bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study of the Thermal Enhancement for Spacecraft PCM Thermal Energy Storage Units
Authors: Wang Shisong , Hou Xu , Yin Jianbao , Xing Yuming , Wang Zixian
Published: November 2022

To access the enhancement effect of the topology optimization and porous foam structure, numerical studies were conducted to investigate the heat conduction enhancement (by metal foam, graphite foam, topologically optimized fins, and combinations of metal foam and topologically optimized fins) of phase change material (PCM (n-octadecane)) based tubular thermal energy storage unit for spacecraft. The results showed that metal foam performed better than topologically optimized fins and a combination of metal foam and topology optimized fins, of which conductive material, unit mass, and volume fraction of PCM were the same. Graphite foam (140 W/(m·K)) had the best heat transfer enhancing effect, making PCM melt much faster than other enhancing methods investigated. A multi-criteria decision-making (MCDM) method integrated with the combined weight and TOPSIS method was introduced to evaluate the preferred alternatives’ performance based on the energy storage time, equivalent density, and energy storage. The evaluation pointed out that 3% topologically optimized aluminum fins with 98% copper foam had the best comprehensive performance. This study guided the optimal design of latent heat thermal energy storage units for spacecraft under microgravity.
Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security
Authors: Staugaitis Algirdas Justinas , Vaznonis Bernardas
Published: November 2022

Global commodity markets, due to major health crises, political tension, sanctions, growing demand, and other global supply and demand factors, are currently particularly unstable. In addition to the macro-environmental factors that drive the prices, agricultural and other commodity markets are becoming more susceptible to the continuously-growing speculation on major commodity exchanges. Therefore, the purpose of this study is to analyze the influence of financial speculation on agricultural and other commodity prices and return volatility. In our study, we use daily returns on wheat, soybean, corn, and oats futures from the Chicago Mercantile Exchange as well as two additional commodities (crude oil and gold) to compare the extent of this effect. To measure this impact, we, besides traditional tools for time-series analysis, apply the threshold autoregressive conditional heteroskedasticity (TGARCH) technique. We also provide a model using dummy variables for the season to determine whether or not financial speculation’s impact on return volatility differs among seasons, as seasonality plays an important role in return dynamics for agriculture. Our study’s findings show that financial speculation, except for the oats market, either has no impact or makes the underlying futures returns less volatile. Therefore, we draw the conclusion that either there is no relationship between the rise in short-run speculation and the volatility of agricultural commodity prices, or the link is at best questionable. Research results provide important implications for the sustainable development of commodity markets, as passive legislation measurers can be seen as more effective ones compared to more strict active ones in order to maintain these markets liquid and capable of distributing price risks for agricultural producers and manufacturers in a challenging economic and geopolitical environment.
Attitudes towards Use of High-Importance Antimicrobials—A Cross-Sectional Study of Australian Veterinarians
Authors: Sri Anna , Bailey Kirsten E. , Gilkerson James R. , Browning Glenn F. , Hardefeldt Laura Y.
Published: November 2022

The timely implementation of antimicrobial stewardship interventions could delay or prevent the development of higher levels of antimicrobial resistance in the future. In food-producing animals in Australia, high-importance antimicrobials, as rated by the Australian Strategic and Technical Advisory Group (ASTAG), include virginiamycin and third-generation cephalosporins (in individual pigs or cattle). The use of high-importance antimicrobials in companion animals is more widespread and less regulated. There is no national antimicrobial use surveillance system for animals in Australia. Consequently, there is a gap in the knowledge about reasonable use across all sectors of veterinary practice. This study explored attitudes towards the use in veterinary medicine of antimicrobials with high importance to human health, and determined levels of agreement about the introduction of restrictions or other conditions on this use. An online survey was distributed via social media and email from June to December 2020 to veterinarians working in Australia. Of the 278 respondents working in clinical practice, 49% had heard of the ASTAG rating system, and 22% used a traffic light system for antimicrobial importance in their practice. Overall, 61% of participants disagreed that veterinarians should be able to prescribe high-importance antimicrobials without restrictions. If there were to be restrictions, there was most agreement amongst all respondents for only restricting high-importance antimicrobials (73%). There is a need for education, guidance, and practical support for veterinarians for prescribing high-importance antimicrobials alongside any restrictions.
Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance
Authors: Yasir Muhammad , Karim Asad Mustafa , Malik Sumera Kausar , Bajaffer Amal A. , Azhar Esam I.
Published: November 2022

Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers.
Effectiveness of the Fungal Metabolite 3-O-Methylfunicone towards Canine Coronavirus in a Canine Fibrosarcoma Cell Line (A72)
Authors: Cerracchio Claudia , Iovane Valentina , Salvatore Maria Michela , Amoroso Maria Grazia , Dakroub Hiba , DellaGreca Marina , Nicoletti Rosario , Andolfi Anna , Fiorito Filomena
Published: November 2022

