even higher values of θ. Hence, the system is observable through the measurement output.
Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels
Authors: Fanni Benigno Marco , Pizzuto Alessandra , Santoro Giuseppe , Celi Simona
Published: July 2022

Background: In the context of a growing demand for the use of in silico models to meet clinical requests, image-based methods play a crucial role. In this study, we present a parametric equation able to estimate the elasticity of vessel walls, non-invasively and indirectly, from information uniquely retrievable from imaging. Methods: A custom equation was iteratively refined and tuned from the simulations of a wide range of different vessel models, leading to the definition of an indirect method able to estimate the elastic modulus E of a vessel wall. To test the effectiveness of the predictive capability to infer the E value, two models with increasing complexity were used: a U-shaped vessel and a patient-specific aorta. Results: The original formulation was demonstrated to deviate from the ground truth, with a difference of 89.6%. However, the adoption of our proposed equation was found to significantly increase the reliability of the estimated E value for a vessel wall, with a mean percentage error of 9.3% with respect to the reference values. Conclusion: This study provides a strong basis for the definition of a method able to estimate local mechanical information of vessels from data easily retrievable from imaging, thus potentially increasing the reliability of in silico cardiovascular models.
Study on Fatigue Life Prediction and Acoustic Emission Characteristics of Sandstone Based on Mesoscopic Crack Propagation Mechanism
Authors: Si Kai , Cui Zhendong , Peng Ruidong , Zhao Leilei
Published: July 2022

Even when the maximum stress is less than the peak stress under conventional loading, fatigue failure of rock is likely to occur, thereby showing its unique characteristics. The present study summarized the factors affecting rock fatigue life from the perspective of phenomenology and studied the fatigue damage process of rock from the microscopic perspective. However, the meso-mechanical mechanism of fatigue–tension failure of rocks is still not very clear, and there are few studies on rock fatigue life that use meso-crack propagation models. In this paper, a mesoscopic model considering wing crack propagation is introduced to examine the fatigue failure of sandstone. A fatigue life prediction formula of sandstone was deduced via a combination with the Paris formula. This formula can quantitatively characterize the impact of upper limit stress and lower limit stress on the fatigue life of sandstone and explain the reason why upper limit stress has a greater influence on the fatigue process of sandstone. Such a prediction formula is applicable only under the condition of low confining pressures, which mainly cause tensile failure due to mesoscopic wing crack propagation. Acoustic emission signals during fatigue failure were monitored and then analyzed using a clustering method and a moment tensor inversion method. Therefore, the tensile or shear properties of mesoscopic failure could be distinguished according to acoustic emission characteristics in different stages of fatigue crack propagation. The results showed that crack sources causing sandstone fatigue failure are mainly tension-type when confining pressure is less than 10 MPa, which further verifies the proposed prediction model of sandstone fatigue life under low confining pressures.
Development and Wave Tank Demonstration of a Fully Controlled Permanent Magnet Drive for a Heaving Wave Energy Converter
Authors: Baker Nick J. , Almoraya Ahmed , Raihan Mohammad A. H. , McDonald Steve , McNabb Luke
Published: July 2022

One option for converting the energy in sea waves into renewable electricity is the development of floating wave energy converters coupled to electrical generators. For this to work, bespoke slow-speed electrical machines coupled to bidirectional power smoothing power electronic converters are required. This paper reports on the successful design and wave tank validation of an electric machine, power converter and fully controlled direct drive power take-off system coupled to two small scale heaving wave energy converters. The design, development and demonstration of linear generators and power converters is presented including some simulated and laboratory results. Demonstration of wave energy converters with pure electric drives, fully automated control, bidirectional power flow and active force management is almost unique and essential for future wave energy development. The results presented prove that direct-drive power take-off for wave energy devices is technically possible and can be used to implement an automated control system with bidirectional power flow in both resonant and non-resonant wave energy systems.
Energy Consumption Depending on the Durability of Pellets Formed from Sawdust with an Admixture of FFP2 Masks
Authors: Čajová Kantová Nikola , Belány Pavol , Holubčík Michal , Čaja Alexander
Published: July 2022

