bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal decomposition methods for nanobubble contrast-enhanced ultrasound
Authors: Wegierak Dana , Perera Reshani , Cooley Michaela , Exner Agata A. , Bayat Mahdi
Published: May 2022

Nanobubble ultrasound contrast agents (NBs) offer enhanced access to deep tumor tissue by extrapolation from vasculature. When combined with targeting moieties, shell-stabilized nanobubbles can outperform other agents used in molecular ultrasound imaging due to their small size. The visualization and decoupling of extravasated and intravascular NBs is critical to furthering nanobubble molecular imaging with ultrasound. Spatiotemporal processing of echo data, usually via singular value decomposition (SVD), enables enhanced decoupling of signals of stationary from moving scatterers and therefore provides a novel tool for analysis of NB extravasation into the parenchyma. Instead of trivial solutions, we have developed non-negative SVD (nnSVD) methods applicable to most commercial scanners without requiring the raw data. Additionally, our formulation allows addition of proper regularizers, such as sparsity, to further expand the decomposition dimensions. We validate these methods in phantom models and present how the suggested decompositions can distinctly decouple flowing from extravasated NBs in in vivo tumour models with enhanced permeability. Our results show the successful tracking of nanobubble accumulation in tumour tissue and can serve as a relative metric for molecular targeting of prostate cancer. Further exploration of these methods could be used to improve long-term imaging and drug delivery capabilities of NBs.
News credibility labels have limited average effects on news diet quality and fail to reduce misperceptions
Authors: Aslett Kevin , Guess Andrew M. , Bonneau Richard , Nagler Jonathan , Tucker Joshua A.
Published: May 2022

As the primary arena for viral misinformation shifts toward transnational threats, the search continues for scalable countermeasures compatible with principles of transparency and free expression. We conducted a randomized field experiment evaluating the impact of source credibility labels embedded in users’ social feeds and search results pages. By combining representative surveys (
n
= 3337) and digital trace data (
n
= 968) from a subset of respondents, we provide a rare ecologically valid test of such an intervention on both attitudes and behavior. On average across the sample, we are unable to detect changes in real-world consumption of news from low-quality sources after 3 weeks. We can also rule out small effects on perceived accuracy of popular misinformation spread about the Black Lives Matter movement and coronavirus disease 2019. However, we present suggestive evidence of a substantively meaningful increase in news diet quality among the heaviest consumers of misinformation. We discuss the implications of our findings for scholars and practitioners.
Machine learning of material properties: Predictive and interpretable multilinear models
Authors: Allen Alice E. A. , Tkatchenko Alexandre
Published: May 2022

Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques can be used with black box solutions, or alternatively, models can be created that are directly interpretable. We revisit material datasets used in several works and demonstrate that simple linear combinations of nonlinear basis functions can be created, which have comparable accuracy to the kernel and neural network approaches originally used. Linear solutions can accurately predict the bandgap and formation energy of transparent conducting oxides, the spin states for transition metal complexes, and the formation energy for elpasolite structures. We demonstrate how linear solutions can provide interpretable predictive models and highlight the new insights that can be found when a model can be directly understood from its coefficients and functional form. Furthermore, we discuss how to recognize when intrinsically interpretable solutions may be the best route to interpretability.
Innate immune cell response to host-parasite interaction in a human intestinal tissue microphysiological system
Authors: Humayun Mouhita , Ayuso Jose M. , Park Keon Young , Martorelli Di Genova Bruno , Skala Melissa C. , Kerr Sheena C. , Knoll Laura J. , Beebe David J.
Published: May 2022

Protozoan parasites that infect humans are widespread and lead to varied clinical manifestations, including life-threatening illnesses in immunocompromised individuals. Animal models have provided insight into innate immunity against parasitic infections; however, species-specific differences and complexity of innate immune responses make translation to humans challenging. Thus, there is a need for in vitro systems that can elucidate mechanisms of immune control and parasite dissemination. We have developed a human microphysiological system of intestinal tissue to evaluate parasite-immune–specific interactions during infection, which integrates primary intestinal epithelial cells and immune cells to investigate the role of innate immune cells during epithelial infection by the protozoan parasite,
Toxoplasma gondii
, which affects billions of people worldwide. Our data indicate that epithelial infection by parasites stimulates a broad range of effector functions in neutrophils and natural killer cell–mediated cytokine production that play immunomodulatory roles, demonstrating the potential of our system for advancing the study of human-parasite interactions.
Zbtb20 identifies and controls a thymus-derived population of regulatory T cells that play a role in intestinal homeostasis
Authors: Krzyzanowska Agata K. , Haynes II Rashade A. H. , Kovalovsky Damian , Lin Hsin-Ching , Osorio Louis , Edelblum Karen L. , Corcoran Lynn M. , Rabson Arnold B. , Denzin Lisa K. , Sant’Angelo Derek B.
Published: May 2022

