bluuebunny/crossref_metadata_2025 huggingface dataset:
Nesidiocoris tenuis in Burkina Faso: Distribution, predatory capacity and insecticide sensibility
Authors: Sawadogo Mathieu W. , Mano Elias , Ahissou Besmer Régis , Somda Irénée , Nacro Souleymane , Legrève Anne , Verheggen François J.
Published: April 2022

Tuta absoluta (Lepidoptera: Gelechiidae) is a worldwide invasive insect species, considered a major pest of tomato. It has recently established in Burkina Faso, where chemical control remains the only affordable option to limit damages. Nesidiocoris tenuis (Hemiptera: Miridae) is commercialized as a biological agent to control this pest in other parts of the world. But very limited information exists on this predator in Burkina Faso. In this study, we first performed an insect survey in all agricultural regions areas of the country, and we found N. tenuis to be widely observed in all of them. Then, we performed two laboratory bioassays and demonstrated that all instars preyed on T. absoluta eggs, whether they were fed ad libitum or they had to actively forage for their prey. Because insecticide tolerant populations of T. absoluta were recently identified, we finally aimed at identifying agrochemicals that do not harm N. tenuis. We evaluated the toxicity of two synthetic insecticides, three bio‐bacterial insecticides and eight plant extracts, all being active ingredients available on the local market. Most of them (i.e., abamectin, emamectin benzoate, spinosad, spinetoram) were highly toxic for both T. absoluta and N. tenuis. In contrast, Bacillus thuringiensis var. Kurstaki, neem oil, Cleome viscosa (Asian spiderflower), Ocimum basilicum (Basil) and Cassia occidentalis (Coffee senna) were compatible with N. tenuis while still controlling T. absoluta. Based on our results, we recommend the application of Bacillus thuringiensis to both control T. absoluta and which does not compromise the maintenance of N. tenuis populations.
High Melanin Content in Melanoma Cells Contributes to EnhancedDNADamage after Rose Bengal Photosensitization
Authors: Kalegari Paloma , Leme Daniela Morais , Disner Geonildo Rodrigo , Cestari Marta Margarete , de Lima Bellan Daniel , Meira Willian Vanderlei , Mazepa Ester , Martinez Glaucia Regina
Published: April 2022

Melanoma is a type of tumor that originates from melanocytes. Irradiation of melanin with UVA and visible light can produce reactive oxygen species (ROS) such as singlet molecular oxygen (1O2). The objective of this study was to examine DNA damage in melanoma cells (B16‐F10) with different melanin contents, subjected to1O2generation. To this end, we used the photosensitizer Rose Bengal acetate (RBAc) and irradiation with visible light (526 nm) (RBAc‐PDT). We used the modified comet assay with the repair enzymes hOGG1 and T4 endonuclease V to detect the DNA damage associated with 8‐oxo‐7,8‐dihydro‐2′‐deoxyguanosine and cyclobutane pyrimidine dimers lesions, respectively. We observed increased formation of hOGG1‐ and T4endoV‐sensitive DNA lesions after light exposure (with or without RBAc). Furthermore, 18 h after irradiation, hOGG1‐sensitive DNA lesions increased compared to that at the initial time point (0 h), which shows that a high melanin content contributes to post‐irradiation formation of them, mainlyviasustained oxidative stress, as confirmed by the measurement of ROS levels and activity of antioxidant enzymes. Contrastingly, the number of T4endoV‐sensitive DNA lesions decreased over time (18 h). Our data indicate that in melanoma cells, a higher amount of melanin may affect DNA damage levels when subjected to RBAc‐PDT.
Forecasting venue popularity on location‐based services using interpretable machine learning
Authors: Wang Lei , Gopal Ram , Shankar Ramesh , Pancras Joseph
Published: April 2022

Customers are increasingly utilizing location‐based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location‐based services, which then better facilitate companies’ operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word‐of‐mouth effect are the top three drivers of venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different popularity measures, training and testing periods, and prediction horizons. These findings extend our knowledge of location‐based services by demonstrating their potential to play a prominent role in attracting consumer engagement and boosting venue popularity. Managers can make better operational decisions such as procurement and staff scheduling based on these more accurate venue popularity prediction methods. Furthermore, this study also highlights the importance of model interpretability which enhances the ability of managers to more effectively utilize machine learning models for effective decision‐making.
Local municipal capacity for climate change action in New York State: Exploring the urban–rural divide
Authors: Allred Shorna B. , Chatrchyan Allison M. , Tsintsadze Giorgi
Published: April 2022