At present, we are still feeling the effects of the COVID-19 pandemic in connection with the huge amount of waste generated. However, the reuse of the produced waste in other processes requires energy consumption. This article deals with the reuse of face masks FFP2, which were added as an admixture to spruce or beech sawdust and then processed into pellets. During the production process of the pellets, energy consumption was measured and further converted to one ton of pellets, and also the consumption was reflected in the price of electricity. After storage, the mechanical durability and dimensions of the individual pellets were measured, and their density was calculated. Based on the results, it can be concluded that spruce pellets with 10% face masks FFP2 (consumption 747.41 kWh; durability 97.53%) and beech pellets with 5% face masks FFP2 (consumption 721.27 kWh; durability 97.38%) achieved higher values of mechanical durability and also consumed more energy than the remaining samples with lower values of durability without considering the sample with spruce sawdust and 5% FFP2 face masks (consumption 872.63 kWh; durability 91.68%). The production of spruce pellets with 5% FFP2 face masks was affected mainly by cold outside weather.
Experimental Studies of the Effect of Air Filter Pressure Drop on the Composition and Emission Changes of a Compression Ignition Internal Combustion Engine
Authors: Dziubak Tadeusz , Karczewski Mirosław
Published: July 2022

This paper presents an experimental evaluation of the effect of air filter pressure drop on the composition of exhaust gases and the operating parameters of a modern internal combustion Diesel engine. A literature analysis of the methods of reducing the emission of toxic components of exhaust gases from SI engines was conducted. It has been shown that the air filter pressure drop, increasing during the engine operation, causes a significant decrease in power output and an increase in fuel consumption, as well as smoke emission of Diesel engines with the classical injection system with a piston (sectional) in-line injection pump. It has also been shown, on the basis of a few literature studies, that the increase in the resistance of air filter flow causes a change in the composition of car combustion engines, with the effect of the air filter pressure drop on turbocharged engines being insignificant. A programme, and conditions of tests, on a dynamometer of a modern six-cylinder engine with displacement Vss = 15.8 dm3 and power rating 226 kW were prepared, regarding the influence of air filter pressure drop on the composition of exhaust gases and the parameters of its operation. For each technical state of the air filter, in the range of rotational speed n = 1000–2100 rpm, measurements of exhaust gas composition and emission were carried out, as well as measurements and calculations of engine-operating parameters, namely that of effective power. An increase in the pressure drop in the inlet system of a modern Diesel truck engine has no significant effect on the emissions of CO, CO2, HC and NOx to the atmosphere, nor does it cause significant changes in the degree of smoke opacity of exhaust gases in relation to its permissible value. An increase in air filter pressure drop from value Δpf = 0.580 kPa to Δpf = 2.024 kPa (by 1.66 kPa) causes a decrease in the maximum filling factor value from ηυ = 2.5 to ηυ = 2.39, that is by 4.5%, and a decrease in maximum power by 8.8%.
A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging
Authors: Zhang Pengfei , Xing Zuoxia , Guo Shanshan , Chen Mingyang , Zhao Qingqi
Published: July 2022

Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind speed, and it is usually measured according to relevant IEC standards, e.g., IEC 61400-12, which has problems such as long measurement duration and harsh conditions. This study proposes a WTG OP assessment method based on SCADA data by using the regression-kriging algorithm. The influences of wind shear, turbulence intensity, and air density on the WTG OP were analyzed. Two regression-kriging output power models were built based on SCADA data (i.e., SCADA2power model) and wind resource parameters from met mast (i.e., wind2power model). According to the evaluation of the simulation result, it was found that the results of the two models are basically consistent. Based on the evaluation of historical data under normal operating conditions, the goodness of fitting output power of the two models is 99.9%. This shows that the regression-kriging-based wind turbine power performance assessment method based on SCADA data has an accurate prediction and the potential of general application in WTG OP evaluation.
Daily Mortality in Different Age Groups Associated with Exposure to Particles, Nitrogen Dioxide and Ozone in Two Northern European Capitals: Stockholm and Tallinn
Authors: Olstrup Henrik , Åström Christofer , Orru Hans
Published: July 2022