Canine coronavirus (CCoV), an alphacoronavirus, may cause self-limiting enteric disease in dogs, especially in puppies. The noteworthy plasticity of coronaviruses (CoVs) occurs through mutation and recombination processes, which sometimes generate new dangerous variants. The ongoing SARS-CoV-2 pandemic and the isolation of a novel canine–feline recombinant alphacoronavirus from humans emphasizes the cross-species transmission ability of CoVs. In this context, exploring antiviral compounds is essential to find new tools for fighting against CoVs infections. Fungi produce secondary metabolites, which are often developed as antibiotics, fungicides, hormones, and plant growth regulators. Previous examinations of benzo-γ-pyrone 3-O-methylfunicone (OMF), obtained from Talaromyces pinophilus, showed that it reduces the infectivity of hepatitis C virus and bovine herpesvirus 1. Based on this evidence, this study evaluated the antiviral ability of OMF against CCoV infection in a canine fibrosarcoma (A72) cell line. During CCoV infection, a non-toxic dose of OMF markedly increased features of cell viability. Moreover, OMF induced a significant reduction in virus yield in the presence of an intense downregulation of the viral nucleocapsid protein (NP). These findings occurred in the presence of a marked reduction in the aryl hydrocarbon receptor (AhR) expression. Taken together, preliminary findings suggest that OMF inhibiting AhR shows promising activity against CCoV infection.
Customer Complaints as an Evaluation Tool Assessing the Performance and Clinical Suitability of Different Implant Design
Authors: Ihde Stefan , Pałka Łukasz
Published: November 2022

During an assessment of dental implant design effectiveness, most of the data are acquired from clinical or animal studies performed by researchers and clinicians. In order to represent scientific significance, those studies have to be properly designed, focusing on a precisely chosen aspect with inclusion and exclusion criteria. The last of these, especially, can create major limitations for the data collection. In order to create a reliable, complete overview of different implant suitability, the data from a single customer can also be a valuable input in dental science. The aim of the study was to compare if the type of implants returned corresponded with sales figures for this type of implant or if there were any differences in the number of complaints. Customer complaints regarding dental implants produced by the same manufacturer acquired from dental professionals from different countries were evaluated over a fixed period of time. After having compared the number of complaints regarding implants to the number of sold implants, it was evident that single-piece implants and those with a polished surface used for immediate loading were significantly less likely to generate customer complaints than conventional two-stage systems (p < 0.001) with rough surfaces and larger endosseous diameters.
A Novel Filter-Level Deep Convolutional Neural Network Pruning Method Based on Deep Reinforcement Learning
Authors: Feng Yihao , Huang Chao , Wang Long , Luo Xiong , Li Qingwen
Published: November 2022

Deep neural networks (DNNs) have achieved great success in the field of computer vision. The high requirements for memory and storage by DNNs make it difficult to apply them to mobile or embedded devices. Therefore, compression and structure optimization of deep neural networks have become a hot research topic. To eliminate redundant structures in deep convolutional neural networks (DCNNs), we propose an efficient filter pruning framework via deep reinforcement learning (DRL). The proposed framework is based on a deep deterministic policy gradient (DDPG) algorithm for filter pruning rate optimization. The main features of the proposed framework are as follows: (1) AA tailored reward function considering both accuracy and complexity of DCNN is proposed for the training of DDPG and (2) a novel filter sorting criterion based on Taylor expansion is developed for filter pruning selection. To illustrate the effectiveness of the proposed framework, extensive comparative studies on large public datasets and well-recognized DCNNs are conducted. The experimental results demonstrate that the Taylor-expansion-based filter sorting criterion is much better than the widely used minimum-weight-based criterion. More importantly, the proposed filter pruning framework can achieve over 10× parameter compression and 3× floating point operations (FLOPs) reduction while maintaining similar accuracy to the original network. The performance of the proposed framework is promising compared with state-of-the-art DRL-based filter pruning methods.
Study of Behaviour of Short Concrete Columns Confined with PVC Tube under Uniaxial Load
Authors: Hammadi Ammar A. , Khaleel Faidhalrahman , Afan Haitham Abdulmohsin , Khan Md Munir Hayet , Sulaibi Ayad Abdul Hammed
Published: November 2022