Climate change is a critical global issue with profound local impacts. Recent scholarship on local government action to address climate change has primarily focused on cities, with less attention paid to how smaller municipalities and rural communities are tackling the issue. Our research investigates how the capacity of urban and rural municipalities in New York State (NYS) affects their ability to take climate action, utilizing data from a statewide survey, interviews, and secondary data on municipal participation in the NYS Climate Smart Communities program. We investigated how municipal capacity (including individual, management, and broader contextual factors) influences climate change action in rural and urban communities. We find that only a quarter of NYS municipalities have taken climate action, and most are mitigation rather than adaptation actions. There is significantly more climate action and municipal climate network participation in urban versus rural communities. Most local municipal officials believe that climate change is occurring and feel the impacts, yet most are unsure about addressing it at the local level. Climate action is best predicted by individual municipal capacity variables of strong climate beliefs and knowledge, and contextual variables of being in an urban municipality versus a rural one, and participation in climate networks. Key barriers to local climate action include the lack of human, financial, and technical resource capacity. Municipal climate networks can help overcome capacity limitations, particularly for resource‐limited rural communities.
Predicting Phenotypes from Brain Connection Structure
Authors: Guha Subharup , Jung Rex , Dunson David
Published: April 2022

This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high-dimensional brain network into low-dimensional pre-specified features prior to applying standard predictive algorithms. These methods are sensitive to feature choice and inevitably discard important information. Instead, we propose a nonparametric Bayes class of models that utilize the entire adjacency matrix defining brain region connections to adaptively detect predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson–Dirichlet processes to find a lower dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a ‘small n, small q’ problem, facilitating an effective stochastic search of the predictors. A spike-and-slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are found to outperform existing approaches and applied to a creative reasoning dataset.
Multifidelity Computer Model Emulation with High-Dimensional Output: An Application to Storm Surge
Authors: Ma Pulong , Karagiannis Georgios , Konomi Bledar A. , Asher Taylor G. , Toro Gabriel R. , Cox Andrew T.
Published: April 2022

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however, such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.
Memes, Misinformation, and Political Meaning
Authors: Lynch Michael P.
Published: April 2022

Are most people sincere when they share misinformation and conspiracies online? This question, while natural and important, is difficult to answer for obvious reasons. But it also applies poorly to one of the main vehicles for misinformation—memes. And it can be ambiguous; as a result, we should be mindful of two distinctions. First, a distinction between belief and a related propositional attitude, commitment. And second, the distinction between the propositional content of an attitude and what I will call its political meaning. I will suggest that these distinctions not only can help us understand how we communicate online, but they also suggest a lesson about what we should be focusing on when fighting misinformation.
Effects of integrative neuromuscular training on the gait biomechanics of children with overweight and obesity
Authors: Molina‐Garcia Pablo , Molina‐Molina Alejandro , Smeets Annemie , Migueles Jairo H. , Ortega Francisco B. , Vanrenterghem Jos
Published: April 2022

Objective

To analyze whether 13 weeks of integrative neuromuscular training can benefit spatiotemporal and kinematic parameters of gait in children with overweight/obesity.

Methods

This is a non‐randomized controlled trial. Fifty children (10.77 ± 1.24 years, 31 girls) with overweight/obesity were allocated to an exercise group (EG) (n = 25) that carried out a 13‐week exercise program based on fundamental movement skills, strength activities and aerobic training, and a control group (CG) (n = 25) that followed their normal lifestyle. Spatiotemporal (i.e., cadence, stance and support times, step length, and stride width) and kinematic (i.e., hip, pelvis, knee, and ankle angles) parameters were evaluated under laboratory conditions through a 3D analysis. ANCOVA was used to test raw and z‐score differences between the EG and CG at post‐exercise, adjusting for pre‐exercise values.

Results

The EG maintained their baseline stance and single‐limb support times while the CG increased them during walking (groups’ difference: 3.1 and 1.9 centiseconds). The EG maintained baseline maximum foot abduction angle during the stance phase whereas the CG showed an increase (groups’ difference: 3.9º). Additional analyses on kinematic profiles demonstrated that the EG walked with similar pelvic tilt and ankle abduction angles at post‐exercise, while the CG increased the pelvic anterior tilt in the whole stance phase (mean groups’ difference: 7.7º) and the ankle abduction angles in early‐ and mid‐stance phases (mean groups’ difference: 4.6º). No changes were observed in the rest of spatiotemporal and kinematic parameters.

Conclusions

The integrative neuromuscular training stopped the progression of some biomechanical alterations during walking in children with overweight/obesity. These findings could contribute to preventing common movement‐derived musculoskeletal disorders in this population, as well as preserving an optimal mechanical efficiency during walking.
Voting with Their Feet by Staying? The Political Drivers of Noncitizens' (Im‐)mobility
Authors: Bennour Salomon , Manatschal Anita
Published: April 2022

