bluuebunny/crossref_metadata_2025 huggingface dataset:
Formic Acid Generation from CO2 Reduction by MOF-253 Coordinated Transition Metal Complexes: A Computational Chemistry Perspective
Authors: Hsieh Meng-Chi , Krishnan Ranganathan , Tsai Ming-Kang
Published: August 2022

The inclusion of transition metal elements within metal–organic frameworks (MOFs) is considered one of the most promising approaches for enhancing the catalytic capability of MOFs. In this study, MOF-253 containing bipyridine coordination sites is investigated for possible transition metal chelation, and a consequent possible CO2 reduction mechanism in the formation of formic acid. All transition metal elements of the third, fourth and fifth periods except hafnium and the lanthanide series are considered using density functional theory calculations. Two distinct types of CO2 reduction mechanisms are identified: (1) the five-coordination Pd center, which promotes formic acid generation via an intramolecular proton transfer pathway; (2) several four-coordination metal centers, including Mn, Pd, and Pt, which generate formic acid by means of heterolytic hydrogen activation. The MOF-253 environment is found to promote beneficial steric hindrance, and to constrain metal–ligand orientation, which consequently facilitates the formation of formic acid, particularly with the tetrahedral Mn center at high-spin electronic state.
Short-Chain Fatty Acids Impair Neutrophil Antiviral Function in an Age-Dependent Manner
Authors: Carrillo-Salinas Francisco J. , Parthasarathy Siddharth , Moreno de Lara Laura , Borchers Anna , Ochsenbauer Christina , Panda Alexander , Rodriguez-Garcia Marta
Published: August 2022

Half of the people living with HIV are women. Younger women remain disproportionally affected in endemic areas, but infection rates in older women are rising worldwide. The vaginal microbiome influences genital inflammation and HIV infection risk. Multiple factors, including age, induce vaginal microbial alterations, characterized by high microbial diversity that generate high concentrations of short-chain fatty acids (SCFAs), known to modulate neutrophil function. However, how SCFAs may modulate innate anti-HIV protection by neutrophils is unknown. To investigate SCFA-mediated alterations of neutrophil function, blood neutrophils from younger and older women were treated with SCFAs (acetate, butyrate and propionate) at concentrations within the range reported during bacterial vaginosis, and phenotype, migration and anti-HIV responses were evaluated. SCFA induced phenotypical changes preferentially in neutrophils from older women. Butyrate decreased CD66b and increased CD16 and CD62L expression, indicating low activation and prolonged survival, while propionate increased CD54 and CXCR4 expression, indicating a mature aged phenotype. Furthermore, acetate and butyrate significantly inhibited neutrophil migration in vitro and specifically reduced α-defensin release in older women, molecules with anti-HIV activity. Following HIV stimulation, SCFA treatment delayed NET release and dampened chemokine secretion compared to untreated neutrophils in younger and older women. Our results demonstrate that SCFAs can impair neutrophil-mediated anti-HIV responses.
An Epigenetic Role of Mitochondria in Cancer
Authors: Liu Yu’e , Chen Chao , Wang Xinye , Sun Yihong , Zhang Jin , Chen Juxiang , Shi Yufeng
Published: August 2022

Mitochondria are not only the main energy supplier but are also the cell metabolic center regulating multiple key metaborates that play pivotal roles in epigenetics regulation. These metabolites include acetyl-CoA, α-ketoglutarate (α-KG), S-adenosyl methionine (SAM), NAD+, and O-linked beta-N-acetylglucosamine (O-GlcNAc), which are the main substrates for DNA methylation and histone post-translation modifications, essential for gene transcriptional regulation and cell fate determination. Tumorigenesis is attributed to many factors, including gene mutations and tumor microenvironment. Mitochondria and epigenetics play essential roles in tumor initiation, evolution, metastasis, and recurrence. Targeting mitochondrial metabolism and epigenetics are promising therapeutic strategies for tumor treatment. In this review, we summarize the roles of mitochondria in key metabolites required for epigenetics modification and in cell fate regulation and discuss the current strategy in cancer therapies via targeting epigenetic modifiers and related enzymes in metabolic regulation. This review is an important contribution to the understanding of the current metabolic-epigenetic-tumorigenesis concept.
Tung Oil Microcapsules Prepared with Different Emulsifiers and Their Effects on the Properties of Coating Film
Authors: Pan Pan , Yan Xiaoxing , Peng Wenwen
Published: August 2022

