bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermodynamically consistent entropic late-time cosmological acceleration
Authors: Zamora D. J. , Tsallis C.
Published: August 2022

Entropic-force cosmology provides, in contrast with dark energy descriptions, a concrete physical understanding of the accelerated expansion of the universe. The acceleration appears to be a consequence of the entropy associated with the information storage in the universe. Since these cosmological models are unable of explaining the different periods of acceleration and deceleration unless a correction term is considered, we study the effects of including a subdominant power-law term within a thermodynamically admissible entropic-force model. The temperature of the universe horizon is obtained by a clear physical principle, i.e., requiring that the Legendre structure of thermodynamics is preserved. We analyze the various types of behaviors, and we compare the performance of thermodynamically consistent entropic-force models with regard to available supernovae data by providing appropriate constraints for optimizing alternative entropies and temperatures of the Hubble screen. The novelty of our work is that the analysis is based on a entropy scaling with an arbitrary power of the Hubble radius, instead of a specific entropy. This allows us to conclude on various models at once, compare them, and conserve the scaling exponent as a parameter to be fitted with observational data, thus providing information about the form of the actual cosmological entropy and temperature. We show that the introduced correction term is capable of explaining different periods of acceleration and deceleration in the late-time universe.
A Conditional Generative Adversarial Network and Transfer Learning-Oriented Anomaly Classification System for Electrospun Nanofibers
Authors: Ieracitano Cosimo , Mammone Nadia , Paviglianiti Annunziata , Morabito Francesco Carlo
Published: August 2022

This paper proposes a generative model and transfer learning powered system for classification of Scanning Electron Microscope (SEM) images of defective nanofibers (D-NF) and nondefective nanofibers (ND-NF) produced by electrospinning (ES) process. Specifically, a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic D-NF/ND-NF SEM images. A transfer learning-oriented strategy is also proposed. First, a Convolutional Neural Network (CNN) is pre-trained on real images. The transfer-learned CNN is trained on synthetic SEM images and validated on real ones, reporting accuracy rate up to 95.31%. The achieved encouraging results endorse the use of the proposed generative model in industrial applications as it could reduce the number of needed laboratory ES experiments that are costly and time consuming.
Reliability analysis of interdependent hypergraph network under different attack strategies
Authors: Peng Hao , Xie Ziyi , Zhao Dandan , Zhong Ming , Han Jianmin , Wang Wei
Published: August 2022

Nodes usually cooperate to form groups and survive or fail in real-world systems. Researchers typically consider the interdependence between node groups in studying the interdependent network. This paper studies the robustness of interdependent hypergraph networks under different attack strategies. According to the characteristics of the network model, we propose a series of target attack strategies and compare the destructive effect of these strategies on the network. Second, we analyze the impact of the random edge removal strategy on the robustness of hypergraph networks under different edge removal ratios. Finally, we propose four target-node edge removal strategies and compare their destructive effects on the network at the same edge removal ratios. Simulation results show that target attack and edge removal strategies can appreciably reduce the robustness of interdependent hypergraph networks and accelerate the networks’ collapse.
An Expansion Planning Approach for Intelligent Grids with Speculative Parallelism
Authors: Wang Zhoukai , Qi Jiaqi , Ma Weigang , Lv Yang , Yang Dongfang
Published: August 2022

Smart grid planning is a standard method used to reduce the net loss of distribution networks and improve the economic efficiency of power systems. As the distribution networks expand, the scale is getting more complex, leading to low efficiency and a long planning time when using traditional methods to implement new routes. To solve this problem, this paper proposed a new approach for intelligent grid expansion planning with speculative particle swarm optimization. First, the expansion planning model for the smart grid is established based on particle swarm optimization from the classical control system. Secondly, the model is parallelized with the speculative parallelism technique to overcome the influence of the internal control dependency and get rid of the original processing order. Finally, the parallel model is implemented on a distributed computing platform to improve the planning efficiency for complex intelligent grids significantly. Experiments show that the proposed approach can improve the efficiency by about 40% in an Apache Spark cluster consisting of 20 nodes compared with the conventional ones. Moreover, the proposed method can also fully utilize the distributed cluster’s computing resources.
Low-Resolution Face Recognition and Sports Training Action Analysis Based on Wireless Sensors
Authors: An Hongjun , Gao Heng
Published: August 2022

This paper constructs a low-resolution model for face recognition and sports training actions based on wireless sensors. The model obtains the distribution of the information size in the face image by calculating the image entropy value, and assigns different weights according to the size of the information to perform face recognition calculation, so that the original module-based algorithm is simply based on image segmentation into one based on entropy. The size of the value is divided into blocks, which solves the problem of computational quantification of category information. In the test stage, the traditional orthogonal matching pursuit algorithm is used to solve the coding coefficients, and the excellent classification and recognition results are obtained by calculating the intra-class matrix of the face image and the inter-class matrix of the sports training action image. Methods that perform well on classification problems further improve face recognition rates. The specific processing process is to add Gaussian noise, salt and pepper noise to the input face image and reduce the size of the face image in the input image, so that the improved algorithms are improved. The experimental results show that the high-efficiency resolution sensing technology is used to learn the sports training actions corresponding to the two modalities, and the matrix coefficient between the obtained high-resolution modal and low-resolution modal images reaches 0.971, and the iteration rate is improved by 71.5%, effectively promoting the high recognition rate of faces and actions.
Artificial Intelligence Empowered Traffic Control for Internet of Things with Mobile Edge Computing
Authors: Lei Qi
Published: August 2022