While research documents that some migrants leave their country of origin for political reasons, we do not know how the political factors in the host‐country matter to explain immigrants' (im‐)mobility behaviours after an initial migration. Addressing this gap, this study explores noncitizens' (im‐)mobility responses to regional integration policies. Building on the evidence that inclusive policies foster immigrants' ties to the host‐country over time, the paper argues that effective exposure to inclusive policies decreases inter‐cantonal and international mobility, i.e. increases noncitizens' immobility. To test this, we run multilevel analyses using STATPOP register data on the entire immigrant resident population of Switzerland, and cantonal integration policy data. Findings reveal that inclusive policies do indeed amplify the sedentary effect of time spent in Switzerland, and, thus, increase noncitizens' immobility. This pattern holds true for international and inter‐cantonal mobility, and is most pronounced among vulnerable immigrants, i.e. non‐EU citizens holding a less‐stable legal permit.
Soil microbial inoculants for sustainable agriculture: Limitations and opportunities
Authors: O'Callaghan Maureen , Ballard Ross A. , Wright David
Published: April 2022

The burgeoning global market for soil microbial inoculants for use in agriculture is being driven by pressure to increase sustainable crop production by managing pests and diseases without environmental impacts. Microbial inoculants, based predominantly on bacteria and fungi, are applied to soil as alternatives to conventional inorganic fertilizers (biofertilizers) or to carry out specific functions including biocontrol of pests and diseases (biopesticides), or for bioremediation and enhancement of soil characteristics. While some soil inoculants such as rhizobia have a long and successful history of use, others have performed inconsistently in the field and failed to live up to their promise suggested by laboratory testing. A more precise understanding of the ecology and modes of action of inoculant strains is key to optimizing their efficacy and guiding their targeted use to situations where they address key limitations to crop production. This will require greater collaboration between science disciplines, including microbiology, plant and soil science, molecular biology and agronomy. Inoculants must be produced and formulated to ensure their effective establishment in the soil and practicality of implementation alongside existing cropping practices. New approaches to strain selection and construction of beneficial microbial consortia should lead to more efficacious inoculant products. Extensive and rigorous field evaluation of inoculants under a range of soil and environmental conditions has rarely been undertaken and is urgently needed to validate emerging inoculant products and underpin successful implementation by growers, especially in a market that is largely unregulated at present.
Detecting spatial variability of soil compaction using soil apparent electrical conductivity and maize traits
Authors: Ren Lidong , D'Hose Tommy , Borra‐Serrano Irene , Lootens Peter , Hanssens Daan , De Smedt Philippe , Cornelis Wim M. , Ruysschaert Greet
Published: April 2022

Soil compaction is one of the main limiting factors for sustainable agriculture. Efficient methods for detecting the distribution of soil compaction at a field scale are urgently needed in precision agriculture. We evaluated the possibility to detect soil compaction from measurements of apparent electrical conductivity (ECa) by using electromagnetic induction (EMI) and maize traits at a ~1 ha field parcel. The soil was Luvisol, and soil compaction‐related properties, such as bulk density (BD), penetration resistance (PR), soil texture, soil salinity, soil moisture and maize traits, were measured at ten locations across the field based on ECa variability. In this site‐specific study, no good correlation between soil compaction properties (i.e. BD and PR) and ECa was found but soil clay content was positively related to ECa in the sublayer (P = 0.08). The variation in the unmanned aerial vehicle (UAV)‐derived maize canopy height was significantly negatively correlated with soil compaction and a negative relationship between dry above‐ground biomass and BD and PR existed at 25–50 cm layer. In addition, ECa and UAV‐derived maize canopy height was negatively correlated (Spearman's rank correlation coefficients of −0.67 in the 0–25 cm layer) under the rather dry conditions prevailing during this study. Overall, as could be expected, detecting soil compaction with ECa was challenging under heterogeneous terrains, while maize traits had a better correlation with spatial variability of soil compaction. Owing to its sensitivity to the integral variation of multiple soil properties, ECa was a good indicator of maize canopy height.
Using the T‐EFA method in a cellular automata‐based urban growth simulation's calibration step
Authors: Ayazli Ismail Ercument , Yakup Ahmet Emir , Bilen Omer
Published: April 2022

Changes in land cover driven by urban sprawl increase the threat of urbanization of forests and agricultural lands. Therefore, monitoring urban sprawl by creating simulation models is frequently carried out to understand sustainable city management. Cellular automata‐based models are mostly preferred to reduce the damage led by urban sprawl, and the SLEUTH model is the most well known. Several methods have been developed for the SLEUTH model calibration step, such as optimum SLEUTH metrics and total exploratory factor analysis (T‐EFA), to improve the model accuracy. This study aims to create a high‐accuracy urban growth simulation model using low‐resolution data, investigate the T‐EFA method's success in the calibration step, and find the urban sprawl effects on land cover change. Istanbul was selected as our study area due to witnessing its tremendous urban sprawl since the 1950s. According to our results, the urban growth that occurred between 2000 and 2018 could be defined more closely to reality using the T‐EFA method, and Istanbul will continue to grow until 2040, with approximately 428.7 km2of agricultural lands, 553.4 km2of forests, and 0.1 km2of wetlands being transformed to urban. In addition, the geologically risky areas under threat of urbanization will increase by 60% between 2018 and 2040.
Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion
Authors: Ruba Ashley L. , Pollak Seth D. , Saffran Jenny R.
Published: April 2022

During the early postnatal years, most infants rapidly learn to understand two naturally evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain‐general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.
Phenylhydrazine‐induced anaemia reduces subcutaneous white and brown adipose tissues in hypothalamic obese rats
Authors: de Souza Domwesley Wendreo , Ceglarek Vanessa Marieli , Siqueira Bruna Schumaker , Volinski Caroline Zanella , Nenevê Juliane Zanon , Arruda João Paulo de Amorin , Vettorazzi Jean Franciesco , Grassiolli Sabrina
Published: April 2022

New Findings

What is the central question of this study?