In water-based coatings, the addition of tung oil microcapsules coated with urea formaldehyde resin (UF) can effectively repair the microcracks in the coating film on the surface of wood. The tung oil as a repairing agent plays an important role in the preparation of microcapsules. In this paper, Span-80, SDBS, OP-10, Tween-80 and SDS were used as five emulsifiers to study the influence of different emulsifiers on the preparation of tung oil microcapsules, and the properties of the coating film added to the waterborne coatings. According to the coating process of three bottoms and three sides, tung oil microcapsules were added to the water-based paint with a content of 12% and coated on the wood surface. The appearance and microstructure of the microcapsules, as well as the mechanical, optical and self-repairing properties of the paint film were analyzed to find out the best emulsifier suitable for the core material. The tung oil microcapsules prepared by Tween-80 have the best morphology, concentrated particle size distribution, particle size of 6–15 μm, and spherical morphology. The film with the microcapsules prepared by Tween-80 had the best performance, small color difference, high gloss, hardness of 5H, adhesion grade 1, elongation at break of 47.23%, impact resistance of 20 kg∙cm, and good toughness. At the same time, the repair rate reached 37.9%. The results provide the application reference for the use of self-repairing microcapsules in coatings.
The Model for Estimating the Failure Mechanism of Tensioned Plasma-Sprayed Zirconia Ceramic Hard Coating
Authors: Dolgov Nikolay , Carjova Kristine , Vinogradov Leonid , Melnychenko Olexandr , Brunavs Janis , Girgensone Sintija , Baronins Janis , Zakoyan Lilit
Published: August 2022

Loading flat coated specimens with tensile forces is a universal method to determine the nature of fracture of hard coatings in maritime applications. Such an approach allows the determination of the shear strength at the interfaces between layers and the cohesive properties. Thus, the authors studied the fracturing processes of plasma-sprayed yttria-stabilized zirconia two-layer coatings under the action of tensile forces. The results revealed cracking characteristics in the outer ceramic layer and delamination of flat metal specimens. An analytical method led to normal stress determination in the coating. It also exhibited shear stresses at the bond coat-substrate interface and the top coat-bond coat interfaces. The results include normal and shear stress distributions before and after fragmentation of the ceramic top layer. Tested substrate, the metal bond coat, and the ceramic top coat exhibit a modulus of elasticity of 196, 142, and 47 GPa, respectively. Engineers could apply the proposed methodology and the mathematical analysis to study the causes of cracks and delamination between layers.
Polymeric Substrates Modification with Biobased Functional Compounds
Authors: Coltelli Maria-Beatrice , Coiai Serena
Published: August 2022

Synthetic polymers and biodegradable and biobased polymers are currently the largest class of materials studied and used for various applications due to their unique and peculiar properties [...]
Structural Properties of Yttrium Aluminum Garnet, Doped with Lanthanum
Authors: Zhanturina Nurgul , Sergeyev Daulet , Aimaganbetova Zukhra , Zhubaev Abzal , Bizhanova Karlygash
Published: August 2022

The results from simulation of the structural and optical properties of YAG pure, doped with two ions and four ions of lanthanum, are presented in this paper to investigate the effect of dopants on a crystal. The simulation was carried out in the Quantum Espresso program. The band structure, density of states, the dielectric function, absorption, refractive index, energy loss function and reflectivity were calculated for different samples. It can be assumed that with a rise in concentration density of states, the impurity atom increases and the position of the most populated levels shifts to the right in the region of negative energies in the case of two atoms and to the left in the case of four atoms of lanthanum. We can draw conclusions about the transparency of the sample in the visible region, because the absorption spectra appear in the UV region. This transparent nature makes them perfect candidates for deep UV nonlinear optical materials. From the analysis of dielectric function, it is clear that in the case of YAG doped with two ions of lanthanum, two peaks appeared compared to the sample with one peak in pure and doped with four ions. Anisotropy is most pronounced with an increase in the degree of doping.
Ionic Liquid-Aided Synthesis of Anatase TiO2 Nanoparticles: Photocatalytic Water Splitting and Electrochemical Applications
Authors: Soundarya T. L. , Jayalakshmi T. , Alsaiari Mabkhoot A. , Jalalah Mohammed , Abate Antonio , Alharthi Fahad A. , Ahmad Naushad , Nagaraju G.
Published: August 2022