Mobile edge computing (MEC) is one of the efficient technologies to provide satisfying quality of experience (QoE) for emerging computation-intensive applications in internet of things (IoT). However, some new challenges will be encountered when MEC is applied in a large-scale IoT with massive access devices or heavy traffic loads such as load balancing and traffic offloading. Aiming at the solution of these problems, this paper proposes a learning-based traffic control architecture for IoT with MEC. Moreover, a deep-learning-based load balancing framework is developed to control user association in IoT. The user association is determined at each IoT access points (IAP) by the deep neural network (DNN), which is the duplication of the well-trained DNN with the global network information. In addition, we propose a reinforcement-learning-based partial traffic offloading scheme to reduce the traffic origination. The IoT devices are able to independently decide its offloading radio according to the channel quality information, service requirement, and workload of the IAP. Simulation results indicate that the proposed deep-learning-based load balancing scheme is able to achieve uniform traffic distribution, and meanwhile our partial offloading policy can significantly reduce the network traffic.
Invariant measures in simple and in small theories
Authors: Chernikov Artem , Hrushovski Ehud , Kruckman Alex , Krupiński Krzysztof , Moconja Slavko , Pillay Anand , Ramsey Nicholas
Published: August 2022

We give examples of (i) a simple theory with a formula (with parameters) which does not fork over [Formula: see text] but has [Formula: see text]-measure 0 for every automorphism invariant Keisler measure [Formula: see text] and (ii) a definable group [Formula: see text] in a simple theory such that [Formula: see text] is not definably amenable, i.e. there is no translation invariant Keisler measure on [Formula: see text]. We also discuss paradoxical decompositions both in the setting of discrete groups and of definable groups, and prove some positive results about small theories, including the definable amenability of definable groups.
A Geometrically Exact Beam Finite Element for Non-Prismatic Strip Beams: The 2D Case
Authors: Gonçalves Rodrigo
Published: August 2022

In this paper, a new finite element for 2D non-prismatic (curved and tapered) elastic beams with narrow rectangular cross-section is proposed. The element is geometrically exact, hence capable of handling large displacements and finite rotations, and allows for cross-section arbitrary warping through the inclusion of additional degrees of freedom (DOFs). These additional DOFs make it possible to capture the non-standard shear stress distributions that are developed in tapered members. All expressions required to implement the proposed finite element are derived and written in a simple vector/matrix form. To assess the accuracy and demonstrate the capabilities of the proposed element, several numerical examples are presented. For comparison purposes, results obtained with refined meshes of standard 2D finite elements are also shown. It is concluded that the proposed element leads to very accurate results with a small computational cost.
Unique decompositions into w-ideals for strong Mori domains
Authors: Ay Saylam Başak , Gürbüz Ezgi , Hamdi Haleh
Published: August 2022

A commutative ring [Formula: see text] has the unique decomposition into ideals (UDI) property if, for any [Formula: see text]-module that decomposes into a finite direct sum of indecomposable ideals, this decomposition is unique up to the order and isomorphism classes of the indecomposable ideals. In [P. Goeters and B. Olberding, Unique decomposition into ideals for Noetherian domains, J. Pure Appl. Algebra 165 (2001) 169–182], the UDI property has been characterized for Noetherian integral domains. In this paper, we aim to study the UDI-like property for strong Mori domains; domains satisfying the ascending chain condition on [Formula: see text]-ideals.
COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins
Authors: Abbasi Wajid Arshad , Anjam Asma , Khalil Sadia , Andleeb Saiqa , Bibi Maryum , Abbas Syed Ali
Published: August 2022

Glycoproteins play an important and ubiquitous role in many biological processes such as protein folding, cell-to-cell signaling, invading microorganism infection, tumor metastasis, and leukocyte trafficking. The key mechanism of glycoproteins must be revealed to model and refine glycosylated protein recognition, which will eventually assist in the design and discovery of carbohydrate-derived therapeutics. Experimental procedures involving wet-lab experiments to reveal glycoproteins are very time-consuming, laborious, and highly costly. However, costly and tedious experimental procedures can be assisted by ranking the most probable glycoproteins through computational methods with improved accuracy. In this study, we have proposed a novel machine learning-based predictive model for glycoproteins identification. Our proposed model is based on sequence-derived structural descriptors (SDSD) that fill the gap of unavailability of protein 3D structures and lack of accuracy in sequence information alone. Through a series of simulation studies, we have shown that our proposed model gives state-of-the-art generalization performance verified through various machine learning-centric and biologically relevant techniques and metrics. Through data mining in this study, we have also identified the role of descriptors in determining glycoproteins. Python-based standalone code together with a webserver implementation of our proposed model (COYOTE: identifiCation Of glYcoprOteins Through sEquences) is available at the URL: https://sites.google.com/view/wajidarshad/software .
Covariance kernel of linear spectral statistics for half-heavy tailed Wigner matrices
Authors: Lodhia Asad , Maltsev Anna
Published: August 2022