Can an anaemic state modify adiposity and metabolic parameters in hypothalamic obese rats?

What is the main finding and its importance?

Hypothalamic obese rats do not display iron deficiency. However, the pharmacological induction of anaemia in hypothalamic obese rats resulted in reduced adiposity, characterized by a decrease in subcutaneous white and brown adipose tissue depots. These findings suggest that iron imbalance in obesity may elevate lipolysis.

Iron imbalance is frequent in obesity. Herein, we evaluated the impact of anaemia induced by phenylhydrazine on adiposity and metabolic state of hypothalamic obese rats. Hypothalamic obesity was induced by high doses of monosodium glutamate (MSG; 4 g/kg) administered to neonatal male rats (n = 20). Controls (CTL; non‐obese rats) received equimolar saline (n = 20). Rats were weaned at 21 days of life. At 70 days, half of the rats received three intraperitoneal doses of phenylhydrazine (PHZ; 40 mg/kg/dose) or saline solution. Body weight and food intake were followed for 4 weeks after PHZ administration. At 92 days, rats were killed and blood was collected for microcapillary haematocrit (Hct) analysis and plasma quantification of glucose, triglycerides, total cholesterol and iron levels. The liver, the spleen, and the white (WAT) and brown (BAT) adipose tissues were excised, weighed and used for histology. MSG‐treated rats developed obesity, hypertriglyceridaemia and insulin resistance, compared to CTL rats, without changes in iron levels and Hct. PHZ administration reduced plasma iron levels and promoted similar tissue injuries in the spleen and liver from MSG and CTL rats. However, in MSG‐treated rats, PHZ decreased fasting glucose levels and Hct, as well as diminishing the subcutaneous WAT and BAT mass. Although MSG‐obesity does not affect plasma iron levels and Hct by itself, PHZ‐induced anaemia associated with obesity induces a marked drop in subcutaneous WAT and BAT mass, suggesting that iron imbalance may lead to increased lipolytic responses in obese rats, compared to lean rats.
Connectedness Efficiency Analysis of Weighted U.S. Freight Railroad Networks
Authors: Hamed Majed , Mao Yujie , Ayyub Bilal M. , Elsibaie Magdy , Omar Tarek
Published: April 2022

Freight rail networks serve a key role in transporting bulk goods to accommodate changing market demands and to serve public needs. Network analyses of such systems can provide important insights into enhancing transportation efficiency and system resilience. This paper develops and investigates a topological analysis model for network efficiency, which is associated with the connectedness of a network's nodes by its links and their corresponding network attributes. This model allows analyzing network topologies with or without assigned weights to their nodes and links based on different attributes. Key attributes may include physical length of links, commodity types and volume of goods transported through links, and dwell-time at nodes, and origination termination volumes of goods, types of goods moved, and origins and destination of goods. The model presented here enables (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining an understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. Such analyses can inform rail managers and executives in planning expansions, route or freight changes, or preparations for potential node or link failures. A case study of an aggregate U.S. freight rail network along with other example topologies is presented to demonstrate the use of selected network attributes and their influence on connectedness efficiency and the impacts of node and link failures on the overall transport efficiency.
Modified Friedmann equations from Kaniadakis entropy and cosmological implications on baryogenesis and $${}^7 Li$$-abundance
Authors: Luciano Giuseppe Gaetano
Published: April 2022

Modified Friedmann equations have been recently derived by implementing the gravity-thermodynamics conjecture in Kaniadakis statistics, which is a relativistic self-consistent generalization of the classical Boltzmann–Gibbs theory. The ensuing cosmological scenario exhibits new extra corrections depending on the model parameter K. In this work we apply Kaniadakis statistics to the horizon entropy of the FRW Universe and explore implications on baryogenesis and primordial Lithium abundance problems. This framework is motivated by the fact that physics of the early Universe is fundamentally relativistic, suggesting that a relativistic description might also involve the statistical properties of horizon degrees of freedom. By requiring consistency with observational data on baryogenesis and Lithium abundance, we constrain the Kaniadakis parameter. We also speculate on the possibility that a running K be allowed to trace the usual thermal history of the Universe in Kaniadakis statistics.
Generalized Maxwell–Higgs vortices in models with enhanced symmetry
Authors: Bazeia D. , Liao M. A. , Marques M. A.
Published: April 2022