Although the association between air pollution and mortality is well established, less is known about the effects in different age groups. This study analyzes the short-term associations between mortality in different age groups (0–14 years of age, 15–64 years of age, and 65+ years of age) and a number of air pollutants in two relatively clean northern European capitals: Stockholm and Tallinn. The concentrations in PM10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM2.5–10 (coarse particles), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), PNC4 (particle number count of particles larger than or equal to 4 nm), NO2 (nitrogen dioxide), and O3 (ozone) were measured during the period of 2000–2016 in Stockholm and 2001–2018 in Tallinn (except for BC and PNC4 which were only measured in Stockholm). The excess risks in daily mortality associated with an interquartile range (IQR) increase in the measured air pollutants were calculated in both single- and multi-pollutant models for lag01 and lag02 (average concentration during the same and the previous day, and the same and the previous two days, respectively) using a quasi-Poisson regression model with a logistic link function. In general, the calculated excess risks per IQR increase were highest in the age group 0–14 years of age in both Stockholm and Tallinn. However, in Stockholm, a statistically significant effect was shown for PM2.5–10, and in Tallinn for O3. In the oldest age group (65+), statistically significant effects were shown for both PM2.5–10, PM10, and O3 in Stockholm, and for O3 in Tallinn.
Effects of the Source–Sink Relationship on Walnut Nut Quality at the Scale of the Fruit–Bearing Branch
Authors: Zhang Shubin , Chen Hong , Pan Cunde
Published: July 2022

Fruit quality is known to be regulated by the balance between leaf number/area and fruit number, but less is known about the effects of fruit–bearing branch agronomic traits, particularly for walnuts (Juglans regia L.). We assessed nut quality, physiological and biochemical indexes of leaves, and microstructure of leaves and fruit stalks at various leaf–to–fruit ratios to gain insight into the relationships between branch agronomic traits and nut quality, to identify those traits that made a better contribution to nut quality and to find out the range of the leaf–to–fruit ratio and the object of fruit to be removed for thinning. We found that the top fruit on the fruit stalk had a higher longitudinal diameter and kernel weight than the bottom at the low leaf–to–fruit level, and branches with more pinnate compound leaves had a better capacity for carbohydrate assimilation and transportation to produce better quality fruits. Specifically, with the increasing leaf number, the branch diameter, total leaf area, net photosynthetic rate, chlorophyll content, fruit weight, fruit diameters, and kernel protein content also increased. Moreover, at the microscopic level, the fruit stalk vascular bundle, leaf thickness, palisade mesophyll thickness, and ratios of palisade mesophyll to spongy mesophyll thickness in the leaf also showed the same trend. Therefore, when the ratio of leaf area to the fruit number was less than 181.5 square centimeters per fruit on the branches, reducing the fruit number could improve the size and the crude fat content of fruits. While the ratio was more than 247.8 square centimeters per fruit, the fruit number had no significant effect on fruit quality, but increasing leaf area and branch diameter could improve the fruit size and yield.
Evaluation of the Marine Bacterial Population in the Great Bitter Lake, Egypt, as a Source of Antimicrobial Secondary Metabolites
Authors: Sedeek Abdelrahman M. , Ismail Maha M. , Elsayed Tarek R. , Ramadan Mohamed A.
Published: July 2022