Titanium dioxide nanoparticles play a crucial role in the production of hydrogen gas evolution. Among the four polymorphic phases of TiO2 (anatase, rutile, brookite, and TiO2 (B)), the anatase phase shows good photo activity in catalytic applications. We prepared a single anatasephase of TiO2 nanoparticles usinga facile one-step ionothermal method with the existence of 1-(3,6-dioxa heptane) 3-methyl imidazolium methane sulfonate IL[DOMIMS]. The ionic liquid-based substitution reaction mechanism was utilized for the ionothermal synthesis of TiO2. The anatase phase structure and nanoparticle-like morphology of synthesized TiO2 nanomaterial were confirmed by XRD analysis and TEM studies. The vibrational frequency of the Ti–O–Ti bond at 544 cm−1 was measured usingthe FTIR spectrum, and the UV absorbance of the sample was studied usingthe UV/visible spectra. The prepared TiO2 nanoparticles showed the best results of H2 generation via awater-splitting reaction, liberating 2084 μmol·g−1·h−1 of H2 gas. TiO2 nanoparticles act as a good material for electrochemical applications such as supercapacitors and sensing of dopamine, as well as a better photocatalyst for the degradation of methylene blue.
Study of Microstructure and Properties of AZ91 Magnesium Alloy Welded Joint with Magnetic Field and TiO2 Activated Flux
Authors: Zhang Guiqing , Liu Xintong , Su Yunhai
Published: August 2022

In order to improve the weldability and bearing capacity of AZ91 magnesium alloy welded joints, magnetic field and active flux were added in the TIG welding process. In the welding process, the magnetic field and welding parameters were unchanged, and the coating amount of active flux was adjusted. The formability, mechanical properties, and microstructure of the welded joints under different coating amounts of activated flux were analyzed, and the crystallization nucleation characteristics of molten pool were discussed. The experimental results reveal that the combined effect of the magnetic field and activated flux has a significant effect on increasing the penetration and promoting mechanical properties. When the coating amount of activated flux is 3 mg/cm2, the highest penetration of the welded joint is obtained, which is 141% of that without activated flux. Meanwhile, the mechanical properties reach the maximum, which is a tensile strength of 292 MPa, elongation of 11.2%, and weld zone hardness of 75.6 HV (0.5 Kgf). The combined effect of TiO2 flux and magnetic field does not change the phase composition and the grain orientation of a weld metal but can affect the grain size. The average grain size of a weld metal under an activated flux coating amount of 3 mg/cm2 is 18.2% smaller than that under an activated flux coating amount of 1 mg/cm2.
Neoadjuvant Chemotherapy in Pregnant Patients with Cervical Cancer: A Monocentric Retrospective Study
Authors: Bernardini Federica , Ferrandina Gabriella , Ricci Caterina , Fagotti Anna , Fanfani Francesco , Cavaliere Anna Franca , Gui Benedetta , Scambia Giovanni , De Vincenzo Rosa
Published: August 2022

Background: To date, little and discordant data still exists on the management of cervical cancer (CC) during pregnancy. In this paper, we report our experience of the treatment of these patients analyzing the oncologic, obstetric, and neonatal outcomes. Methods: Between January 2010 and December 2021, 13 patients were diagnosed with CC during pregnancy. All patients underwent platinum-based neoadjuvant chemotherapy (NACT) and 11/13 patients underwent a cesarean radical hysterectomy (CRH). Results: All 13 patients were diagnosed with squamous-cell carcinoma, FIGO-2018 stage between IB2-IIIC1. The majority of patients had a partial (61.5%) or complete (15.4%) response to NACT. Most patients had a regular course of pregnancy and the obstetric complications observed were gestational diabetes mellitus in 23.1% and IUGR in 15.4% of cases. CRH was performed in the absence of major complications. Only 2 patients (15.4%) had disease recurrence and only 1 patient (7.7%) died of disease. All children are currently healthy. At birth, we observed mainly prematurity-related complications (38.5% respiratory distress syndrome and 7.7% neonatal jaundice) and only a case of congenital malformation (hypospadias). In our pediatric population, we reported a case of malignancy (acute myeloid leukemia). Conclusion: NACT seems to be safe and efficacious in controlling tumor burden during pregnancy. CRH following NACT appears to be feasible, avoiding repeated surgery and treatment delays. This approach is also reasonably safe from a maternal, obstetric, and neonatal point of view.
Hotspots and Season Related to Wildlife Roadkill in the Amazonia–Cerrado Transition
Authors: Santos Evandro , Cordova Milton , Rosa Clarissa , Rodrigues Domingos
Published: August 2022

The construction of new roads opens access to native environments, resulting in changes to the landscape. These roads cause the death of native wildlife due to collisions with vehicles, which is the main cause of human-induced vertebrate death. This work aimed to investigate the spatial distribution of roadkills on the BR-163 highway, Mato Grosso, Brazil, an Amazonia–Cerrado transition zone, to identify roadkill hotspots. The study area consisted of 244 km of road. Twenty-five trips were made totaling 6100 km of surveyed roadway between 2008 and 2011. A total of 1005 individuals from 65 species was recorded in this study. Mammals were the most affected, both in number of individuals and species, followed by birds, reptiles, and amphibians. The species with the highest roadkill rates were Cerdocyon thous and Tyto furcata. Identified endangered mammals included Myrmecophaga tridactyla, Chrysocyon brachyurus, Priodontes maximus, Speothos venaticus, Tapirus terrestris, Pteronura brasiliensis, and Ateles marginatus. The highest rates of roadkill were recorded during the wet season. The location and number of hotspots varied among classes and were related to different environmental variables such as waterbodies, forest fragments, and agricultural areas. Roadkills may be a major threat for vulnerable species and should therefore be studied to define conservation plans for local species and road traffic.
Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging
Authors: Shao Wenyi , Leung Kevin H. , Xu Jingyan , Coughlin Jennifer M. , Pomper Martin G. , Du Yong
Published: August 2022