Topological vortices in relativistic gauge theories in flat three-dimensional spacetime are investigated. We consider the symmetry$$\mathrm {U(1)}\times \cdots \times \mathrm {U(1)}$$U(1)×⋯×U(1), and for each$$\mathrm {U(1)}$$U(1)subgroup, a complex scalar field transforming under its action is introduced, as well as generalized permeabilities through which the subsystems are coupled. We investigate in detail the features of static, finite energy solutions within this class of generalized Maxwell–Higgs models, and study the effect of the winding numbers in the magnetic properties of each subsystem. A BPS bound and the related first order equations are introduced for a large class of models. Finally, we present some specific models and solve their equations of motion to find solutions engendering many distinct features in relation to each other and to the standard Nielsen–Olesen vortex.
Input-Driven Double-Head Pushdown Automata
Authors: Holzer Markus , Kutrib Martin , Malcher Andreas , Wendlandt Matthias
Published: April 2022

We introduce and study input-driven deterministic and nondeterministic double-head pushdown automata. A double-head pushdown automaton is a slight generalization of an ordinary pushdown automaton working with two input heads that move in opposite directions on the common input tape. In every step one head is moved and the automaton decides on acceptance if the heads meet. Demanding the automaton to work input-driven it is required that every input symbol uniquely defines the action on the pushdown store (push, pop, state change). Normally this is modeled by a partition of the input alphabet and is called a signature. Since our automaton model works with two heads either both heads respect the same signature or each head owes its own signature. This results in two variants of input-driven double-head pushdown automata. The induced language families on input-driven double-head pushdown automata are studied from the perspectives of their language describing capability, their closure properties, and decision problems.
Numerical solution of Rosenau–KdV equation using Sinc collocation method
Authors: Deng Wenchao , Wu Beibei
Published: April 2022

The numerical method based on Sinc function is applied for the solution of Rosenau–KdV equation in this paper. The equation is fully-discretized by using the Sinc collocation method for spatial discretization and the forward finite difference for time discretization. The difference scheme is indicated to be conditionally stable using error analysis. The validity and accuracy of this method are verified by serval numerical experiments using single solitary waves, double solitary waves and multiple solitary waves.
Enhanced Edge Detection for 3D Crack Segmentation and Depth Measurement with Laser Data
Authors: Cao Ting , Hu Jinyuan , Liu Sheng
Published: April 2022

With the usage of computer visual technology in civil engineering, pavement crack survey with imaging sensor gained wide attention in the past years. Unfortunately, it is still a challenge to achieve the satisfying results. This paper presents a pavement crack survey approach based on edge detection for laser data. At first, the LS-40 line-laser scanner is implemented to achieve 3D pavement surface data. With the advantage of the various depth information exhibited in the pavement data, an enhanced edge detection based on fractional differential is proposed for 3D crack segmentation. The proposed method could effectively enhance the crack boundary and maintain texture details, which can guarantee the high accuracy in crack segmentation. Moreover, a novel plane fitting method based on dynamic threshold is studied to calculate crack depth information. It can not only identify and remove invalid points effectively, but also accomplish plane calculation with errors in three directions. Experiments verify that the proposed approach can achieve the satisfying result and can work well in F-measure system.
APPLYING THE LOCAL MARTINGALE THEORY OF BUBBLES TO CRYPTOCURRENCIES
Authors: CHOI SOON HYEOK , JARROW ROBERT A.
Published: April 2022

Cryptocurrencies provide a natural setting to test for the existence of price bubbles using the local martingale theory of bubbles because cryptocurrencies have no cash flows. Using a robust statistical algorithm, we test for price bubbles in eight cryptocurrencies, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC), from 1 January 2019 to 17 July 2019. The statistical test first estimates the cryptocurrencies’ volatilities as a function of the price level. Then, these estimates are extrapolated over the positive real line using power functions. Finally, these power functions underly a sequence of hypothesis tests for price bubbles that control for both Type I and Type II errors. Five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, LTC does not, and the evidence for ETH and XRP is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies.
Advances in Active Control of Wind-Induced Vibration of Long-Span Suspension Bridges
Authors: Guo Zengwei , Lin Sen , Ni Qian
Published: April 2022

This paper reviews various active control measures used for controlling the wind-induced vibration, which is a problem of great concern for long-span suspension bridges. Firstly, a brief discussion of passive control measures and their limitations is presented. Then, the development of active aerodynamic control and active mechanical control is comprehensively reviewed to present the characteristics and limitations of numerous control devices with emphasis on the control surfaces adopted for active aerodynamic control. Particularly, the existing challenges and suggestions for future studies are proposed for various countermeasures, especially for the control surfaces used in active control. It is expected that the information provided in this article will facilitate further research in the area of vibration control for long-span suspension bridges under various wind conditions.
Dynamic Fracture Analysis of Natural Gas Pipelines Based on a Cohesive Zone Model
Authors: Liao Yi , Liu Changlei , Liao Kexi , Jia Jiaxin , Ge Liang , Xiang Meizhen , Chen Jun
Published: April 2022