The expression of BTB-ZF transcription factors such as ThPOK in CD4+T cells, Bcl6 in T follicular helper cells, and PLZF in natural killer T cells defines the fundamental nature and characteristics of these cells. Screening for lineage-defining BTB-ZF genes led to the discovery of a subset of T cells that expressed Zbtb20. About half of Zbtb20+T cells expressed FoxP3, the lineage-defining transcription factor for regulatory T cells (Tregs). Zbtb20+Tregswere phenotypically and genetically distinct from the larger conventional Tregpopulation. Zbtb20+Tregsconstitutively expressed mRNA for interleukin-10 and produced high levels of the cytokine upon primary activation. Zbtb20+Tregswere enriched in the intestine and specifically expanded when inflammation was induced by the use of dextran sodium sulfate. Conditional deletion ofZbtb20in T cells resulted in a loss of intestinal epithelial barrier integrity. Consequently, knockout (KO) mice were acutely sensitive to colitis and often died because of the disease. Adoptive transfer of Zbtb20+Tregsprotected the Zbtb20 conditional KO mice from severe colitis and death, whereas non-Zbtb20 Tregsdid not. Zbtb20 was detected in CD24hidouble-positive and CD62LloCD4 single-positive thymocytes, suggesting that expression of the transcription factor and the phenotype of these cells were induced during thymic development. However, Zbtb20 expression was not induced in “conventional” Tregsby activation in vitro or in vivo. Thus, Zbtb20 expression identified and controlled the function of a distinct subset of Tregsthat are involved in intestinal homeostasis.
830-million-year-old microorganisms in primary fluid inclusions in halite
Authors: Schreder-Gomes Sara I. , Benison Kathleen C. , Bernau Jeremiah A.
Published: May 2022

Primary fluid inclusions in bedded halite from the 830-m.y.-old Browne Formation of central Australia contain organic solids and liquids, as documented with transmitted light and ultraviolet–visible (UV-vis) petrography. These objects are consistent in size, shape, and fluorescent response with cells of prokaryotes and eukaryotes and with organic compounds. This discovery shows that microorganisms from saline depositional environments can remain well preserved in halite for hundreds of millions of years and can be detected in situ with optical methods alone. This study has implications for the search for life in both terrestrial and extraterrestrial chemical sedimentary rocks.
Neonatal listeriosis: a rare but not-to-be forgotten infection
Authors: Gomez Gretell , Islam Shamim
Published: May 2022

Listeria monocytogenesis a foodborne pathogen, typically associated with contaminated dairy products and deli meats.L. monocytogenescan lead to severe infections in high-risk patient populations; in neonates, listeriosis is rare but carries a high rate of neurological morbidity and mortality. Here a case of neonatal listeriosis, in the newborn of a young Hispanic mother who frequently ate queso fresco (a fresh Mexican cheese), is presented. Pregnant women are commonly counselled to avoid unpasteurised dairy during the pregnancy, but many are unaware that soft cheeses, and other food products, may pose risks for perinatal infection.L. monocytogenesremains a cause of food-related outbreaks and maternal and neonatal sepsis around the world, and healthcare providers should ensure that expectant mothers are carefully counselled regarding potential sources.
New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology
Authors: Nath Siddharth , Marie Abdullah , Ellershaw Simon , Korot Edward , Keane Pearse A
Published: May 2022

Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.
Postoperative opioid prescribing, use and pain trends following general surgery procedures: a retrospective cohort study among veterans comparing non-opioid versus chronic opioid users
Authors: Bleicher Josh , Brooke Benjamin Sands , Bayless Kimberlee , Anderson Zachary , Beckstrom Julie , Zhang Chong , Presson Angela P , Huang Lyen C , Buys Michael Jacob
Published: May 2022

Introduction

Understanding postoperative opioid use patterns among different populations is key to developing opioid stewardship programs.