An experimental investigation has been carried out to evaluate the effectiveness of Polyvinyl chloride (PVC) confinements in short plain circular concrete columns. The experimental part is conducted using different PVC tube diameters (110, 160, 220, and 250 mm) with two types of confinement strategies (fully and confined with the cut ends). The results are validated with unconfined samples (control samples). The test results showed that using external confinement of concrete columns by PVC tubes enhances the ultimate load capacity of the short columns. For fully confined samples, the enhancement ratio ranges between 5% and 8.3%, and from 4.16% to 15% for samples with cut ends. Furthermore, the confining of PVC pipes with the cut ends (CCC) has a more considerable effect on load capacity for all diameters except the ones with 250 mm, where the samples with full confinement (Cc) carried a bigger load than those with cutting ends. Finally, a numerical simulation of samples is carried out by the finite element (FE) model using the ABAQUS software. For all scenarios, the results of the numerical analysis showed considerable similarity to the experimental results, with R2 of 0.95 indicating the high linearity between the actual and simulated compressive strength values. Moreover, the FE induces fewer simulated errors with a relative error (RE) ranging from 0.16% to 6% for all scenarios.
Deep Learning Based Urban Building Coverage Ratio Estimation Focusing on Rapid Urbanization Areas
Authors: Le Quang Hoai , Shin Hyunkyu , Kwon Nahyun , Ho Jongnam , Ahn Yonghan
Published: November 2022

Urban parameters, such as building density and the building coverage ratio (BCR), play a crucial role in urban analysis and measurement. Although several approaches have been proposed for BCR estimations, a quick and effective tool is still required due to the limitations of statistical-based and manual mapping methods. Since a building footprint is crucial for the BCR calculation, we hypothesize that Deep Learning (DL) models can aid in the BCR computation, due to their proven automatic building footprint extraction capability. Thus, this study applies the DL framework in the ArcGIS software to the BCR calculation task and evaluates its efficiency for a new industrial district in South Korea. Although the accuracy achieved was limited due to poor-quality input data and issues with the training process, the result indicated that the DL-based approach is applicable for BCR measuring, which is a step toward suggesting an implication of this method. Overall, the potential utility of this proposed approach for the BCR measurement promises to be considerable.
Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses
Authors: Michaud Paradis Marie-Chloé , Doucet François R. , Rousselot Steeve , Hernández-García Alex , Rifai Kheireddine , Touag Ouardia , Özcan Lütfü Ç. , Azami Nawfal , Dollé Mickaël
Published: November 2022

Laser-induced breakdown spectroscopy (LIBS) is a valuable tool for the solid-state elemental analysis of battery materials. Key advantages include a high sensitivity for light elements (lithium included), complex emission patterns unique to individual elements through the full periodic table, and record speed analysis reaching 1300 full spectra per second (1.3 kHz acquisition rate). This study investigates deep learning methods as an alternative tool to accurately recognize different compositions of similar battery materials regardless of their physical properties or manufacturer. Such applications are of interest for the real-time digitalization of battery components and identification in automated manufacturing and recycling plant designs.
Unleashing the Potentials of Quantum Probability Theory for Customer Experience Analytics
Authors: Rika Havana , Aviv Itzhak , Weitzfeld Roye
Published: November 2022

In information systems research, the advantages of Customer Experience (CX) and its contribution to organizations are largely recognized. The CX analytics evaluate how customers perceive products, ranging from their functional usage to their cognitive states regarding the product, such as emotions, sentiment, and satisfaction. The most recent research in psychology reveals that cognition analytics research based on Classical Probability Theory (CPT) and statistical learning, which is used to evaluate people’s cognitive states, is limited due to their reliance on rational decision-making. However, the cognitive attitudes of customers are characterized by uncertainty and entanglement, resulting in irrational decision-making bias. What is captured by traditional CPT-based data science in the context of cognition aspects of CX analytics is only a small portion of what should be captured. Current CX analytics efforts fall far short of their full potential. In this paper, we set a novel research direction for CX analytics by Quantum Probability Theory (QPT). QPT-based analytics have been introduced recently in psychology research and reveal better cognition assessment under uncertainty, with a high level of irrational behavior. Adopting recent advances in the psychology domain, this paper develops a vision and sets a research agenda for expanding the application of CX analytics by QPT to overcome CPT shortcomings, identifies research areas that contribute to the vision, and proposes elements of a future research agenda. To stimulate debate and research QPT-CX analytics, we attempt a preliminary characterization of the novel method by introducing a QPT-based rich mathematical framework for CX cognitive modeling based on quantum superposition, Bloch sphere, and Hilbert space. We demonstrate the implementation of the QPT-CX model by the use case of customers’ emotional motivator assessments while implementing quantum vector space with a set of mathematical axioms for CX analytics. Finally, we outline the key advantages of quantum CX over classical by supporting theoretical proof for each key.
Hierarchical Structure and Properties of the Bone at Nano Level
Authors: Hamandi Farah , Goswami Tarun
Published: November 2022