The ecological uniqueness of the Great Bitter Lake ecosystem makes its bacterial population interesting for investigation. Here, we present the first trial to evaluate the biosynthetic capacity of the bacterial population at the lake as a source of novel antimicrobials. We collected different samples from various locations throughout the lake including the oxic sediment, anoxic sediment, shore water, and off-shore water. We modified a molecular approach to compare and choose the samples with the highest bacterial biosynthetic capacity by quantifying the polyketide synthase gene clusters in their total community DNA. Furthermore, we screened the bacterial isolates recovered from these samples and their metabolic extracts for antimicrobial activity. We tried to tentatively investigate the identity of the active metabolites by PCR screening and LC–MS. The bacterial population in the oxic sediment had the highest biosynthetic capacity compared to other sample types. Four active Bacillus isolates were identified. The isolated Bacillus species were expected to produce numerous probable bioactive metabolites encoded by biosynthetic gene clusters related to the polyketide synthases (either individual or hybrid with non-ribosomal peptide synthetase), such as Bacillomycin D, Iturin A, Bacilosarcin B, Bacillcoumacin G and Macrolactin (N and G). These results suggest that the under-explored bacterial community of the Great Bitter Lake has a prospective biosynthetic capacity and can be a promising source for novel antibiotics.
A Public Infrastructure for a Trusted Wireless World
Authors: Carnley Renee , Bagui Sikha
Published: July 2022

The novelty of this work lies in examining how 5G, blockchain-based public key infrastructure (PKI), near field communication (NFC), and zero trust architecture securely provide not only a trusted digital identity for telework but also a trusted digital identity for secure online voting. The paper goes on to discuss how blockchain-based PKI, NFC, and the cloud provide a roadmap for how industry and governments can update existing frameworks to obtain a trusted digital identity in cyberspace that would provide secure telework and online voting capabilities.
Effects of Boiling Processing on Texture of Scallop Adductor Muscle and Its Mechanism
Authors: Wu Zi-Xuan , Fan Ying-Chen , Guo Chao , Liu Yu-Xin , Li De-Yang , Jiang Peng-Fei , Qin Lei , Bai Yan-Hong , Zhou Da-Yong
Published: July 2022

The objective of this study was to reveal the effects of boiling processing on the texture of scallop adductor muscle (SAM) and its mechanism. Compared to the fresh sample, all the texture indicators, including the hardness, chewiness, springiness, resilience, cohesiveness, and shear force of 30-s- and 3-min-boiled SAMs increased time-dependently (p < 0.05). As the boiling time increased further to 15 min, the shear force and cohesiveness still increased significantly (p < 0.05), and the resilience and hardness were maintained (p > 0.05), but the springiness and chewiness decreased significantly (p < 0.05). The overall increase in the texture indicators of the boiled SAMs was due to the boiling-induced protein denaturation, aggregation, and increased hydrophobicity, resulting in the longitudinal contraction and lateral expansion of myofibrils, the longitudinal contraction and lateral cross-linked aggregation of muscle fibers, and the loss of free water. However, the decreasing springiness and chewiness of the 15-min-boiled SAMs was due to the significant degradation of proteins (especially collagen), resulting in the destruction of the connective tissue between the muscle fiber clusters. Both from a subjective sensory point of view and from the objective point of view of protein denaturation and degradation, 3-min-boiled SAMs are recommended. The quality improvement of thermally processed products by controlled, moderate cooking is of practical value from the perspective of food consumption.
Alleviation of Metabolic Disturbance by Substituting Kanjang High in Bacillus for Salt through Modulation of Gut Microbiota in Estrogen-Deficient Rats
Authors: Park Sunmin , Zhang Ting , Yue Yu , Jeong Su-Ji , Ryu Myeong-Seon , Wu Xuangao , Yang Hee-Jong , Jeong Do-Yeon
Published: July 2022