While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson’s disease (PD), they require a large amount of data for training. It is often difficult to collect a large population of patient data to support the ML research, and the ground truth of lesion is also unknown. This paper leverages a generative adversarial network (GAN) to generate digital brain phantoms for training ML-based PD SPECT algorithms. A total of 594 PET 3D brain models from 155 patients (113 male and 42 female) were reviewed and 1597 2D slices containing the full or a portion of the striatum were selected. Corresponding attenuation maps were also generated based on these images. The data were then used to develop a GAN for generating 2D brain phantoms, where each phantom consisted of a radioactivity image and the corresponding attenuation map. Statistical methods including histogram, Fréchet distance, and structural similarity were used to evaluate the generator based on 10,000 generated phantoms. When the generated phantoms and training dataset were both passed to the discriminator, similar normal distributions were obtained, which indicated the discriminator was unable to distinguish the generated phantoms from the training datasets. The generated digital phantoms can be used for 2D SPECT simulation and serve as the ground truth to develop ML-based reconstruction algorithms. The cumulated experience from this work also laid the foundation for building a 3D GAN for the same application.
miR-146a-5p, miR-223-3p and miR-142-3p as Potential Predictors of Major Adverse Cardiac Events in Young Patients with Acute ST Elevation Myocardial Infarction—Added Value over Left Ventricular Myocardial Work Indices
Authors: Scărlătescu Alina Ioana , Barbălată Teodora , Sima Anca Volumnia , Stancu Camelia , Niculescu Loredan Ștefan , Micheu Miruna Mihaela
Published: August 2022

Acute ST elevation myocardial infarction (STEMI) remains a leading cause of morbidity and mortality worldwide despite continuous advances in diagnostic, prognostic and therapeutic methods. Myocardial work (MW) indices and miRNAs have both emerged as potential prognostic markers in acute coronary syndromes in recent years. In this study we aim to assess the prognostic role of myocardial work indices and of a group of miRNAs in young patients with STEMI. We enrolled 50 young patients (<55 years) with STEMI who underwent primary PCI and 10 healthy age-matched controls. We performed standard 2D and 3D echocardiography; we also calculated left ventricular global longitudinal strain (GLS) and the derived myocardial work indices. Using RT-PCR we determined the plasmatic levels of six miRNAs: miR-223-3p, miR-142-3p, miR-146a-5p, miR-125a-5p, miR-486-5p and miR-155-5p. We assessed the occurrence of major adverse cardiac events (MACE) at up to one year after STEMI. Out of 50 patients, 18% experienced MACE at the one-year follow-up. In a Cox univariate logistic regression analysis, myocardial work indices were all significantly associated with MACE. The ROC analysis showed that GWI, GCW and GWE as a group have a better predictive value for MACE than each separately (AUC 0.951, p = 0.000). Patients with higher miRNAs values at baseline (miR-223-3p, miR-142-3p and miR-146a-5p) appear to have a higher probability of developing adverse events at 12 months of follow-up. ROC curves outlined for each variable confirmed their good predictive value (AUC = 0.832, p = 0.002 for miR-223-3p; AUC = 0.732, p = 0.031 for miR-142-3p and AUC = 0.848, p = 0.001 for miR-146a-5p); the group of three miRNAs also proved to have a better predictive value for MACE together than separately (AUC = 0.862). Moreover, adding each of the miRNAs (miR-233, miR-142-3p and miR-146a-5p) or all together over the myocardial work indices in the regression models improved their prognostic value. In conclusion, both myocardial work indices (GWI, GCW and GWE) and three miRNAs (miR-223-3p, miR-142-3p and miR-146a-5p) have the potential to be used as prognostic markers for adverse events after acute myocardial infarction. The combination of miRNAs and MW indices (measured at baseline) rather than each separately has very good predictive value for MACE in young STEMI patients (C-statistic 0.977).
Prenatal Course and Sonographic Features of Congenital Mesoblastic Nephroma
Authors: Tongsong Theera , Palangmonthip Watchareepohn , Chankhunaphas Wisit , Luewan Suchaya
Published: August 2022