Fracture research of natural gas pipelines with cracks is an important part of pipeline integrity evaluation. Based on the cohesive zone model (CZM), a numerical analysis is conducted of the dynamic fracture of X80 steel pipelines under explosion load. The factors considered include the crack propagation length, crack propagation velocity, dynamic crack tip opening angle (CTOA) and gas pressure. The results show that the whole cracking process can be divided into three stages: rapid cracking, stable propagation, and deceleration and stop cracking. When in the rapid cracking stage, the axial cracking of the pipeline is dominant. However, in the stable propagation stage, lateral expansion plays a more significant role. Besides, there exist upper limits on the length and velocity of crack propagation. In addition, it is also found that the time for the inflection point of rapid cracking and stable propagation is later than that to reach the velocity peak. The dynamic CTOA decreases with axial crack propagation, and the steady-state value of the CTOA is positively correlated with the load. The high-pressure gas escapes from the pipeline along the crack, resulting in the increase of air pressure outside the pipeline. Moreover, the peak pressure outside the pipeline is approximately linear its the initial crack length. The present research on the dynamic fracture mechanics behavior of the pipeline provides a necessary supplement for the test.
Sensitivity and Reliability Analyses in to Actively Controlled Structures Under Earthquake
Authors: Hosseinaei Saeed , Ghasemi Mohammad Reza , Etedali Sadegh , Chan Tommy H. T.
Published: April 2022

This study aims for a probabilistic assessment of active seismic control of structures in the presence of uncertainties of the structural model and the stochastic model of the ground acceleration. First, sensitivity analyses based on the Borgonovo sensitivity index are conducted to measure the effect of uncertainty sources on the maximum and root mean square (RMS) of the main structural responses in different seismic hazard regions. Then, the reliability analyses are conducted using Monte Carlo simulation (MCS) and importance sampling (IS) methods to the estimation of failure probabilities and reliability indices of the structures in different seismic hazard regions, force capacity of actuators, and seismic performance levels. Numerical studies carried out on a 10-story structure show that the uncertainties in peak ground acceleration (PGA) and story stiffness have the most effects on the seismic responses of the structure. The effect of the considered uncertainties is negligible on the maximum demand control force. The results also show that equipping the structure with an active control system results in a significant reduction in the failure probabilities of the structure. The target reliability index for the uncontrolled structure in life safety seismic performance level may not be satisfied in high and very high seismic hazard regions, it can however be easily dealt with, using the proposed technique, in the corresponding controlled structures. By increasing the control force capacity, an increment is observed in the reliability index. A high and suitable reliability index is given for both controlled and uncontrolled in the collapse prevention seismic performance level.
Performance Analysis and Critical Review on Segmentation Techniques for Brain Tumor Classification
Authors: Raju Ayalapogu Ratna , Pabboju Suresh , Ramisetty Rajeswara Rao
Published: April 2022

An irregular growth in brain cells causes brain tumors. In recent years, a considerable rate of increment in medical cases regarding brain tumors has been observed, affecting adults and children. However, it is highly curable in recent times only if detected in the early time of tumor growth. Moreover, there are many sophisticated approaches devised by researchers for predicting the tumor regions and their stages. In addition, Magnetic Resonance Imaging (MRI) is utilized commonly by radiologists to evaluate tumors. In this paper, the input image is from a database, and brain tumor segmentation is performed using various segmentation techniques. Here, the comparative analysis is performed by comparing the performance of segmentation approaches, like Hybrid Active Contour (HAC) model, Bayesian Fuzzy Clustering (BFC), Active Contour (AC), Fuzzy C-Means (FCM) clustering technique, Sparse (Sparse FCM), and Black Hole Entropy Fuzzy Clustering (BHEFC) model. Moreover, segmentation technique performance is evaluated with the Dice coefficient, Jaccard coefficient, and segmentation accuracy. The proposed method shows high Dice and Jaccard coefficients of 0.7809 and 0.6456 by varying iteration with the REMBRANDT dataset and a better segmentation accuracy of 0.9789 by changing image size in the Brats-2015 database.
Locust Mayfly Optimization-Tuned Neural Network for AI-Based Pruning in Chess Game
Authors: Chole Vikrant , Gadicha Vijay
Published: April 2022