A high salt intake may exacerbate menopausal symptoms and substituting for different types of traditionally made kanjang (TMK; soy sauce) may prevent it. This study examined whether substituting salt with lyophilized TMK containing low and high Bacillus and biogenic amines in a high-fat diet might modulate the menopausal symptoms and the energy, glucose, and lipid metabolism in ovariectomized (OVX) rats. They were categorized into salt (Control), TMK with high Bacillus and low biogenic amines (HBLB), TMK with high Bacillus and high biogenic amines (HBHB), TMK with low Bacillus and low biogenic amines (LBLB), and TMK with low Bacillus and high biogenic amines (LBHB). Sham-operated rats consumed the same diet as the Control. HBLB, HBHB, and LBHB prevented increased tail skin temperature compared to the Control. HBHB and HBLB partially inhibited the increased weight gain and abdominal fat mass by reducing the food efficiency without changing the serum 17β-estradiol concentrations. Serum glucose and insulin concentrations and the insulin resistance index by the homeostatic model assessment for insulin resistance showed a positive association for weight gain. HBLB and HBHB decreased the serum malondialdehyde and tumor-necrosis factor-α levels. Hepatic triglyceride storage was lower in all TMK groups than in the Control, while hepatic glycogen accumulation was higher in the HBLB, HBHB, and LBHB groups than in the Control and LBLB groups. Accordingly, the mRNA expression of peroxisome proliferator-activated receptors-γ(PPAR-γ) was higher in the HBLB and HBHB groups compared to the Control, and that of fatty acid synthase was opposite to PPAR-γ expression. However, HBLB and HBHB improved dyslipidemia and insulin resistance compared to the Control, but their improvement did not reach that of the Normal-control. The acetic acid concentrations in the portal vein were lower in the LBLB than in the Control, while the butyric acid contents were higher in the LBHB and HBLB groups than in the Control. HBHB, HBLB, and LBHB elevated Akkermansia and Lactobacillus, and HBLB and LBLB increased Bacteroides and Ruminococcus compared to the Control. Polycyclic aromatic hydrocarbon degradation, bile acid synthesis, and unsaturated fatty acid biosynthesis were significantly higher in the HBLB group than in the Control group. In conclusion, substituting salts to TMK with a high Bacillus content regardless of the bioamine contents partially improved the menopausal symptoms and metabolic disturbance in estrogen-deficient animals.
An Integrative Glycomic Approach for Quantitative Meat Species Profiling
Authors: Chia Sean , Teo Gavin , Tay Shi Jie , Loo Larry Sai Weng , Wan Corrine , Sim Lyn Chiin , Yu Hanry , Walsh Ian , Pang Kuin Tian
Published: July 2022

It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.
Sign Retention in Classical MF-DFA
Authors: Yang Mengdie , Zhang Yudong , Wang Jian
Published: July 2022

In this paper, we propose a one-dimensional (1D) multifractal sign retention detrending fluctuation analysis algorithm (MF-S-DFA). The proposed method is based on conventional multifractal detrending fluctuation analysis (MF-DFA). As negative values may exist in the calculation in the original MF-DFA model, sign retention is considered to improve performance. We evaluate the two methods based on time series constructed by p-model multiplication cascades. The results indicate that the generalized Hurst exponent H(q), the scale exponent τ(q) and the singular spectrum f(α) estimated by MF-S-DFA behave almost consistently with the theoretical values. Moreover, we also employ distance functions such as DH and Dτ. The results prove that MF-S-DFA achieves more accurate estimation. In addition, we present various numerical experiments by transforming parameters such as nmax, q and p. The results imply that MF-S-DFA obtains more excellent performance than that of conventional MF-DFA in all cases. Finally, we also verify the high feasibility of MF-S-DFA in ECG signal classification. Through classification of normal and abnormal ECG signals, we further corroborate that MF-S-DFA is more effective than conventional MF-DFA.
On the Finite-Time Boundedness and Finite-Time Stability of Caputo-Type Fractional Order Neural Networks with Time Delay and Uncertain Terms
Authors: Priya Bandana , Thakur Ganesh Kumar , Ali M. Syed , Stamov Gani , Stamova Ivanka , Sharma Pawan Kumar
Published: July 2022