In this paper, we analyze the covariance kernel of the Gaussian process that arises as the limit of fluctuations of linear spectral statistics for Wigner matrices with a few moments. More precisely, the process we study here corresponds to Hermitian matrices with independent entries that have [Formula: see text] moments for [Formula: see text]. We obtain a closed form [Formula: see text]-dependent expression for the covariance of the limiting process resulting from fluctuations of the Stieltjes transform by explicitly integrating the known double Laplace transform integral formula obtained in [F. Benaych-Georges and A. Maltsev, Fluctuations of linear statistics of half-heavy-tailed random matrices, Stochastic Process. Appl. 126(11) (2016) 3331–3352]. We then express the covariance as an integral kernel acting on bounded continuous test functions. The resulting formulation allows us to offer a heuristic interpretation of the impact the typical large eigenvalues of this matrix ensemble have on the covariance structure.
Effervescent Progesterone Vaginal Tablet Mono-Administration Demonstrated Comparable Pregnancy Rate Across the Different Serum Progesterone Levels on the Day of Embryo Transfer in SHIFT Study
Authors: Fujiwara Toshihiro , Kusumi Maki , Utsunomiya Takafumi , Nomiyama Mari , Yanagida Kaoru , Watanabe Yoshitsugu , Yamaguchi Yukiharu , Tanaka Atsushi
Published: August 2022

Background: Hormone replacement (HR)-frozen thawed embryo transfer (FET) is preferentially used in Japan. HR-FET needs supplementation of progesterone, but there are few data about the efficacy and safety of vaginal progesterone tablet. The aim of this study is to investigate the efficacy of vaginal progesterone tablet in HR-FET cycle in Japanese patients.

Methods: This study was multicenter, single arm, prospective study. Patients with infertility were administered 300 mg/day of vaginal progesterone tablet for luteal phase support in HR-FET cycle from 2015 to 2016.

Results: Main outcomes were available for 344 patients, of which 49 in the cleavage stage ET group and 295 in the blastocyst ET group. Ongoing pregnancy rate were 10.2% in cleavage stage ET group and 28.1% in blastocyst ET group. As for the secondary outcomes in the blastocyst ET group, embryo quality of blastocyst, the number of past ET, and maternal age were significantly associated with pregnancy rate at 2 weeks after progesterone administration. Mean serum progesterone level was 12.1 ± 5.3 ng/mL at the day of ET. There was no relationship between the serum progesterone level on ET day and either of pregnancy rates at 2, 4, and 6 weeks after progesterone administration.

Conclusions: The efficacy of monotherapy using 300 mg/day of vaginal progesterone tablet in HR-FET was shown and the serum progesterone level on ET day did not affect pregnancy rate. (UMIN000021983)
Electroosmosis Augmented MHD Third-Grade Fluid with Slip and Variable Properties: An Application for Blood Flow in Arteries
Authors: Choudhari Rajashekhar , Vaidya Hanumesh , Prasad Kerehalli Vinayaka , Gulab Rathod Kirankumar , Guedri Kamel , Rehman Aysha , Galal Ahmed M.
Published: August 2022

The electroosmotic force effect on the peristaltic motion of the third-grade fluid is considered in a uniform channel. The governing equations that supplement the flow are designed for long wavelengths and low Reynolds numbers. Solutions are obtained for velocity, temperature, concentration, and trapping by considering the variable liquid properties for analyzing the various parameter effects. These effects are depicted through graphs and the relevance is discussed. The variable fluid properties have a declining impact on the velocity and temperature fields. Increasing the Helmholtz–Smoluchowski velocity values decreases the velocity field. Temperature decreases as the Deborah number increases. The velocity slip characteristics rise, and the trapping bolus’s size shrinks. The results of this paper may be beneficial in understanding the control of microvascular transport in the time of fractionation of blood into plasma and erythrocytes.
Internal Family Systems Therapy (IFS): A Compassionate Approach to Therapy
Authors: Mok Cecilia Hwee Bee
Published: August 2022