Methods

We performed a retrospective cohort study on opioid prescribing, use, and pain after general surgery procedures for patients cared for by a transitional pain service at a veterans administration hospital. Discharge opioid prescription quantity, 90-day opioid prescription, and patient reported outcome pain measures were compared between chronic opioid users and non-opioid users (NOU). Additionally, 90-day total opioid use was evaluated for NOU.

Results

Of 257 patients, 34 (13%) were on chronic opioid therapy, over 50% had a mental health disorder, and 29% had a history and/or presence of a substance use disorder. NOU were prescribed a median (IQR) of 10 (7, 12) tablets at discharge, while chronic opioid users were prescribed 6 (0, 12) tablets (p<0.001). 90-day opioid prescription (not including baseline opioid prescription for chronic users) was 10 (7, 15) and 6 (0, 12) tablets, respectively (p=0.001). There were no differences in changes in pain intensity or pain interference scores during recovery between groups. Median 90-day opioid use post discharge for NOU was 4 (0, 10) pills.

Discussion

Non-opioid and chronic opioid users required very few opioid pills following surgery, and patients on chronic opioid therapy quickly returned to their baseline opioid use after a small opioid prescription at discharge. There was no difference in pain recovery between groups. Opioid prescribing guidelines should include patients on chronic opioid therapy and could consider recommending a more conservative prescribing approach.
Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm
Authors: Moussa Charles , Wang Hao , Bäck Thomas , Dunjko Vedran
Published: May 2022

As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of parameters are being tweaked to optimize a cost function out of the quantum circuit output. One of these algorithms, the Quantum Approximate Optimization Algorithm stands out as a promising approach to tackling combinatorial problems. However, finding the appropriate parameters is a difficult task. Although QAOA exhibits concentration properties, they can depend on instances characteristics that may not be easy to identify, but may nonetheless offer useful information to find good parameters. In this work, we study unsupervised Machine Learning approaches for setting these parameters without optimization. We perform clustering with the angle values but also instances encodings (using instance features or the output of a variational graph autoencoder), and compare different approaches. These angle-finding strategies can be used to reduce calls to quantum circuits when leveraging QAOA as a subroutine. We showcase them within Recursive-QAOA up to depth 3 where the number of QAOA parameters used per iteration is limited to 3, achieving a median approximation ratio of 0.94 for MaxCut over 200 Erdős-Rényi graphs. We obtain similar performances to the case where we extensively optimize the angles, hence saving numerous circuit calls.
Joule–Thomson expansion of Born–Infeld AdS black holes in consistent 4D Einstein–Gauss–Bonnet gravity
Authors: Zhang Chao-Ming , Zhang Ming , Zou De-Cheng
Published: May 2022

In this paper, the Joule–Thomson expansion of Born–Infeld AdS black holes in the consistent Aoki–Gorji–Mukohyama theory of 4D Einstein–Gauss–Bonnet gravity is studied in the extended phase space. We further analyze the effect of parameters [Formula: see text] and [Formula: see text] on the inversion curves and plot the inversion and isenthalpic curves in the [Formula: see text]–[Formula: see text] plane, which can determine the cooling–heating regions.
Invariant differential operators for the Jacobi algebra 𝒢2
Authors: Aizawa N. , Dobrev V. K.
Published: May 2022

In this paper, we construct explicitly the intertwining differential operators for the Jacobi algebra [Formula: see text]. For the construction, we use the singular vectors of the Verma modules over [Formula: see text] which we have constructed earlier. We construct the function spaces on which the operators act. We find two versions of the left (representation) action and the right action. These actions are combined with the singular vectors to provide the intertwining differential operators.
Cosmic and growth matter analysis of deformed Hořava–Lifshitz gravity
Authors: Jawad Abdul , Shad Mehwish , Bamba Kazuharu
Published: May 2022

Hořava–Lifshitz gravity is a very fascinating proposal of modified gravity for explaining the current acceleration and other cosmic issues. In continuation to this discussion, we explore the consequences of Tsallis dark energy with Hubble and particle horizons in the framework of deformed Hořava–Lifshitz gravity for flat FRW universe. The interaction between Tsallis holographic dark energy and dark matter is also taken into account to discuss the underlying scenario. It is found that fractional dark energy density ([Formula: see text]) approaches to [Formula: see text] as redshift goes to zero (present epoch) for both cases of horizons. The cosmic parameter ([Formula: see text]) shows phantom crossing behavior in both models, i.e. it evolutes the universe from quintessence phase to phantom phase by crossing the vacuum horizon. In both cases of horizons, the deceleration supports the cosmic acceleration by exhibiting the negative values. The perturbation sound speed also favors the viability of our models through positive behavior. The coincidence parameter supports the exchange of dark matter to dark energy phenomenon through positivity. We also discuss the behavior of growth rate and growth index in the growth of matter density perturbations for both horizons. These parameters mimic the [Formula: see text]CDM behavior of the universe.
Computation of sandwiched relative α-entropy of two n-mode Gaussian states
Authors: Parthasarathy K. R.
Published: May 2022