The art of mimicking a human’s responses and behavior in a programming machine is called Artificial intelligence (AI). AI has been incorporated in games in such a way to make them interesting, especially in chess games. This paper proposes a hybrid optimization tuned neural network (NN) to establish a winning strategy in the chess game by generating the possible next moves in the game. Initially, the images from Portable Game Notation (PGN) file are used to train the NN classifier. The proposed Locust Mayfly algorithm is utilized to optimally tune the weights of the NN classifier. The proposed Locust Mayfly algorithm inherits the characteristic features of hybrid survival and social interacting search agents. The NN classifier involves in finding all the possible moves in the board, among which the best move is obtained using the mini-max algorithm. At last, the performance of the proposed Locust mayfly-based NN method is evaluated with help of the performance metrics, such as specificity, accuracy, and sensitivity. The proposed Locust mayfly-based NN method attained a specificity of 98%, accuracy of 98%, and a sensitivity of 98%, which demonstrates the productiveness of the proposed mayfly-based NN method in pruning.
Nuclear magnetic resonance thermosensing properties of holmium(III) and thulium(III) tris(tetra-15-crown-5-phthalocyaninato) complexes
Authors: Babailov Sergey P. , Polovkova Marina A. , Zapolotsky Evgeny N. , Kirakosyan Gayane A. , Martynov Alexander G. , Gorbunova Yulia G.
Published: April 2022

Temperature dependences of paramagnetic chemical shifts in NMR spectra of lanthanide tris-phthalocyaninates Ln2[(15C5)4Pc]3(where [(15C5)4Pc][Formula: see text] is 2,3,9,10,16,17,24,25-tetrakis(15-crown-5)phthalocyaninate dianion, Ln = Ho(III), Tm(III)) have been studied in the physiological temperature range (from 303 to 323 K). The observed maximum temperature sensitivity [Formula: see text]/dT turns out to be 0.25 ppm/K for the signals of the thulium complex and 0.16 ppm/K for the holmium complex. By the example of Tm2[(15C5)4Pc]3, it has been shown that the use of temperature sensitivities normalized to signal half-widths (∣CT∣/[Formula: see text] is expedient to judge the applicability of the observed temperature dependences of LISs of analogous lanthanide complexes (Ln = Tb, Dy, Ho, Tm) for determining temperatures. The investigated kinetically and thermodynamically stable Ln2[(15C5)4Pc]3complexes can be considered promising for the design of thermosensitive NMR probes for determination of the local temperature in nonpolar solutions.
Atypical Swyer Syndrome Presenting with Spontaneous Breast Development and Secondary Amenorrhea
Authors: Park Joon Cheol
Published: April 2022

46, XY pure gonadal dysgenesis (Swyer syndrome) is usually presented as sexual infantilism with delayed puberty and primary amenorrhea. Rarely, patients can present with spontaneous breast development and/or menstruation. A 32-year-old woman complained of secondary amenorrhea lasting 1 year. Her menarche occurred at the age of 15. On physical examination, her external genitals were completely feminine. Breast development and pubic hair were compatible with Tanner stage V. Pelvic ultrasound showed a small-sized uterus and gonads. Laboratory investigations revealed follicle-stimulating hormone (FSH) 75.86 mIU/mL, estradiol 28.67 pg/mL, testosterone 0.27 ng/mL, dehydroepiandrosterone sulfate (DHEAS) 260.55 ug/dL and anti-Müllerian hormone (AMH) < 0.02 ng/mL. Chromosomal analysis indicated a 46, XY karyotype. She underwent laparoscopic gonadectomy, and pathology revealed dysgenetic gonads without germ cell tumors. Only a few cases of Swyer syndrome, spontaneous breast development or spontaneous vaginal bleeding related with germ cell tumor have been reported in the literature. To my knowledge, this is the first report of Swyer syndrome with unexplained spontaneous breast development and menstruation, despite the absence of hormone-producing gonadal neoplasm.
IoT-Based Response Time Analysis of Messages for Smart Autonomous Collision Avoidance System Using Controller Area Network
Authors: Biswal Anil Kumar , Singh Debabrata , Pattanayak Binod Kumar , Samanta Debabrata , Banerjee Amit , Seteikin A. Y. , Samusev I. G.
Published: April 2022

Many accidents and serious problems occur on the road due to the rapid increase in traffic congestion in all sections of the country. Autonomous vehicles provide a solution to successfully and cost-effectively avoid this problem while minimizing user disruption. Currently, more engaging electromechanical elements with an analog interface are used to develop affordable automobiles for efficient and cost-effective operation for a smart driving platform with a semiautonomous automobile, strengthening the vehicle involvement of the driver while increasing safety. As a result, it takes longer for various car elements to respond, which causes more problems during message transmission. This project aims to create a Controller Area Network (CAN) for analyzing message response times by incorporating a few application nodes on the IoT platform, such as an antilock braking system, flexible cruise control, and seat belt section, for some real-time control system applications. These application nodes are car analytical parts that are linked to IoT modules to prevent collisions. An autonomous device for collision avoidance and obstacle detection in a vehicle can impact road accidents if the CAN protocol is implemented.
Chinese Short Text Classification by ERNIE Based on LTC_Block
Authors: Zhang Meng , Shang Xiang
Published: April 2022