Internal Family Systems Therapy (IFS) has gained recognition globally as an effective psychotherapy and an evidence-based practice. It has seen widespread acceptance and application not only in individual, couple, and family therapy but also in diverse fields such as education, coaching and more. The IFS model was developed by Dr Richard Schwartz based on the concepts of systems therapy and multiplicity of the mind. The non-pathologising approach of IFS views the psychiatric symptoms that a client comes into counselling with, as protectors or parts forced into extreme roles caused by circumstances and experiences for the positive intention of maintaining functioning, despite negative consequences. The approach of IFS reflects compassion and its goal is to restore balance to the client’s inner system by unburdening the parts of their extreme roles, beliefs or feelings, and to restore the client’s Self to leadership. IFS offers an alternative neuroscience-informed intervention model to the stage model of treatment for trauma. The contribution of IFS is not only in the interventions itself but also in advancing therapeutic relationship and for self-therapy. This paper describes the application of IFS therapy to individuals by providing vignettes to highlight the impact of its non-pathologising approach on the client as well as the therapist in the counselling process by viewing parts from a lens of self-compassion.
Data Completeness and Complex Semantics in Conceptual Modeling: The Need for a Disaggregation Construct
Authors: Li Yuanxia , Currim Faiz , Ram Sudha
Published: August 2022

Conceptual modeling is important for developing databases that maintain the integrity and quality of stored information. However, classical conceptual models have often been assumed to work on well-maintained and high-quality data. With the advancement and expansion of data science, it is no longer the case. The need to model and store data has emerged for settings with lower data quality, which creates the need to update and augment conceptual models to represent lower-quality data. In this paper, we focus on the intersection between data completeness (an important aspect of data quality) and complex class semantics (where a complex class entity represents information that spans more than one simple class entity). We propose a new disaggregation construct to allow the modeling of incomplete information. We demonstrate the use of our disaggregation construct for diverse modeling problems and discuss the anomalies that could occur without this construct. We provide formal definitions and thorough comparisons between various types of complex constructs to guide future application and prove the unique interpretation of our newly proposed disaggregation construct.
The Many Facets of Data Equity
Authors: Jagadish H. , Stoyanovich Julia , Howe Bill
Published: August 2022

Data-driven systems can induce, operationalize, and amplify systemic discrimination in a variety of ways. As data scientists, we tend to prefer to isolate and formalize equity problems to make them amenable to narrow technical solutions. However, this reductionist approach is inadequate in practice. In this article, we attempt to address data equity broadly, identify different ways in which it is manifest in data-driven systems, and propose a research agenda.
Voltage Sensor Implementations for Remote Power Attacks on FPGAs
Authors: Moini Shayan , Deric Aleksa , Li Xiang , Provelengios George , Burleson Wayne , Tessier Russell , Holcomb Daniel
Published: August 2022

This article presents a study of two types of on-chip FPGA voltage sensors based on ring oscillators (ROs) and time-to-digital converter (TDCs), respectively. It has previously been shown that these sensors are often used to extract side-channel information from FPGAs without physical access. The performance of the sensors is evaluated in the presence of circuits that deliberately waste power, resulting in localized voltage drops. The effects of FPGA power supply features and sensor sensitivity in detecting voltage drops in an FPGA power distribution network (PDN) are evaluated for Xilinx Artix-7, Zynq 7000, and Zynq UltraScale+ FPGAs. We show that both sensor types are able to detect supply voltage drops, and that their measurements are consistent with each other. Our findings show that TDC-based sensors are more sensitive and can detect voltage drops that are shorter in duration, while RO sensors are easier to implement because calibration is not required. Furthermore, we present a new time-interleaved TDC design that sweeps the sensor phase. The new sensor generates data that can reconstruct voltage transients on the order of tens of picoseconds.
The Pricing and Ownership of US Green Bonds
Authors: Baker Malcolm , Bergstresser Daniel , Serafeim George , Wurgler Jeffrey
Published: August 2022

We review the pricing and ownership of green bonds, whose proceeds are used for environmentally focused purposes. After presenting an overview of the literature on green securities and green bonds in particular, we summarize the US corporate and municipal green bond markets. Green municipal bonds provide the best opportunity for detailed empirical study of how pricing and ownership differ from those of ordinary bonds. Green bonds are issued at a small premium of several basis points over similar ordinary bonds except when they are issued simultaneously with ordinary bonds from the same issuer; in that situation, a premium emerges over time on the secondary market. Green bonds, especially small or nearly riskless ones, are also more closely held than ordinary bonds. These facts are consistent with a simple framework that incorporates assets with nonpecuniary utility.
Entropy Profiling for the Diagnosis of NCA/Gr-SiOx Li-Ion Battery Health
Authors: Wojtala Malgorzata E. , Zülke Alana A. , Burrell Robert , Nagarathinam Mangayarkarasi , Li Guanchen , Hoster Harry E. , Howey David A. , Mercer Michael P.
Published: August 2022