A formula for the sandwiched relative [Formula: see text]-entropy [Formula: see text] for [Formula: see text], of two [Formula: see text]-mode Gaussian states [Formula: see text], [Formula: see text] in the boson Fock space [Formula: see text] is presented. This computation extensively employs the [Formula: see text]-parametrization of Gaussian states in [Formula: see text] introduced in Ref. 10.
An Efficient Sequential Reliability Analysis Method for Multidisciplinary System
Authors: Fu Chao , Liu Ji-Hong , Yu Hong-Yan
Published: May 2022

Multidisciplinary reliability analysis (MRA) is an important part of reliability-based multidisciplinary design optimization (RBMDO). In order to solve the problem of low computational efficiency caused by nested loops in multidisciplinary reliability analysis, an efficient multidisciplinary reliability analysis method based on decoupling idea is proposed. The method decouples system analysis, system sensitivity analysis and reliability analysis of Single discipline to form a recursive cycle, which eliminates the complete optimization calculation of reliability analysis model by system-level optimizer in each iteration. In the process of system sensitivity analysis, the global sensitivity equation is used to perform the concurrent subspace sensitivity analysis, which improves the efficiency of MRA. The decoupled multidisciplinary reliability analysis can get rid of the restriction that the traditional reliability analysis can only be carried out by using nonlinear optimization method, and the modified advanced mean value (MAMV) and other most probable point (MPP) search algorithms can be used to solve the reliability problem. Finally, three examples are used to verify the effectiveness of the method.
A Novel Approach for Identifying Hyper-Elastic Material Parameters of Cartilage based on FEM and Neural Networks
Authors: Ma Yu , Sang Jianbing , Li Yang , Wang Fang , Shao Kexin , Tian Weichang
Published: May 2022

Cartilage damage and degeneration may lead to osteoarthritis for both animals and humans. Quantitative studies on the nonlinear hyper-elastic behavior of cartilages are essential to evaluate cartilage tissue deterioration. However, direct identification of the material behavior is not feasible. This paper presents a procedure to characterize the nonlinear mechanical behavior of the cartilage tissue by an inverse method using measurable structural quantities. First, a two-way neural network (NN) is established, which uses the fully trained forward problem neural network instead of the forward problem solver to generate training samples for inverse problem neural network. Moreover, based on the experimental data of the kangaroo shoulder joint, a nonlinear finite element (FE) model is then created to produce a dataset for training the forward network. Furthermore, intensive studies are conducted to examine the performance of our two-way NN method for the prediction of cartilage hyper-elastic material parameters by comparison with the direct inverse NN method. When only the direct inverse problem neural network is used for training, all samples are from FE simulations and the simulation time is 50.7 h, and the prediction time is tens of seconds. Besides, our two-way neural network calls the trained forward NN to collect training samples, and all the samples can be obtained in seconds, with which the simulation time is only 78 s. The predicted results are in good agreement with the experimental data, and the comparison shows that our two-way NN is an efficient and proficient method to predict the parameters for other biological soft tissues.
CHANGES IN GLOBAL LAND USE AND CO2 EMISSIONS FROM US BIOETHANOL PRODUCTION: WHAT DRIVES DIFFERENCES IN ESTIMATES BETWEEN CORN AND CELLULOSIC ETHANOL?
Authors: MIGNONE BRYAN K. , HUSTER JONATHAN E. , TORKAMANI SARAH , O’ROURKE PATRICK , WISE MARSHALL
Published: May 2022