Short text classification, an important direction of the basic research of natural language processing, has extensive applications. Its effect depends on feature extraction methods and feature representation methods. This paper proposed an LTC_Block-based short text classification model named ERNIE to classify Chinese short texts and extract semantics in the corpus to address the polysemy problem in the text. In this model, LTC_Block, a double-channel structural unit composed of BiLSTM and TextCNN, was used to extract the contextual sequences and overall features of semantics, and residual connection was used to integrate features and further classify short texts. Experiments on two different datasets showed that ERNIE achieved a better classification effect than mainstream models, proving its feasibility and effectiveness.
Molecular Evidence of Coinfection with Acute Respiratory Viruses and High Prevalence of SARS-CoV-2 among Patients Presenting Flu-Like Illness in Bukavu City, Democratic Republic of Congo
Authors: Ntagereka Patrick Bisimwa , Basengere Rodrigue Ayagirwe , Baharanyi Tshass Chasinga , Kashosi Théophile Mitima , Buhendwa Jean-Paul Chikwanine , Bisimwa Parvine Basimane , Kusinza Aline Byabene , Mugumaarhahama Yannick , Shukuru Dieudonne Wasso , Patrick Simon Baenyi , Tonui Ronald , Birindwa Ahadi Bwihangane , Mukwege Denis
Published: April 2022

The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with clinical manifestation cases that are almost similar to those of common respiratory viral infections. This study determined the prevalence of SARS-CoV-2 and other acute respiratory viruses among patients with flu-like symptoms in Bukavu city, Democratic Republic of Congo. We screened 1352 individuals with flu-like illnesses seeking treatment in 10 health facilities. Nasopharyngeal swab specimens were collected to detect SARS-CoV-2 using real-time reverse transcription-polymerase chain reaction (RT-PCR), and 10 common respiratory viruses were detected by multiplex reverse transcription-polymerase chain reaction assay. Overall, 13.9% (188/1352) of patients were confirmed positive for SARS-CoV-2. Influenza A 5.6% (56/1352) and Influenza B 0.9% (12/1352) were the most common respiratory viruses detected. Overall, more than two cases of the other acute respiratory viruses were detected. Frequently observed symptoms associated with SARS-CoV-2 positivity were shivering (47.8%; OR = 1.8; CI: 0.88–1.35), cough (89.6%; OR = 6.5, CI: 2.16–28.2), and myalgia and dizziness (59.7%; OR = 2.7; CI: 1.36–5.85). Moreover, coinfection was observed in 12 (11.5%) specimens. SARS-CoV-2 and influenza A were the most cooccurring infections, accounting for 33.3% of all positive cases. This study demonstrates cases of COVID-19 infections cooccurring with other acute respiratory infections in Bukavu city during the ongoing outbreak of COVID-19. Therefore, testing for respiratory viruses should be performed in all patients with flu-like symptoms for effective surveillance of the transmission patterns in the COVID-19 affected areas for optimal treatment and effective disease management.
Maternal Inflammation Exaggerates Offspring Susceptibility to Cerebral Ischemia–Reperfusion Injury via the COX-2/PGD2/DP2 Pathway Activation
Authors: Li Yuke , Luo Wen , Zhang Jiahua , Luo Ying , Han Wenli , Wang Hong , Xia Hui , Chen Zhihao , Yang Yang , Chen Qi , Li Huan , Yang Lu , Hu Congli , Huang Haifeng , Peng Zhe , Tan Xiaodan , Li Miaomiao , Yang Junqing
Published: April 2022

The pathogenesis of cerebral ischemia–reperfusion (I/R) injury is complex and does not exhibit an effective strategy. Maternal inflammation represents one of the most important factors involved in the etiology of brain injury in newborns. We aimed to investigate the effect of maternal inflammation on offspring susceptibility to cerebral I/R injury and the mechanisms by which it exerts its effects. Pregnant SD rats were intraperitoneally injected with LPS (300 μg/kg/day) at gestational days 11, 14, and 18. Pups were subjected to MCAO/R on postnatal day 60. Primary neurons were obtained from postnatal day 0 SD rats and subjected to OGD/R. Neurological deficits, brain injury, neuronal viability, neuronal damage, and neuronal apoptosis were assessed. Oxidative stress and inflammation were evaluated, and the expression levels of COX-2/PGD2/DP pathway-related proteins and apoptotic proteins were detected. Maternal LPS exposure significantly increased the levels of oxidative stress and inflammation, significantly activated the COX-2/PGD2/DP2 pathway, and increased proapoptotic protein expression. However, maternal LPS exposure significantly decreased the antiapoptotic protein expression, which subsequently increased neurological deficits and cerebral I/R injury in offspring rats. The corresponding results were observed in primary neurons. Moreover, these effects of maternal LPS exposure were reversed by a COX-2 inhibitor and DP1 agonist but exacerbated by a DP2 agonist. In conclusion, maternal inflammatory exposure may increase offspring susceptibility to cerebral I/R injury. Moreover, the underlying mechanism might be related to the activation of the COX-2/PGD2/DP2 pathway. These findings provide a theoretical foundation for the development of therapeutic drugs for cerebral I/R injury.