Graphite-silicon (Gr-Si) blends have become common in commercial Li-ion battery negative electrodes, offering increased capacity over pure graphite. Lithiation/delithiation of the silicon particles results in volume changes, which may be associated with increased hysteresis of the open circuit potential (OCP). The OCP is a function of both concentration and temperature. Entropy change measurement—which probes the response of the OCP to temperature—offers a unique battery diagnostics tool. While entropy change measurements have previously been applied to study degradation, the implications of Si additives on the entropy profiles of commercial cells have not been explored. Here, we use entropy profiling to track ageing markers in the same way as differential voltage analysis. In addition to lithiation/delithiation hysteresis in the OCP of Gr-Si blends, cells with Gr-Si anodes also exhibit differences in entropy profile depending on cycling direction, reflecting degradation-related morphological changes. For cycled cells, entropy change decreased during discharge, likely corresponding to graphite particles breaking and cracking. However, entropy change during charge increased with cycling, likely due to the volume change of silicon. Over a broad voltage range, these combined effects led to the observed rise in entropy hysteresis with age. Conversely, for calendar aged cells entropy hysteresis remained stable.
Inflammatory diseases in hematology: a review
Authors: Henrie Ryan , Cherniawsky Hannah , Marcon Krista , Zhao Eric J. , Marinkovic Angelina , Pourshahnazari Persia , Parkin Stephen , Chen Luke Y. C.
Published: August 2022

Hematopoietic cells are instrumental in generating and propagating protective inflammatory responses to infection or injury. However, excessive inflammation contributes to many diseases of the blood, bone marrow, and lymphatic system. We review three clinical categories of hematological inflammatory diseases in which recent clinical and translational advances have been made. The first category is monogenic inflammatory diseases. Genotype-driven research has revealed that previously mysterious diseases with protean manifestations are characterized by mutations that may be germline (e.g., deficiency of ADA2 or GATA2 deficiency) or somatic [e.g., vacuoles, enzyme E1, X-linked, autoinflammatory, somatic (VEXAS) syndrome]. The second category is the cytokine storm syndromes, including hemophagocytic lymphohistiocytosis, and Castleman disease. Cytokine storm syndromes are characterized by excessive production of inflammatory cytokines including interleukin-6 and interferon-γ, causing end-organ damage and high mortality. Finally, we review disorders associated with monoclonal and polyclonal hypergammaglobulinemia. The serum protein electrophoresis (SPEP) is typically ordered to screen for common diseases such as myeloma and humoral immunodeficiency. However, monoclonal and polyclonal hypergammaglobulinemia on SPEP can also provide important information in rare inflammatory diseases. For example, the autoinflammatory disease Schnitzler syndrome is notoriously difficult to diagnose. Although this orphan disease has eluded precise genetic or histological characterization, the presence of a monoclonal paraprotein, typically IgM, is an obligate diagnostic criterion. Likewise, polyclonal hypergammaglobulinemia may be an important early, noninvasive diagnostic clue for patients presenting with rare neoplastic diseases such as Rosai-Dorfman disease and angioimmunoblastic T-cell lymphoma. Applying these three categories to patients with unexplained inflammatory syndromes can facilitate the diagnosis of rare and underrecognized diseases.
Differential effects of high-altitude exposure on markers of oxidative stress, antioxidant capacity, and iron profiles
Authors: Rytz Chantal L. , Pun Matiram , Mawhinney Jamie A. , Mounsey Craig A. , Mura Mathlide , Martin Agnès , Pialoux Vincent , Hartmann Sara E. , Furian Michael , Rawling Jean M. , Lopez Ivan , Soza Daniel , Moraga Fernando A. , Lichtblau Mona , Bader Patrick R. , Ulrich Silvia , Bloch Konrad E. , Frise Matthew C. , Poulin Marc J.
Published: August 2022

High-altitude (HA) exposure may stimulate significant physiological and molecular changes, resulting in HA-related illnesses. HA may impact oxidative stress, antioxidant capacity, and iron homeostasis, yet it is unclear how both repeated exposure and HA acclimatization may modulate such effects. Therefore, we assessed the effects of weeklong repeated daily HA exposure (2,900–5,050 m) in altitude-naïve individuals ( n = 21 individuals, 13 females, mean ± SD, 25.3 ± 3.7 yr) to mirror the working schedule of HA workers ( n = 19 individuals, all males, 41.1 ± 9.4 yr) at the Atacama Large Millimeter Array (ALMA) Observatory (San Pedro de Atacama, Chile). Markers of oxidative stress, antioxidant capacity, and iron homeostasis were measured in blood plasma. Levels of protein oxidation ( P < 0.001) and catalase activity ( P = 0.023) increased and serum iron ( P < 0.001), serum ferritin ( P < 0.001), and transferrin saturation ( P < 0.001) levels decreased with HA exposure in both groups. HA workers had lower levels of oxidative stress, and higher levels of antioxidant capacity, iron supply, and hemoglobin concentration as compared with altitude-naïve individuals. On a second week of daily HA exposure, changes in levels of protein oxidation, glutathione peroxidase, and nitric oxide metabolites were lower as compared with the first week in altitude-naïve individuals. These results indicate that repeated exposure to HA may significantly alter oxidative stress and iron homeostasis, and the degree of such changes may be dependent on if HA is visited naïvely or routinely. Further studies are required to fully elucidate differences in HA-induced changes in oxidative stress and iron homeostasis profiles among visitors of HA.
A Review of Hybrid Process Development Based on Electrochemical and Advanced Oxidation Processes for the Treatment of Industrial Wastewater
Authors: Asaithambi Perumal , Yesuf Mamuye Busier , Govindarajan Rajendran , Hariharan N.M. , Thangavelu Perarasu , Alemayehu Esayas
Published: August 2022