Land use change (LUC) CO2 emissions associated with bioenergy production depend on the amount of land required to produce bioenergy crops, the carbon stored in such crops (including in the leaves, stalk, roots and soil), and the carbon emitted when another land cover is directly or indirectly displaced as a result. In this study, we use a global integrated assessment model [the Global Change Analysis Model (GCAM)] to explore the differences in estimates of LUC CO2 emissions for two crops (corn and switchgrass) used to produce ethanol in the United States under alternative assumptions about natural lands protection. Varying the latter assumptions for corn ethanol results in net LUC CO2 emissions between 7 and 41 gCO2 per MJ of ethanol, whereas varying the same assumptions for switchgrass ethanol results in net emissions between [Formula: see text]26 and 14 gCO2 per MJ of ethanol. The low-end estimate for each occurs when natural lands are assumed to be fully protected everywhere, which leads to significant cropland intensification. The high-end estimate for each occurs when natural lands are assumed to be unprotected everywhere, leading to greater cropland expansion and associated conversion of unmanaged forest and pasture. Results from this study could be used to inform scenarios of future energy system change or life cycle assessment of biofuels for which LUC emissions would be an input.
Systematic Review of Comparative Studies of the Impact of Realism in Immersive Virtual Experiences
Authors: Gonçalves Guilherme , Coelho Hugo , Monteiro Pedro , Melo Miguel , Bessa Maximino
Published: May 2022

The adoption of immersive virtual experiences (IVEs) opened new research lines where the impact of realism is being studied, allowing developers to focus resources on realism factors proven to improve the user experience the most. We analyzed papers that compared different levels of realism and evaluated their impact on user experience. Exploratorily, we also synthesized the realism terms used by authors. From 1,300 initial documents, 79 met the eligibility criteria. Overall, most of the studies reported that higher realism has a positive impact on user experience. These data allow a better understanding of realism in IVEs, guiding future R&D.
Using Deep Learning Models to Detect Fake News about COVID-19
Authors: Chen Mu-Yen , Lai Yi-Wei , Lian Jiunn-Woei
Published: May 2022

The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of
Coronavirus Disease (COVID-19)
, a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the
World Health Organization (WHO)
, posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and
compare them based on different text feature selection
s. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The
long and short-term memory (LSTM)
model, the
gated recurrent unit (GRU)
model, and the
bidirectional long and short-term memory (BiLSTM)
model were selected for fake news detection. BiLSTM produces the best detection result,
with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82%
.
Effect of Nanoparticles on Wire Surface Coating Using Viscoelastic Third‐Grade Fluid as a Coating Polymer inside Permeable Covering Die with Variable Viscosity and Magnetic Field
Authors: Khan Zeeshan , Khan Ilyas , Ahammad N. Ameer , Basha D. Baba , Andualem Mulugeta
Published: May 2022

Goal. The parameters of coated wire products are determined by momentum and heat transmission inside dies. As a consequence, it is essential to understand the polymerization movement, heat mass transmission, and wall stress concentration. The wire covering technique necessitates a boost in thermal efficiency. As a result, the goal of this study is to see how nanomaterials affect the heat and mass transfer mechanisms of third‐grade liquid in wire coating analysis. The Buongiorno model is adopted for nanofluids. Methodology/approach. Continuity, momentum, energy, and nanoparticle volume fraction concentration is used to establish the governing equations. For highly nonlinear, the numerical methodology bvph2 technique is applied to yield numerical solutions. The impacts of the input parameters on motion, temperature, and volume fraction are examined using pictorial representations. Moreover, using the ND‐solve, the numerical results are validated analytically. Findings. In Reynolds Modeling, the stress on the entire wire surface integrated shear forces at the surface dominate Vogel’s model, according to the analytical conclusions of this inquiry. It is observed that the nanomaterials appear to have a favorable impact on wire force throughout the entire surface and shear forces at the surface. The polymer velocity can be increased using a non‐Newtonian parameter. The temperature profile is increased in the first half of the segment with larger values of random motion and nonlinear thermal while decreases in the later part. In addition, the Brownian motion component raises the concentration profile, but the thermophoresis factor decreases it. Practical implications. This research could aid in the advancement of wire coating technologies. Originality/value. For the first time, Brownian motion with generation/absorption slippage processes is used to investigate the importance of nanoparticles in wire coating assessment. Two different models are utilized for time‐dependent viscosity: Reynolds and Vogel models.
New Media Public Relations Regulation Strategy Model Based on Generative Confrontation Network
Authors: Lu Qingshuang
Published: May 2022