Bone is a highly hierarchical complex structure that consists of organic and mineral components represented by collagen molecules (CM) and hydroxyapatite crystals (HAC), respectively. The nanostructure of bone can significantly affect its mechanical properties. There is a lack of understanding how collagen fibrils (CF) in different orientations may affect the mechanical properties of the bone. The objective of this study is to investigate the effect of interaction, orientation, and hydration on atomic models of the bone composed of collagen helix (CH) and HAC, using molecular dynamics simulations and therefrom bone-related disease origins. The results demonstrate that the mechanical properties of the bone are affected significantly by the orientation of the CF attributed to contact areas at 0° and 90° models. The molecular dynamics simulation illustrated that there is significant difference (p < 0.005) in the ultimate tensile strength and toughness with respect to the orientation of the hydrated and un-hydrated CF. Additionally, the results indicated that having the force in a longitudinal direction (0°) provides more strength compared with the CF in the perpendicular direction (90°). Furthermore, the results show that substituting glycine (GLY) with any other amino acid affects the mechanical properties and strength of the CH, collagen–hydroxyapatite interface, and eventually affects the HAC. Generally, hydration dramatically influences bone tissue elastic properties, and any change in the orientation or any abnormality in the atomic structure of either the CM or the HAC would be the main reason of the fragility in the bone, affecting bone pathology.
Effect of Inoculum Microbial Diversity in Ex Situ Biomethanation of Hydrogen
Authors: Logroño Washington , Kluge Paul , Kleinsteuber Sabine , Harms Hauke , Nikolausz Marcell
Published: November 2022

The effects of the inoculum origin, temperature or operational changes on ex situ biomethanation by complex microbial communities have been investigated; however, it remains unclear how the diversity of the inoculum influences the process and its stability. We explored the effect of microbial diversity of four inocula (coded as PF, WW, S37 and Nrich) on methane production, process stability and the formation of volatile fatty acids as by-products. The highest methane amounts produced were 3.38 ± 0.37 mmol, 3.20 ± 0.07 mmol, 3.07 ± 0.27 mmol and 3.14 ± 0.06 mmol for PF, WW, S37 and Nrich, respectively. The highest acetate concentration was found in less diverse cultures (1679 mg L−1 and 1397 mg L−1 for S37 and Nrich, respectively), whereas the acetate concentrations remained below 30 mg L−1 in the more diverse cultures. The maximum concentration of propionate was observed in less diverse cultures (240 mg L−1 and 37 mg L−1 for S37 and Nrich cultures, respectively). The highly diverse cultures outperformed the medium and low diversity cultures in the long-term operation. Methanogenic communities were mainly composed of hydrogenotrophic methanogens in all cultures. Aceticlastic methanogenesis was only active in the highly diverse sludge community throughout the experiment. The more diverse the inocula, the more methane was produced and the less volatile fatty acids accumulated, which could be attributed to the high number of microbial functions working together to keep a stable and balanced process. It is concluded that the inoculum origin and its diversity are very important factors to consider when the biomethanation process is performed with complex microbial communities.
Is Lymphedema a Systemic Disease? A Paired Molecular and Histological Analysis of the Affected and Unaffected Tissue in Lymphedema Patients
Authors: Wolf Stefan , von Atzigen Julia , Kaiser Bettina , Grünherz Lisanne , Kim Bong-Sung , Giovanoli Pietro , Lindenblatt Nicole , Gousopoulos Epameinondas
Published: November 2022

Secondary lymphedema is a chronic, debilitating disease and one of the most common side effects of oncologic surgery, substantially decreasing quality of life. Despite the progress conducted in lymphedema research, the underlying pathomechanisms remain elusive. Lymphedema is considered to be a disease affecting an isolated extremity, yet imaging studies suggest systemic changes of the lymphatic system in the affected patients. To evaluate potential systemic manifestations in lymphedema, we collected matched fat and skin tissue from the edematous and non-edematous side of the same 10 lymphedema patients as well as anatomically matched probes from control patients to evaluate whether known lymphedema manifestations are present systemically and in comparison to health controls. The lymphedematous tissue displayed various known hallmarks of lymphedema compared to the healthy controls, such as increased epidermis thickness, collagen deposition in the periadipocyte space and the distinct infiltration of CD4+ cells. Furthermore, morphological changes in the lymphatic vasculature between the affected and unaffected limb in the same lymphedema patient were visible. Surprisingly, an increased collagen deposition as well as CD4 expression were also detectable in the non-lymphedematous tissue of lymphedema patients, suggesting that lymphedema may trigger systemic changes beyond the affected extremity.
Significance of Features from Biomedical Signals in Heart Health Monitoring
Authors: Mamun Mohammad Mahbubur Rahman Khan
Published: November 2022