Nowadays, increased human activity, industrialization, and urbanization result in the production of enormous quantities of wastewater. Generally, physicochemical and biological methods are employed to treat industrial effluent and wastewater and have demonstrated high efficacy in removing pollutants. However, some industrial effluent and wastewater contain contaminants that are extremely difficult to remove using standard physicochemical and biological processes. Previously, electrochemical and hybrid advanced oxidation processes (AOP) were considered a viable and promising alternative for achieving an adequate effluent treatment strategy in such instances. These processes rely on the production of hydroxyl radicals, which are highly reactive oxidants that efficiently break down contaminants found in wastewater and industrial effluent. This review focuses on the removal of contaminants from industrial effluents and wastewater through the integration of electrochemical and advanced oxidation techniques. These processes include electrooxidation, electrocoagulation/electroflocculation, electroflotation, photo-Fenton, ozone-photo-Fenton, sono-photo-Fenton, photo-electro-Fenton, ozone/electrocoagulation, sono-electrocoagulation, and peroxi/photo/electrocoagulation. The data acquired from over 150 published articles, most of which were laboratory experiments, demonstrated that the hybrid process is more effective in removing contaminants from industrial effluent and wastewater than standalone processes.
Toxoplasma gondii Infection and Threatened Abortion in Women from Northern Peru
Authors: Valladares-Garrido Mario J. , Failoc-Rojas Virgilio E. , Ichiro-Peralta C. , Astudillo-Rueda David , Silva-Díaz Heber
Published: August 2022

Introduction. Toxoplasma gondii infection can cause important complications during pregnancy. Threatened abortion may be a late indicator for infection in settings with high prevalence of toxoplasmosis. We aimed to determine the association between T. gondii infection and threatened abortion in women from northern Peru. Methods. We conducted a secondary analysis of a cross-sectional study in pregnant women from a hospital and a rural community in Lambayeque, Peru. Exposure variable was serological diagnosis of toxoplasmosis, defined as the demonstration of either IgM or IgG antibodies against T. gondii. Outcome variable was threatened abortion, defined as the diagnosis of bloody vaginal discharge or bleeding during the first half of pregnancy. Prevalence ratios were estimated in simple and multiple regression analyses. Results. Of 218 pregnant women, 35.8% presented positive serology for T. gondii and 14.7% had threatened abortion in their current pregnancy. Pregnant women with positive T. gondii infection had 2.45-fold higher frequency of threatened abortion (PR: 2.45, 95% CI: 1.15-5.21). In addition, the frequency of threatened abortion decreased by 9% for each additional year of age (PR: 0.91, 95% CI: 0.86-0.97). A previous history of threatened abortion also showed a higher frequency of threatened abortion (PR: 5.22, 95% CI: 2.45-11.12). Conclusions. T. gondii infection is associated with threatened abortion. An early age of pregnancy and a previous history of abortion are also associated with this condition.
A Systematic Review of Virtual Reality-Based Education Research Using Latent Dirichlet Allocation: Focus on Topic Modeling Technique
Authors: Kim Daeseok , Im Tami
Published: August 2022

Using virtual reality in the context of education is becoming important since this technology enhances learners’ motivation and performance with transfer of learning, problem-solving skills, educational equity, and multisensory learning. Along with the 4th Industrial Revolution and COVID-19, the attention on virtual reality has been increased again. Some studies reviewed the trends of research on virtual reality-based education, mostly focusing on specific subject matters or areas. The purpose of the study is to investigate the change of research trends in the virtual reality-based education field by implementing the topic modeling analysis based on latent Dirichlet allocation (LDA) technique with 6,755 articles published in the last 30 years (between 1992 and 2022). As a result of this study, it was revealed that the research on virtual reality-based education was clearly divided into the following four periods; 1992–2011, 2012–2016, 2017–2019, and 2020–2022. The main topics for each period were suggested. Here are three major findings of this study. First, it is identified that the weight of “virtual reality for learning and teaching” has increased in recent years. Second, conversely, it is identified that the proportion of “virtual reality in medical education” has decreased relatively recently. It means it is possible to interpret that the proportion of other topics has relatively increased. Third, the topics of “virtual reality education platform” and “virtual reality-based education in rehabilitation” continue to maintain a certain weight. Limitations of this study and further research suggestions are provided based on the results of the study.
TAB2 Promotes the Biological Functions of Head and Neck Squamous Cell Carcinoma Cells via EMT and PI3K Pathway
Authors: Liu Huijuan , Zhang Hui , Fan Haidong , Tang Su , Weng Junquan
Published: August 2022