Background: Congenital mesoblastic nephroma (CMN) is the most common renal tumor among fetuses and infants before the age of 6 months. It usually behaves as a benign tumor. The prenatal features and outcomes of pregnancies with fetal CMN have never been systematically reviewed and analyzed, whereas neonatal or pediatric series have been published several times. The aims of this study are to (1) describe the prenatal natural course and prenatal sonographic char-acteristics of CMN; (2) determine the outcomes of pregnancies with fetal CMN; and (3) demonstrate typical sonographic images together with video clips of prenatal CMN, as an educational example based on our index case presented here. Methods: Studies focused on fetal CMN, including those consecutively published on PubMed from 1980 to June 2022 as well as the index case presented here, were identified and validated to perform a systematic review. The data of fetal imaging and the prenatal course of pregnancies were extracted for analysis. Results: The findings derived from 41 cases of review are as follows: (1) No single case has been diagnosed in the first half of pregnancy. No cases were detected during routine anomaly screening at mid-pregnancy. All cases were de-tected in the third trimester or late second trimester. (2) Polyhydramnios is very common and is the first clinical manifestation in most cases, leading to detailed ultrasound in the second half of pregnancy. (3) Preterm birth and low birth weight are the most common adverse pregnancy out-comes, resulting in neonatal morbidity. (4) Hydrops fetalis, though relatively rare, can be associated with CMN and is a grave sign. (5) Prenatal diagnosis is essential since it is critical for the antenatal plan, comprising either referral to a tertiary care center or proper surveillance to prevent serious obstetric complications, especially preterm birth. (6) Ultrasound is the primary tool for prenatal diagnosis of CMN, whereas MRI can be used as an adjunct if some other tumors are suspicious or sonographic features are not typical for CMN. Conclusion: In contrast to CMN in neonates, fetal CMN is much more serious since it significantly impacts adverse pregnancy outcomes and perinatal morbidity and mortality. The typical prenatal course and the sonographic features of CMN are described.
Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning
Authors: Feher Balazs , Kuchler Ulrike , Schwendicke Falk , Schneider Lisa , Cejudo Grano de Oro Jose Eduardo , Xi Tong , Vinayahalingam Shankeeth , Hsu Tzu-Ming Harry , Brinz Janet , Chaurasia Akhilanand , Dhingra Kunaal , Gaudin Robert Andre , Mohammad-Rahimi Hossein , Pereira Nielsen , Perez-Pastor Francesc , Tryfonos Olga , Uribe Sergio E. , Hanisch Marcel , Krois Joachim
Published: August 2022

The detection and classification of cystic lesions of the jaw is of high clinical relevance and represents a topic of interest in medical artificial intelligence research. The human clinical diagnostic reasoning process uses contextual information, including the spatial relation of the detected lesion to other anatomical structures, to establish a preliminary classification. Here, we aimed to emulate clinical diagnostic reasoning step by step by using a combined object detection and image segmentation approach on panoramic radiographs (OPGs). We used a multicenter training dataset of 855 OPGs (all positives) and an evaluation set of 384 OPGs (240 negatives). We further compared our models to an international human control group of ten dental professionals from seven countries. The object detection model achieved an average precision of 0.42 (intersection over union (IoU): 0.50, maximal detections: 100) and an average recall of 0.394 (IoU: 0.50–0.95, maximal detections: 100). The classification model achieved a sensitivity of 0.84 for odontogenic cysts and 0.56 for non-odontogenic cysts as well as a specificity of 0.59 for odontogenic cysts and 0.84 for non-odontogenic cysts (IoU: 0.30). The human control group achieved a sensitivity of 0.70 for odontogenic cysts, 0.44 for non-odontogenic cysts, and 0.56 for OPGs without cysts as well as a specificity of 0.62 for odontogenic cysts, 0.95 for non-odontogenic cysts, and 0.76 for OPGs without cysts. Taken together, our results show that a combined object detection and image segmentation approach is feasible in emulating the human clinical diagnostic reasoning process in classifying cystic lesions of the jaw.
Statistical Mechanics of Long Walks in Dynamic Complex Networks: Statistical Arguments for Diversifying Selection
Authors: Volchenkov Dimitri , Suh C. Steve
Published: August 2022

We study the thermodynamic limit of very long walks on finite, connected, non-random graphs subject to possible random modifications and transportation capacity noise. As walks might represent the chains of interactions between system units, statistical mechanics of very long walks may be used to quantify the structural properties important for the dynamics of processes defined in networks. Networks open to random structural modifications are characterized by a Fermi–Dirac distribution of node’s fugacity in the framework of grand canonical ensemble of walks. The same distribution appears as the unique stationary solution of a discrete Fokker–Planck equation describing the time evolution of probability distribution of stochastic processes in networks. Nodes of inferior centrality are the most likely candidates for the future structural changes in the network.
An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages
Authors: Feng Guoce , Zhang Lei , Ai Feifan , Zhang Yirui , Hou Yupeng
Published: August 2022