Cardiovascular diseases require extensive diagnostic tests and frequent physician visits. With the advance in signal processing and sensor technology, now it is possible to acquire vital signs from the human body and process the signal to extract features necessary to primarily diagnose symptoms of cardiovascular disease early. This can help prevent deadly health incidents such as heart attack and or stroke, as well as reduce the number of visits to a health care facility. The proper detection of an elevated ST segment of ECG wave at an early stage may save the patient from having a heart attack or ST elevated myocardial infarction later. The use of a variety of complementary biomedical sensors can lead to a better diagnosis than what is possible when a single sensor is used. This paper proposes a MATLAB GUI which can detect elevated ST segments of ECG waves and use information from a variety of biomedical sensors to bring forth a technique to assess heart health to predict potential heart failure conditions. The proposed technique used fusion among multiple biomedical sensors to reduce the false alarm in diagnosis. Data from the online dataset were used to show the effectiveness and promise of the proposed detection of elevated ST segments and diagnosis techniques using the GUI.
Acute Administration of Ethanol and of a D1-Receptor Antagonist Affects the Behavior and Neurochemistry of Adult Zebrafish
Authors: Scerbina Tanya , Gerlai Robert
Published: November 2022

Alcohol abuse represents major societal problems, an unmet medical need resulting from our incomplete understanding of the mechanisms underlying alcohol’s actions in the brain. To uncover these mechanisms, animal models have been proposed. Here, we explore the effects of acute alcohol administration in zebrafish, a promising animal model in alcohol research. One mechanism via which alcohol may influence behavior is the dopaminergic neurotransmitter system. As a proof-of-concept analysis, we study how D1 dopamine-receptor antagonism may alter the effects of acute alcohol on the behavior of adult zebrafish and on whole brain levels of neurochemicals. We conduct these analyses using a quasi-inbred strain, AB, and a genetically heterogeneous population SFWT. Our results uncover significant alcohol x D1-R antagonist interaction and main effects of these factors in shoaling, but only additive effects of these factors in measures of exploratory behavior. We also find interacting and main effects of alcohol and the D1-R antagonist on dopamine and DOPAC levels, but only alcohol effects on serotonin. We also uncover several strain dependent effects. These results demonstrate that acute alcohol may act through dopaminergic mechanisms for some but not all behavioral phenotypes, a novel discovery, and also suggest that strain differences may, in the future, help us identify molecular mechanisms underlying acute alcohol effects.
Discordant Amyloid Status Diagnosis in Alzheimer’s Disease
Authors: García-Vallés Lorena , Peña-Bautista Carmen , Álvarez-Sánchez Lourdes , Ferrer-Cairols Inés , Baquero Miguel , Cháfer-Pericás Consuelo
Published: November 2022

Introduction: Early and accurate Alzheimer’s disease (AD) diagnosis has evolved in recent years by the use of specific methods for detecting its histopathological features in concrete cases. Currently, biomarkers in cerebrospinal fluid (CSF) and imaging techniques (amyloid PET) are the most used specific methods. However, some results between both methods are discrepant. Therefore, an evaluation of these discrepant cases is required. Objective: The aim of this work is to analyze the characteristics of cases showing discrepancies between methods for detecting amyloid pathology. Methodology: Patients from the Neurology Department of La Fe Hospital (n = 82) were diagnosed using both methods (CSF biomarkers and amyloid-PET). Statistical analyses were performed using logistic regression, and sex and age were included as covariables. Additionally, results of standard neuropsychological evaluations were taken into account in our analyses. Results: The comparison between CSF biomarker (Aβ42) and amyloid PET results showed that around 18% of cases were discrepant—mainly CFS-negative and PET-positive cases had CSF levels close to the cut-off point. In addition, a correlation between the episodic memory test and CSF biomarkers levels was observed. However, the same results were not obtained for other neuropsychological domains. In general, CSF- and PET-discrepant cases showed altered episodic memory in around 66% of cases, while 33% showed normal performance. Conclusions: In common clinical practice at tertiary memory centers, result discrepancies between tests of amyloid status are far more common than expected. However, episodic memory tests remain an important support method for AD diagnosis, especially in cases with discrepant results between amyloid PET and CSF biomarkers.
Alpha-Lipoic Acid Supplementation Restores Early Age-Related Sensory and Endothelial Dysfunction in the Skin
Authors: de Bengy Anne-France , Decorps Johanna , Martin Lisa S. , Pagnon Aurélie , Chevalier Fabien P. , Sigaudo-Roussel Dominique , Fromy Bérengère
Published: November 2022