This study investigates the problem of finite-time boundedness of a class of neural networks of Caputo fractional order with time delay and uncertain terms. New sufficient conditions are established by constructing suitable Lyapunov functionals to ensure that the addressed fractional-order uncertain neural networks are finite-time stable. Criteria for finite-time boundedness of the considered fractional-order uncertain models are also achieved. The obtained results are based on a newly developed property of Caputo fractional derivatives, properties of Mittag–Leffler functions and Laplace transforms. In addition, examples are developed to manifest the usefulness of our theoretical results.
Monitoring Hydrothermal Activity Using Major and Trace Elements in Low-Temperature Fumarolic Condensates: The Case of La Soufriere de Guadeloupe Volcano
Authors: Inostroza Manuel , Moune Séverine , Moretti Roberto , Robert Vincent , Bonifacie Magali , Chilin-Eusebe Elodie , Burtin Arnaud , Burckel Pierre
Published: July 2022

At the hydrothermal stage, volcanoes are affected by internal and external processes that control their fumarolic and eruptive activity. Monitoring hydrothermal activity is challenging given the diverse nature of the processes accounting for deeper magmatic and shallow hydrothermal sources. A better understanding of these processes has commonly been achieved by combining geochemical and geophysical techniques. However, existing geochemical techniques only include the surveillance of major gas components in fumarolic emissions or major ions in cold/thermal springs. This work presents a long-term (2017–2021) surveillance of major and trace elements in fumarolic condensates from the Cratère Sud vent, a low-temperature steam-rich emission from the La Soufriere de Guadeloupe volcano. This fumarole presented a fluctuating performance, offering a unique opportunity to reveal the behavior of major and trace elements, as well as the physicochemical processes affecting magmatic and hydrothermal sources. Time-series analyses allowed for the identification of pH-related chemical fluctuations associated with (1) variable inputs of deep magmatic components at the root of the hydrothermal system, (2) pressurization episodes of the hydrothermal system with increasing fluid–rock interaction, acid gas scrubbing, and vapor scavenging of metals, and (3) the decreased hydrothermal activity, decreasing scrubbing efficiency. Variations in the volatile content (e.g., S, Sb, B, Cl, Bi, Zn, Mo, Br, Cd, Ag, Cu, and Pb), the amount of leached rock-related elements (e.g., Na, Mg, Al, Si, P, K, Ca, Ti, Cr, Mn, Fe, Rb, Sr, Y, Cs, Ba, REEs, and U), and variations in the concentration of Cl and S alone, are postulated as key parameters to monitor volcanic–hydrothermal systems in unrest, such as La Soufriere. Our results demonstrate that monitoring using condensates is a useful geochemical technique, complementing conventional methods, such as “Giggenbach” soda flasks or the so-called Multigas.
The Memory of a Fault Gouge: An Example from the Simplon Fault Zone (Central Alps)
Authors: Argante Valentina , Tanner David Colin , Brandes Christian , von Hagke Christoph , Tsukamoto Sumiko
Published: July 2022

Faut gouge forms at the core of the fault as the result of a slip in the upper brittle crust. Therefore, the deformation mechanisms and conditions under which the fault gouge was formed can document the stages of fault movement in the crust. We carried out a microstructural analysis on a fault gouge from a hanging-wall branch fault of the Simplon Fault Zone, a major low-angle normal fault in the European Alps. We use thin-section analysis, together with backscattered electron imaging and X-ray diffractometry (XRD), to show that a multistage history from ductile to brittle deformation within the fault gouge. We argue that this multistage deformation history is the result of continuous exhumation history from high to low temperature, along the Simplon Fault Zone. Because of the predominance of pressure solution and veining, we associated a large part of the deformation in the fault gouge with viscous-frictional behaviour that occurred at the brittle-ductile transition. Phyllosilicates and graphite likely caused fault lubrication that we suggested played a role in localizing slip along this major low-angle normal fault.
Marital Satisfaction and Perceived Family Support in Families of Children with Autistic Spectrum Disorder: Dyadic Analysis
Authors: He Bijing , Wongpakaran Tinakon , Wongpakaran Nahathai , Wedding Danny
Published: July 2022