A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed.
Quantum Teleportation and Dense Coding in Multiple Bosonic Reservoirs
Authors: Wang Yu , Hu Ming-Liang
Published: August 2022

The effect of a reservoir on quantum communication depends on its spectral density. The efficiency of quantum teleportation and dense coding is explored when each one of the channel qubits is coupled simultaneously to multiple bosonic reservoirs. It is shown that the non-Markovianity triggered by increasing the reservoir number can induce revivals of quantum advantages of the two protocols after their disappearance. However, the backflow of information to the system that signifies non-Markovianity does not always induce immediate revivals of the quantum advantages. There may be a delayed effect for some initial states, and only as the backflow of information accumulates to a certain extent can the revivals of quantum advantages be triggered.
An Entropy-Based Measure of Complexity: An Application in Lung-Damage
Authors: Ortiz-Vilchis Pilar , Ramirez-Arellano Aldo
Published: August 2022

The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.
FCNet: Stereo 3D Object Detection with Feature Correlation Networks
Authors: Wu Yingyu , Liu Ziyan , Chen Yunlei , Zheng Xuhui , Zhang Qian , Yang Mo , Tang Guangming
Published: August 2022

Deep-learning techniques have significantly improved object detection performance, especially with binocular images in 3D scenarios. To supervise the depth information in stereo 3D object detection, reconstructing the 3D dense depth of LiDAR point clouds causes higher computational costs and lower inference speed. After exploring the intrinsic relationship between the implicit depth information and semantic texture features of the binocular images, we propose an efficient and accurate 3D object detection algorithm, FCNet, in stereo images. First, we construct a multi-scale cost–volume containing implicit depth information using the normalized dot-product by generating multi-scale feature maps from the input stereo images. Secondly, the variant attention model enhances its global and local description, and the sparse region monitors the depth loss deep regression. Thirdly, for balancing the channel information preservation of the re-fused left–right feature maps and computational burden, a reweighting strategy is employed to enhance the feature correlation in merging the last-layer features of binocular images. Extensive experiment results on the challenging KITTI benchmark demonstrate that the proposed algorithm achieves better performance, including a lower computational cost and higher inference speed in 3D object detection.
The Effect of Message Framing on Preschoolers’ Attitudes and Behavioral Intentions: Influences of Age, Issue Involvement, and Delivery Method
Authors: Kim Bokyung , Pack Yun Hyun , Yi Soon Hyung
Published: August 2022

Many previous studies have revealed that people’s decision-making may differ depending on message framing—whether the same content is presented with an emphasis on gain or loss. However, almost nothing is known about preschooler responses according to message framing. This study aimed to identify the characteristics of the message framing effect in preschoolers and to examine the influence of age, issue involvement, and delivery method on this effect. A total of 180 participants ranging from 3–5 years of age were randomly assigned to one of four conditions with different combinations of frame types and delivery methods. The attitudes and behavioral intentions of the children toward the messages were measured via questionnaire. The results showed that the effect of persuasive messages increased with age (p < 0.001, ηp2 = 0.14) and was higher when the messages were more relevant to children (p < 0.001, ηp2 = 0.15). Furthermore, loss-framed messages were more effective than gain-framed messages for issues delivered verbally with pictures, while the opposite was true for issues delivered only verbally (p < 0.001, ηp2 = 0.06). These results enable developmental and theoretical discussions of the framing effect in children and provide practical data for improving the persuasion efficacy of message delivery in children.
A 0.17 pJ/bit 28 Gb/s/pin Single-Ended PAM-4 Transmitter for On-Chip Short-Reach Unterminated Channels
Authors: Park Soyeon , Kim Jintae
Published: August 2022

This paper presents the design of a single-ended four-level pulse-amplitude modulation (PAM-4) transmitter for an on-chip short-reach unterminated channel. To achieve multi-output generation, a local voltage buffer consisting of a diode-connected device and a leaker transistor is introduced. By charge-sharing between a local reservoir capacitor and an unterminated channel, the proposed transmitter generates mid-level output voltages without using the DC current, thereby realizing multi-level signaling without significantly increasing the static current. A prototype chip was fabricated by 28 nm CMOS process, and the transmitter exhibits an energy efficiency of 0.17 pJ/bit at 28 Gb/s/pin, which is state-of-the-art energy efficiency as a multi-level transmitter having a data rate beyond 20 Gb/s.
Supervisor Design for a Pressurized Reactor Unit in the Presence of Sensor and Actuator Faults
Authors: Koumboulis Fotis N. , Fragkoulis Dimitrios G. , Kalkanas Ioannis , Fragulis George F.
Published: August 2022