Background. Transforming growth factor β1-activated kinase 1 binding protein 2 (TAB2) mediates a variety of biological processes through activated nuclear factor κ-light-chain-enhancer of activated B cell (NF-κB) signaling pathways. TAB2 has been reported to be upregulated in a variety of tumors. However, little is known about its potential role in oral squamous cell carcinoma (OSCC). Material and Methods. Patients’ clinicopathological and transcription data were obtained from The Cancer Genome Atlas (TCGA) database. Immunohistochemistry staining was used to determine TAB2 expression in OSCC tissues (IHC). The expression of TAB2 in OSCC cell lines was detected by western blotting. The CCK-8 test and flow cytometry assay were utilized to evaluate cell proliferation, apoptosis, and cell cycle in OSCC cell lines. Enrichment analysis and identification of predicted signaling pathways were performed by Gene Ontology and KEGG analysis. Finally, the expression of downstream signal molecules was performed using western blotting to validate the mechanism investigations. Results. TAB2 expression level was aberrantly upregulated in OSCC patients. TAB2 expression was shown to be inversely associated to prognosis. The phenotypic of OSCC cells was considerably impacted by TAB2. OSCC cells with deleted TAB2 exhibit decreased proliferation and increased apoptosis. Additionally, OSCC progression is aided by TAB2 overexpression. Further mechanism studies showed that TAB2 could regulate the progression of OSCC by mediating the upregulation of EMT and PI3K-AKT signaling pathways. Conclusion. This study sheds light on the carcinogenic role of TAB2 in OSCC and provides a potential therapeutic strategy.
Malnutrition, Eating Habits, Food Consumption, and Risk Factors of Malnutrition among Students at the University of Maroua, Cameroon
Authors: Ntentie Francoise Raissa , Angie Mbong Mary-Ann , Tonou Tchuente Boris Ronald , Biyegue Nyangono Christine Fernande , Wandji Nguedjo Maxwell , Bissal Cedric , Souavourbe Palouma , Avom-Me Mbida Franck , Enyong Oben Julius
Published: August 2022

Context and objective. The time spent by young people at the university is generally marked by a profound transition in lifestyle and eating habits, which exposes them to several risk factors for diseases that will develop later such as chronic diseases related to diet. The objective of this study was to assess malnutrition and identify the particular risk factors for malnutrition among students at the University of Maroua. Methods. Three hundred and thirty students of both sexes, aged between 17 and 35 years old, were recruited from January to February 2018. Anthropometric parameters (weight, height, waist and hip circumference, and BMI) were measured, and an eating and lifestyle questionnaire was administered to each participant, as well as a 24-hour dietary recall. Results. The mean age of participants was 23.95 ± 3.67 years and BMI was 22.19 ± 2.78 kg/m2. Approximately 6.1% were underweight, 12.1% overweight, and 0.9% obese, and all age groups were affected. Concerning eating habits, low protein, fruit, and vegetable consumption were recorded among students. Cereals were the main source of carbohydrates consumed. Besides, 6% of them had a low dietary intake, 21% had a poorly diversified diet, and 2% were highly food insecure. The students’ daily macronutrient intake was within the recommended reference values, except for carbohydrates, which exceeded and represented 62.69± 13.84% of daily energy intake. Also, only 32.2% of respondents had adequate energy intake. A poorly diversified diet was associated with a high incidence of overweight. Conclusions. Both forms of malnutrition are indeed present among the students of the University of Maroua, and nutritional education for this young segment of the population will be essential to prevent complications associated with malnutrition in the working life.
Ownership, Corporate Governance, and Bank Performance in Iran
Authors: Asadi Abdorreza , Ramezankhani Mahyar
Published: August 2022

An efficient banking system with the right monetary policy by controlling liquidity and inflation and directing resources to productive economic activities plays an essential role in economic development. However, banks’ performance is influenced by various political, economic, managerial, and social factors, and the study of these factors has been considered the topic of interest to researchers. This paper uses the structural equation modeling method to investigate the effect of ownership structure and corporate governance on listed banks’ performance in the Tehran Stock Exchange from 2011 to 2017. Based on the results, ownership structure dimensions have a relatively insignificant impact on corporate governance. However, the financial performance dimension has a statistically significant negative effect. The results also indicate that corporate governance is significantly associated with a positive effect on financial performance. Consequently, the results indicate that corporate governance may mitigate the negative impact that ownership structure dimensions may have on bank financial performance.
Network Pharmacology Analysis and Experimental Validation to Explore the Anti-inflammatory Mechanism of Asiatic Acid on Alcoholic Steatohepatitis
Authors: Jiang Tao , Xu Jinhong , Lu Yang , Chen Xiaojin , Li Yongxia
Published: August 2022