Background: Raising children with autism spectrum disorder (ASD) causes tremendous stress for parents that may lead to marital conflict and relationship dissatisfaction. Many factors are associated with parent relationships including severity of autistic behaviors and social support. This study aimed to investigate whether severity of autistic behaviors, perceived family support, and complementarity of interpersonal styles between husbands and wives predicted couple satisfaction among the parents of children with ASD. Method: Seven hundred ninety-seven parent dyads of children aged 7–14 years old with ASD participated in the study. Measurements used included couple satisfaction index, perceived family support using the Multidimensional Scale of Perceived Social Support, inter-personal style using the inventory of interpersonal problems, the ABC autism checklists as well as sociodemographic and related factors. The Actor Partner Interdependence Model estimated by multilevel modeling was used for analysis. Results: Perceived family support was relevant in married couples regarding their marital relationship, but the effects on husbands and wives differed. Husbands’ relationship satisfaction was predicted by how they perceived being supported by family. The severity of autistic behaviors predicted relationship satisfaction but only actor effect. Negative prediction of interpersonal complementarity on couple satisfaction was observed. In addition, time spent on raising children had a negative impact on the quality of the relationship. Partner effect of time spent was observed among women. Conclusion: Dyadic analysis using an actor–partner independence model confirmed perception of family support predicts relationship satisfaction among parents of children with ASD in addition to the severity of autistic behaviors and time spent caring for children. Complementarity of individual interpersonal style had no effect on couple satisfaction. This research suggests implications for interventions regarding building skills that elicit support from family members.
Analysis of Patient Satisfaction through the Effect of Healthcare Spending on Waiting Times for Consultations and Operations
Authors: Santos-Jaén José Manuel , Valls Martínez María del Carmen , Palacios-Manzano Mercedes , Grasso Mayra Soledad
Published: July 2022

In recent years, public authorities have invested large amounts of public money in trying to reduce waiting times for consultations and operations with the aim of improving the quality of the healthcare system. Our research aims to analyze the effect of these investments on patient satisfaction through the mediating relationship of waiting times for consultations and operations, as well as from a gender perspective. By studying a series of key indicators of the Spanish healthcare system and applying partial least squares structural equation modelling (PLS-SEM), the findings show that the model explains 12.10% of the variance in consultant waiting times, 51.90% in operation waiting times, and 27.00% in patient satisfaction. We found that increased public spending leads to increased patient satisfaction by reducing waiting times. However, no gender-based differences were found. The results provide exciting implications for theory and practice, indicating how policymakers can orient their strategies towards improving patient satisfaction.
Self-Assessed Aspects of Health 3 Months after COVID-19 Hospitalization—A Swedish Cross-Sectional Study
Authors: Larsson Alexandra C. , Engwall Marie , Palstam Annie , Persson Hanna C.
Published: July 2022

It is not yet fully understood how the patients self-assess their overall health in the early recovery after COVID-19 and if certain patient groups are more prominent in perceived long-time effects of COVID-19. The aim of this study was to describe self-assessed aspects of health in body function, activity and participation 3 months after hospitalization due to COVID-19 and identify difference between groups depending in age, sex and level of hospital care. This cross-sectional study consists of self-assessed aspects of health and recovery in 168 participants (mean age 64 years old, 69% men) previously hospitalized patients due to COVID-19. We have previously published data, from hospital discharge, on this cohort were predominantly the older patients and previous ICU-treated participants were affected. In this study there were differences in between groups. Of the study population 72% perceived fatigue, 64% respiratory difficulties, 37% perceived symptoms of anxiety. Three-months after COVID-19 this cohort was overall still affected. The recovery process is multifaced and the cohort heterogeneous, hence the rehabilitation needs to be highly individualized, and the follow-up of this patient group is of importance regardless of age, sex and previous level of hospital care.