The preservation of the efficient functionality of a pressurized reactor unit in the presence of faults is the aim of the present paper. To satisfy this aim, a distributed supervisory control scheme, considering the possibility of system faults, was designed. Towards this aim, the models of the subsystems of the total pressurized reactor unit in the presence of sensor and actuator faults are developed, using finite deterministic automata. This is the first contribution of the paper. The desired performance of the unit was formulated in the form of rules guaranteeing the desired behavior of a pressurize–depressurize cycle and safety specifications. The rules were translated to six desired regular languages. The realization of these languages, in the form of supervisor automata, was accomplished. This is the second contribution of the paper. A modular supervisory design scheme, towards safety and tolerance in the presence of faults, was proposed and realized, and the properties of the proposed supervisors and the controlled automaton were proven. This is the third contribution of the paper. The complexity of each supervisor was computed. The efficiency of the supervisory design scheme was illustrated through simulations. A PLC implementation of the derived supervisors was proposed. The derived supervisors are suitable for implementation as function blocks.
Effects of Instant Messaging Related Technostress on Work Performance and Well-Being
Authors: Hurbean Luminita , Dospinescu Octavian , Munteanu Valentin , Danaiata Doina
Published: August 2022

The new era of hyper-communication has led organizations to increasingly adopt communications technologies such as instant messaging (IM) for better connections and improved work efficiency. The ongoing COVID-19 pandemic has exerted further pressure and many organizations increasingly depend on IM tools to manage remote workers. In this new context, negative workplace impacts of IM seem more plausible, because technology use causes technostress. In this paper, we propose a research model to analyze instant messaging usage effects on individual work performance and well-being by the means of relevant techno-stressors. The model was tested on a sample of 372 employees from Romanian organizations that use instant messaging at work. Econometric analysis relies on the Structural Equation Modelling methodology and uses SPSS AMOS. Our findings suggest that the usage of instant messaging significantly influences on perceived techno-complexity, techno-overload and techno-invasion, which in turn influences, to some extent, both individual work performance and well-being. Dividing the sample according to the type of work (teleworking and office work), we compared the results between these two groups and encountered some significant differences.
Enhancement of Underwater Images by CNN-Based Color Balance and Dehazing
Authors: Zhu Shidong , Luo Weilin , Duan Shunqiang
Published: August 2022

Convolutional neural networks (CNNs) are employed to achieve the color balance and dehazing of degraded underwater images. In the module of color balance, an underwater generative adversarial network (UGAN) is constructed. The mapping relationship between underwater images with color deviation and clean underwater images is learned. In the module of clarity improvement, an all-in-one dehazing model is proposed in which a comprehensive index is introduced and estimated by deep CNN. The third module to enhance underwater images adopts an adaptive contrast improvement method by fusing global and local histogram information. Combined with several underwater image datasets, the proposed enhancement method based on the three modules is evaluated, both by subjective visual effects and quantitative evaluation metrics. To demonstrate the advantages of the proposed method, several commonly used underwater image enhancement algorithms are compared. The comparison results indicate that the proposed method gains better enhancement effects for underwater images in different scenes than the other enhancement algorithms, since it can significantly diminish the color deviation, blur, and low contrast in degraded underwater images.
A Reconfigurable Modular Vehicle Control Strategy Based on an Improved Artificial Potential Field
Authors: Zhou Jing , Wang Jiacheng , He Jiazhong , Gao Jian , Yang Aixi , Hu Sideng
Published: August 2022

The reconfigurable modular vehicle group can transform into different configurations according to different requirements to be competent for various tasks and scenarios and to facilitate the utilization of robots in unstructured scenarios. Efficient and effective reconfiguration strategies and path planning are essential for improving the performance of modular vehicle groups. First, a multi-sensing four-wheel-drive Mecanum vehicle was built, which was equipped with UWB positioning (based on wireless carrier communication technology), communication based on ESP8266 modules, ultrasonic ranging and a magnetic structure. Second, concerning the indoor storage environment, a UWB two-way bilateral ranging and positioning system was designed, and the experimental accuracy for positioning could reach ±0.1 m. Third, a path planning strategy based on the improved artificial potential field method was adopted. According to the target configuration as well as the obstacle avoidance requirements, the motion space was converted into a gravitational field and a repulsive force field, and the vector superposition of the gradients was used for the path planning of each vehicle in turn. Depending on the reconfiguration command and the connection matrix, the magnetic structure would strengthen or disconnect the vehicle group configuration. Finally, the vehicle reconfiguration from the stochastic dispersion state to the target configuration and the transition between different configurations were accomplished using the proposed strategy in both simulations and experiments.
Saving Energy by Optimizing Warehouse Dock Door Allocation
Authors: Stanković Ratko , Rogić Kristijan , Šafran Mario
Published: August 2022