Many changes characterize skin aging, and the resulting dysfunctions still constitute a real challenge for our society. The aim of this study was to compare the skin aging of two rat strains, Wistar and Brown Norway (BN), considered as “poorly aging” and “healthy aging” models, respectively, and to assess the effect of alpha-lipoic acid (LPA), especially on skin microcirculation. To this purpose, various skin characteristics were studied at 6, 12, and 24 months and compared to the results of LPA treatment performed at 12 or 24 months. Skin aging occurred in both strains, but we showed an early occurrence of different age-related disorders in the Wistar strain compared to BN strain, especially regarding weight gain, glycemia dysregulation, basal skin perfusion, endothelial function, and skin resistance to low pressure. LPA treatment tended to improve skin resistance to low pressure in BN but not in Wistar despite the improvement of basal skin perfusion, endothelial function, and skin sensory sensitivity. Overall, this study confirmed the healthier aging of BN compared to Wistar strain and the positive effect of LPA on both general state and skin microcirculation.
Advances in the Fabrication and Characterization of Superhydrophobic Surfaces Inspired by the Lotus Leaf
Authors: Farzam Melika , Beitollahpoor Mohamadreza , Solomon Samuel E. , Ashbaugh Henry S. , Pesika Noshir S.
Published: November 2022

Nature has proven to be a valuable resource in inspiring the development of novel technologies. The field of biomimetics emerged centuries ago as scientists sought to understand the fundamental science behind the extraordinary properties of organisms in nature and applied the new science to mimic a desired property using various materials. Through evolution, living organisms have developed specialized surface coatings and chemistries with extraordinary properties such as the superhydrophobicity, which has been exploited to maintain structural integrity and for survival in harsh environments. The Lotus leaf is one of many examples which has inspired the fabrication of superhydrophobic surfaces. In this review, the fundamental science, supported by rigorous derivations from a thermodynamic perspective, is presented to explain the origin of superhydrophobicity. Based on theory, the interplay between surface morphology and chemistry is shown to influence surface wetting properties of materials. Various fabrication techniques to create superhydrophobic surfaces are also presented along with the corresponding advantages and/or disadvantages. Recent advances in the characterization techniques used to quantify the superhydrophobicity of surfaces is presented with respect to accuracy and sensitivity of the measurements. Challenges associated with the fabrication and characterization of superhydrophobic surfaces are also discussed.
The Effectiveness of Polyvinylidene Fluoride Membranes Modified with Poloxamer and Single/Multi-Walled Carbon Nanotubes for Lactalbumin Purification
Authors: Arahman Nasrul , Sari Widia Puspita , Sari Indah Maulana , Rosnelly Cut Meurah , Mulyati Sri , Fahrina Afrillia , Bilad Muhammad Roil , Gunawan Poernomo , Pasaoglu Mehmet Emin , Teber Oğuz Orhun , Vatanpour Vahid , Koyuncu Ismail , Yusni Yusni
Published: November 2022

The application of separation technology using ultrafiltration/nanofiltration membranes for protein purification and concentration has grown rapidly in the last decade. Innovations to synthesize membranes with properties and performance that suit the characteristics of the feed solution have been and will keep developing. This study aims to examine the strategies to improve the performance of the Polyvinylidene Fluoride (PVDF) membrane for lactalbumin protein isolation. The PVDF polymer membrane was modified by adding Poloxamer 188 (Po1) copolymer and a combination of two types of nanocarbons, i.e., single-walled carbon nanotubes (S-CnT) and multi-walled carbon nanotubes (M-CnT). The following membrane characteristics were examined: mechanical properties, morphological structure, porosity, elemental composition and functional groups, and surface hydrophilicity. The membrane’s filtration performance was analyzed in terms of its ability to pass water (flux) and concentrate lactalbumin protein. The results showed that the changes in the membrane morphological structure were clearly visible in the SEM test, which exposed more open membrane pores after adding Pol and S-CnT/M-CnT additives. The mechanical properties of the membrane also increased, as indicated by the increase in the tensile strength from 12.1 MPa to 16.07 MPa. In general, it was found that the composition of the PVDF/Pol/S-CnT/NMP polymer solutions resulted in better filtration performance compared to the membranes made of only the PVDF/NMP polymer solution.
Follow-up of Interleukin 6 and Other Blood Markers during the Hospitalization of COVID-19 Patients: A Single-Center Study
Authors: Garrido Maritza P. , Vaswani Varsha , Contreras Katherinne , Barberán Marcela , Valenzuela-Valderrama Manuel , Klajn Diana , Romero Carmen , Vial Covarrubias María Jesús , Cornejo Rodrigo Alfredo
Published: November 2022