Objective. The mechanism of action of asiatic acid (AA) on alcoholic steatohepatitis (ASH) was investigated using network pharmacology and experiments. Methods. Through data retrieval, network construction, and enrichment analysis, the potential mechanism of AA in the treatment of alcoholic steatohepatitis was explored. Animal and cell models were established in this study. Animal Model. The mouse model was divided into six groups: normal group; model group; low, medium, and high AA group; and silibinin-positive group. Cell Model. An in vitro inflammatory model of RAW264.7 cells was established by alcohol stimulation. Results. Compared with the model group, the low, medium, and high AA group showed decreased serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglyceride (TG), and total cholesterol (T-CHO). The inflammatory factor tumor necrosis factor alpha (TNF-α), interleukin 1 beta (IL-1β), and interleukin 6 (IL-6) in a dose-dependent manner were decreased. In addition, hematoxylin-eosin staining showed that liver tissue damage and inflammatory cell infiltration in mice were significantly reduced with increasing doses. Further, oil red staining showed that lipid accumulation in hepatocytes in the low, medium, and high AA group was significantly reduced, with increasing dose. In addition, in the cellular model, real-time reverse transcriptase-polymerase chain reaction (Real-Time RT-PCR) and enzyme-linked immunosorbent assay (ELISA) results showed that AA could alleviate alcohol-induced cellular inflammation, while western blot and immunofluorescence results showed that AA might alleviate alcohol-induced cellular inflammation by inhibiting the nuclear factor-κB (NF-κB) pathway. Conclusion. This study provides multiple lines of evidence that asiatic acid may alleviate alcoholic hepatitis in mice by modulating the NF-κB pathway.
Application of Cloud Computing and GIS Based on Internet of Things in Oil and Gas Storage and Transportation Production Management and Safety Monitoring and Early Warning System
Authors: Wang Kai
Published: August 2022

The purpose of this study is to design an information management system, which can be used for oil and gas pipeline path planning, oil and gas production scheduling, oil and gas data statistical analysis, and other functions. In this study, a production management information system for oil and gas storage and transportation based on the Internet of Things and GIS technology is proposed. Through the integration of Internet and GIS technology, it can accurately analyze the path planning of oil and gas pipelines. The experiment proves the reliability of the system, and in the aspect of path planning, the simulated path of the system will be 20–30 km shorter than the original laying path, especially for long-distance pipeline laying, which can be reduced by about 30 km.
Research on the Spatial-Temporal Coupling Effect of Industrial Structure Upgrading and Human Resources in Western China
Authors: Meng Tian
Published: August 2022

Industrial development and human resources matching can better promote economic growth. The mismatch, incoordination, and noninteraction between the upgrading of the industrial structure and human resources in the western region have severely restricted the speed and quality of regional economic development. Based on the in-depth analysis of the coupling effect mechanism of industrial structure upgrading and human resources in the western region, this paper constructs an evaluation index system for industrial structure upgrading and human resources in the western region. The coupling coordination degree model was used to calculate and analyze the coupling degree and coupling coordination degree between the industrial structure upgrading and human resources in western China. The results show that from 2009 to 2018, the industrial structure upgrade and the level of human resources in the western regions continued to rise. Before 2015, the level of industrial structure was higher than the level of human resources, and then the opposite trend appeared. From the perspective of regional distribution, the coupling and coordination of the two systems in western regions are quite different. The upgrading of industrial structure and the coordination of human resources in the three regions of Shaanxi, Chongqing, and Sichuan are generally higher than those in other regions, but there was still a gap from the high-quality coupling state. Therefore, an in-depth understanding of the coupling stage, current situation, and problems of industrial structure upgrading and human resources in western regions will help to further promote the high-quality economic development of western regions.
Estimation of Sound Speed Profiles Using a Random Forest Model with Satellite Surface Observations
Authors: Ou Zhenyi , Qu Ke , Liu Chen
Published: August 2022

Sound speed profile (SSP) inversion is usually performed by linear statistical regression, such as the single empirical orthogonal function regression (sEOF-r) model. However, due to the complex dynamic activities of the ocean, the relationship between parameters is not strictly linear, often resulting in an unsatisfactory inversion result. In this study, an algorithm based on the random forest (RF) integrated learning model, for SSP inversion, was proposed. Using the sea surface temperature anomaly (SSTA) and sea surface height anomaly (SSHA) data, the sound speed profile of the upper 1000 m layer in the South China Sea was reconstructed, and its accuracy was evaluated through the root mean square error (RMSE). The accuracy of the evaluation demonstrated that the RF model proposed here could reconstruct the SSP in the upper 1000 m layer better than the sEOF-r can. Compared with the latter, the average reconstruction accuracy of the RF model was improved by 0.56 m/s. The linear regression of the sEOF-r model fell short of expectations in the regression between surface and subsurface parameters. By removing the constraints of linear inversion, the nonlinear regression of the RF model showed a smaller RMSE and better robustness in the reconstruction process and was superior to the sEOF-r model at all depths. As a result, it provided an effective integrated learning model for SSP reconstruction.
Generalized Multiquartic Mappings, Stability, and Nonstability
Authors: Bodaghi Abasalt
Published: August 2022

In this article, a generalized form of n‐quartic mappings is introduced. The structure of such mappings is studied, and in fact, it is shown that every multiquartic mapping can be described as an equation, namely, the (generalized) multiquartic functional equation. Moreover, by applying two fixed point techniques, some results corresponding to known stability outcomes including Hyers, Rassias, and Găvruţa stabilities are presented. Using a characterization result, an appropriate counterexample is supplied to invalidate the results in the case of singularity.