As energy consumption constantly gains importance, it has become one of the major issues in managing logistics systems. However, it is ranked against other company priorities, and the rationalization for investing in energy needs to be justified by the savings achieved. A solution for reducing energy consumption via electric forklifts for performing docking operations at distribution centers, which requires no investments in infrastructure or equipment, is outlined in this paper. The solution is based on optimizing inbound dock door allocation, and the energy savings are quantified using a simulation model. A case study of a local FMCG distributor’s logistics center was conducted to collect the data and information needed for modeling inbound docking operations and performing simulation experiments. The optimal dock door allocation was obtained using a linear programming method using an MS Excel spreadsheet optimizer (Solver), while the simulation of the docking operations was carried out using FlexSim simulation software. The experimental results show that the solution outlined in this paper enables savings in the electric energy consumption of forklifts of between 12.8% and 14.5%, compared to the empirical solution applied by the company in the case study. The intended contribution of this paper is not limited to presenting an applicable solution for energy savings in performing logistics processes, but also aims to draw the attention of more researchers and companies to the ways in which logistics processes are managed and performed in terms of raising energy efficiency.
Does Renewable Energy Sector Affect Industrialization-CO2 Emissions Nexus in Europe and Central Asia?
Authors: Mentel Grzegorz , Tarczyński Waldemar , Dylewski Marek , Salahodjaev Raufhon
Published: August 2022

Current research assesses the impact of industrialization and the renewable energy sector on greenhouse gas emissions, proxied by CO2 emissions in Europe and Central Asia. We rely on a two-step system GMM estimator on a sample of 48 countries over the period 2000–2018. Empirical results show that industrialization has a positive effect on CO2 emissions: a 10% increase in industry value added as % of GDP leads to an increase of 2.6% in CO2 emissions. In contrast, renewable energy mitigates CO2 emissions. Ten percentage points increase in renewable energy consumption reduces CO2 emissions per capita by 2.2%. The interaction term between renewable energy and industry value added is negative, suggesting that renewable energy consumption compensates for the negative effect of industrialization on environmental quality. Our main results also confirm the U-shaped inverted relationship between GDP per capita and CO2 emissions. Our study has a number of policy implications and avenues for future research.
A Review of the Impact of Battery Degradation on Energy Management Systems with a Special Emphasis on Electric Vehicles
Authors: Fanoro Mokesioluwa , Božanić Mladen , Sinha Saurabh
Published: August 2022

The increasing popularity of electric vehicles (EVs) has been attributed to their low-carbon and environmentally friendly attributes. Extensive research has been undertaken in view of the depletion of fossil fuels, changes in climatic conditions due to air pollution, and the goal of developing EVs capable of matching or exceeding the performance of today’s internal combustion engines (ICEs). The transition from ICE vehicles to EVs can reduce greenhouse gases significantly over a vehicle’s lifetime. Across the different types of EVs, the widespread usage of batteries is due to their high power density and steady output voltage, making them an excellent energy storage device (ESD). The current downsides of battery-powered electric vehicles include long recharge times, the impact of additional strain on the grid, poor societal acceptance due to high initial costs, and a lack of adequate charging infrastructure. Even more problematic is their short driving range when compared to standard ICE and fuel cell EVs. Battery degradation occurs when the capacity of a battery degrades, resulting in a reduction in travel range. This review article includes a description of battery degradation, degradation mechanisms, and types of degradation. A detailed investigation of the methods used to address and reduce battery degeneration is presented. Finally, some future orientation in terms of EV research is offered as vital guidance for academic and industrial partners.
Transition to Renewable Energy for Communities: Energy Storage Requirements and Dissipation
Authors: Michaelides Efstathios
Published: August 2022

The transition of residential communities to renewable energy sources is one of the first steps for the decarbonization of the energy sector, the reduction of CO2 emissions, and the mitigation of global climate change. This study provides information for the development of a microgrid, supplied by wind and solar energy, which meets the hourly energy demand of a community of 10,000 houses in the North Texas region; hydrogen is used as the energy storage medium. The results are presented for two cases: (a) when the renewable energy sources supply only the electricity demand of the community, and (b) when these sources provide the electricity as well as the heating needs (for space heating and hot water) of the community. The results show that such a community can be decarbonized with combinations of wind and solar installations. The energy storage requirements are between 2.7 m3 per household and 2.2 m3 per household. There is significant dissipation in the storage–regeneration processes—close to 30% of the current annual electricity demand. The entire decarbonization (electricity and heat) of this community will result in approximately 87,500 tons of CO2 emissions avoidance.