COVID-19 is a recent respiratory illness with high morbidity and mortality; therefore, the study and characterization of blood markers associated with the improvement or deterioration of COVID-19 patients are crucial. This study compared levels of interleukin 6 (IL-6), procalcitonin (PCT), D-dimer, cortisol, dehydroepiandrosterone sulfate (DHEA-S), c-reactive protein (CRP), 25-OH vitamin D, anti-SARS-CoV-2 IgG antibodies, and viremia in mild–moderate and severe–critical COVID-19 patients. In addition, the time course of blood markers was studied in severe–critical cases. The results show that levels of IL-6, PCT, D-dimer, and CRP, the cortisol/DHEA-S ratio, as well as positive viremia and anti-Spike IgGs were higher in severe–critical patients requiring hospitalization. During follow-up, most severe–critical cases displayed similar time patterns of IL-6 and viral load, whereas anti-SARS-CoV-2 antibody curves showed an inverse pattern. A decrease in IL-6 levels was associated with the improvement of COVID-19 patients, mostly through a reduced oxygen requirement. This preliminary study suggests that an increase in serum IL-6, PCT, D-dimer and CRP levels and the cortisol/DHEA-S ratio could support the selection of patients with poorer prognosis and the need for an intensive or alternative treatment. Additionally, changes in IL-6 during hospitalization were associated with changes in patient’s status mainly with a decrease in oxygen requirements, which indicates that serial measurements of IL-6 could predict the outcome of severe–critical patients with COVID-19 pneumonia.
Localization in Two-Dimensional Quasicrystalline Lattices
Authors: González-García Luis Antonio , Alva-Sánchez Héctor , Paredes Rosario
Published: November 2022

We investigate the emergence of localization in a weakly interacting Bose gas confined in quasicrystalline lattices with three different rotational symmetries: five, eight, and twelve. The analysis, performed at a mean field level and from which localization is detected, relies on the study of two observables: the inverse participation ratio (IPR) and the Shannon entropy in the coordinate space. Those physical quantities were determined from a robust statistical study for the stationary density profiles of the interacting condensate. Localization was identified for each lattice type as a function of the potential depth. Our analysis revealed a range of the potential depths for which the condensate density becomes localized, from partially at random lattice sites to fully in a single site. We found that localization in the case of five-fold rotational symmetry appears for (6ER,9ER), while it occurs in the interval (12ER,15ER) for octagonal and dodecagonal symmetries.
Entanglement Negativity and Concurrence in Some Low-Dimensional Spin Systems
Authors: Lima Leonardo
Published: November 2022

The influence of magnon bands on entanglement in the antiferromagnetic XXZ model on a triangular lattice, which models the bilayer structure consisting of an antiferromagnetic insulator and normal metal, is investigated. This effect was studied in ferromagnetic as well as antiferromagnetic triangular lattices. Quantum entanglement measures given by the entanglement negativity have been studied, where a magnon current is induced in the antiferromagnet due to interfacial exchange coupling between localized spins in the antiferromagnet and itinerant electrons in a normal metal. Moreover, quantum correlations in other frustrated models, namely the metal-insulation antiferromagnetic bilayer model and the Heisenberg model with biquadratic and bicubic interactions, are analyzed.
The Properties of Alpha Risk Parity Portfolios
Authors: Gava Jérôme , Turc Julien
Published: November 2022

Risk parity is an approach to investing that aims to balance risk evenly across assets within a given universe. The aim of this study is to unify the most commonly-used approaches to risk parity within a single framework. Links between these approaches have been identified in the published literature. A key point in risk parity is being able to identify and control the contribution of each asset to the risk of the portfolio. With alpha risk parity, risk contributions are given by a closed-form formula. There is a form of antisymmetry—or self-duality—in alpha risk portfolios that lie between risk budgeting and minimum-risk portfolios. Techniques from information geometry play a key role in establishing these properties.
Rosenblatt’s First Theorem and Frugality of Deep Learning
Authors: Kirdin Alexander , Sidorov Sergey , Zolotykh Nikolai
Published: November 2022

The Rosenblatt’s first theorem about the omnipotence of shallow networks states that elementary perceptrons can solve any classification problem if there are no discrepancies in the training set. Minsky and Papert considered elementary perceptrons with restrictions on the neural inputs: a bounded number of connections or a relatively small diameter of the receptive field for each neuron at the hidden layer. They proved that under these constraints, an elementary perceptron cannot solve some problems, such as the connectivity of input images or the parity of pixels in them. In this note, we demonstrated Rosenblatt’s first theorem at work, showed how an elementary perceptron can solve a version of the travel maze problem, and analysed the complexity of that solution. We also constructed a deep network algorithm for the same problem. It is much more efficient. The shallow network uses an exponentially large number of neurons on the hidden layer (Rosenblatt’s A-elements), whereas for the deep network, the second-order polynomial complexity is sufficient. We demonstrated that for the same complex problem, the deep network can be much smaller and reveal a heuristic behind this effect.
Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory
Authors: Wu Liangshun , Qu Junsuo , Shi Haonan , Li Pengfei
Published: November 2022

Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time.
The Influence of Entrepreneurial Motivation on the Valuation of Socioeconomic Benefits of Business Incubator Functions
Authors: Lin-Lian Cristina , De-Pablos-Heredero Carmen , Montes-Botella José Luis , Lin Susana
Published: November 2022

This paper aims to evaluate whether the reasons an entrepreneur starts a business influence their assessment of the contribution of business incubators to society. The influence of business incubator functions was found to be positively valued by entrepreneurs in the socioeconomic ecosystem, according to the results of our empirical SEM study. This study contributes in helping to understand the opinions that entrepreneurs have as protagonists in the entrepreneurial ecosystem, according to their reasons to start a business, on the functionality of incubators, and the sustainable contribution of such entities to socioeconomic benefits in society.
Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses
Authors: Lampropoulos Georgios , Keramopoulos Euclid , Diamantaras Konstantinos , Evangelidis Georgios
Published: November 2022

This study aims to understand the public’s perspectives, sentiments, attitudes, and discourses regarding the adoption, integration, and use of augmented reality and virtual reality in education and in general by analyzing social media data. Due to its nature, Twitter was the selected platform. Over 17 million tweets were retrieved from January 2010 to December 2020 and four datasets were created. Two of them referred to the general use of these technologies and two to their educational use. The data was analyzed using text mining, sentiment analysis (e.g., polarity and emotion detection), and topic modeling methods. TextBlob, Word-Emotion Association Lexicon (EmoLex), Valence Aware Dictionary for Sentiment Reasoning (VADER), and Latent Dirichlet Allocation (LDA) were some of the tools used. Based on the results, the majority of the public were positively disposed toward the general and the educational use of both augmented reality and virtual reality and mostly expressed positive emotions (e.g., anticipation, trust, and joy) when referring to them. In total, 11 topics emerged that were related to education, new technologies, digital and social media use, marketing and advertising, the industrial domain, the health domain, gaming, fitness and exercising, devices, the travel and tourism domain, and software development kits. The educational benefits of augmented reality and virtual reality, their ability to enrich both teaching and learning activities, and their role as effective educational means were evident.
A Three-Phase CDRT Strategy Based on Successive Relay for Smart Grid
Authors: Luo Mofan , Liu Geng , Ren Jie , Yao Liang , Liu Ying , Song Jianjian , Chen Jian , Zhou Yuchen
Published: November 2022

In the vision of the future smart grid, the communication is often featured by wide range, massive connect, and low latency, which poses new requirements on the reachable distance and spectral efficiency of wireless communication. In this regard, this paper studies ergodic capacity enhancement by applying successive relay (SR) technology to a non-orthogonal multiple access (NOMA) based Coordinated Direct and Relay transmission (CDRT) system, where a base station (BS) communicates with a near user (NU) directly while communicating with a far user (FU) with the help of a group of relays. We design a novel three-phase CDRT strategy based on SR technology to overcome the half-duplex (HD) constrain without introducing additional noise. The proposed strategy can improve the spectral efficiency while expanding the communication coverage, which to some extent improves the communication quality of the edge users of the smart grid and reduces the communication delay. To analyze the performance of the proposed three-phase CDRT strategy, an exact and closed-form expression for ergodic capacity of the NU, the FU, and the whole system is derived. Finally, the numerical and simulation results validate the analysis results and show that the proposed strategy can improve the ergodic capacity of FU without reducing the capacity scaling of NU.
Rail Fastener Status Detection Based on MobileNet-YOLOv4
Authors: Fu Junpeng , Chen Xingjie , Lv Zhaomin
Published: November 2022

As an important part of track inspection, the detection of rail fasteners is of great significance to improve the safety of train operation. Additionally, rail fastener detection belongs to small-target detection. The YOLOv4 algorithm is relatively fast in detection and has some advantages in small-target detection. Therefore, YOLOv4 is used for rail fastener status detection. However, YOLOv4 still suffers from the following two problems in rail fastener status detection. First, the features extracted by the original feature extraction network of YOLOv4 are relatively rough, which is not conducive to crack anomaly detection on rail fasteners. In addition, the traditional convolutional neural network has a larger number of parameters and calculations, which are difficult to run on the embedded system with low memory and processing power. To effectively solve those two problems, this paper proposes a rail fastener status detection algorithm based on MobileNet-YOLOv4 (M-YOLOv4). The edge features and texture features of rail fasteners are very important for rail fastener detection, and CSPDarknet53 cannot effectively extract the features of fasteners. The MobileNet is used to replace the CSPDarknet53 feature extraction network in the YOLOv4 algorithm, which can extract subtle features of rail fasteners and reduce the number of parameters and calculations of the algorithm. The experimental results show that the M-YOLOv4 algorithm has high detection accuracy and low resource consumption in rail fastener status detection. The false-alarm rate (FAR), missed-alarm rate (MAR), and error rate (ER) were 5.71%, 1.67%, and 4.24%, respectively, and the detection speed reached 59.8 fps. Compared with YOLOv4, the number of parameters and calculations were reduced by about 80.75% and 83.20%, respectively.