The rapid development of new media weakens the control of traditional news media on information. At the same time, under the impact of the rapid development of new media, it has brought new challenges to the regulation of public relations. In the new media environment, this paper constructs a text emotion generation model based on GAN to support the application of new media public relations regulation strategy. Aiming at the problem of insufficient constraint information of keywords in text generation, this paper uses the confrontation generation model based on reinforcement learning to supplement sentence components around keywords, so as to generate the text with the highest quality. At the same time, in order to extend GAN from continuous space to discrete space, the differentiable function based on Softmax transformation is adopted to approach the original nondifferentiable function. In this paper, LTP word segmentation system is used to select 356742 pieces of data with a length less than 20 for the experiment. Compared with Seqtoseq+attenion and Transform models, this model has higher similarity of real text distribution and higher text diversity. The retention degree of the main content of the input text is as high as 96.17%, which is higher than that of Seqtoseq+attenion model of 8.49% and Transform model of 6.11%. It provides effective support for the regulation of new media public relations.
Attention-Based Bi-DLSTM for Sentiment Analysis of Beijing Opera Lyrics
Authors: Jin Cong , Song Zhen , Xu Jiaqi , Gao Huiyue
Published: May 2022

We propose a sentiment analysis model based on Bi-DLSTM to solve the problem of sentiment analysis of Beijing Opera lyrics. A Bi-LSTM network with dilated recurrent skip connections (Bi-DLSTM) is introduced in this model, which can improve the ability to exact long-sequence information. The proposed model can learn the dependence of long sequences in different time dimensions and effectively improve the semantic extraction performance of lyrics. The attention mechanism is introduced to ensure the recognition of the more important words in the text sequence, which further improves the performance of the network. In order to solve the problem of lack of data on lyric sentiment analysis on the Internet, we build a dataset that can be used for lyric sentiment analysis. This paper completes multiple experiments on four datasets and verifies the effectiveness of the proposed model.
Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering
Authors: Mehrotra Tushar , Shukla Neha , Chaudhary Tarunika , Rajput Gaurav Kumar , Altuwairiqi Majid , Asif Shah Mohd
Published: May 2022

Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.
Theoretical Modeling of Composite Micro‐ and Nano‐Fiber Devices and Electronic Information Application Research
Authors: Yang Haibin , Liu Zhidong
Published: May 2022

With the continuous development of information science and technology, micro‐ and nano‐fiber optic sensing technology has been widely used in the fields of medicine, communication engineering, and environmental monitoring, and fiber optic devices are widely available in the market because of their advantages such as anti‐magnetic interference, corrosion resistance, light weight, high sensitivity, and transmission bandwidth. The purpose of this paper is to investigate the intrinsic correlation between micro‐ and nano‐optical fiber devices and electronic information, introduce the fabrication process of micro‐ and nano‐optical fiber, numerical simulations, and corresponding magnetic field experiments, explore the effects of polarization dispersion and polarization‐related dissipation on the system, and enhance the sensing characteristics of micro‐ and nano‐optical fiber through experimental design to maximize its functionality. The experimental results show that the refractive index and magnetic field exhibit a linear relationship with correlation coefficients of 0.995 and 0.994, respectively, when the external magnetic field is between 70 Oe and 300 Oe.
Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials
Authors: Kshirsagar Pravin R. , Kumar Neeraj , Almulihi Ahmed H. , Alassery Fawaz , Khan Asif Irshad , Islam Saiful , Rothe Jyoti P. , Jagannadham D. B. V. , Dekeba Kenenisa
Published: May 2022

Waste management is a critical problem for every country, whether it is developed or developing. Selecting and managing waste are a critical part of preserving the environment and maximizing resource efficiency. In addition to reducing trash and disposal, reusable items are predicted to be of great benefit since they lessen our dependence on raw materials. The usage of compostable trash may be expanded outside fertilizers and dung after the metallic, chemicals, and glass items have been recycled. After a good scrubbing, the glass may be broken down and remelted to create new items. Reusing waste items via garbage recovery is one of the best methods to do so. This document outlines the steps that must be taken to maximize the use of garbage. This work describes a reusable industrial robot arm for grasping and sorting things depending on the resources they contain. Gripping, motion control, and object material categorization are all integrated into a full-automation, reusable system architecture in this study. LeNet also was adjusted to classify garbage into cartons and plastics using an artificial intelligent technique, with the use of a customized LeNet model. Movement in terms of moving the robot in the most efficient way possible, the robot’s grabbing, and categorization were incorporated into the movement design process. The system’s grabbing and object categorization success rates and computation time are calculated as metrics for evaluation.
A Promising Insight: The Potential Influence and Therapeutic Value of the Gut Microbiota in GI GVHD
Authors: Li Jiahua , Zhang Xueyan , Chen Yiru , Zheng Qingqing , Zhao Mingyi , Jiang Hua
Published: May 2022

Allogeneic hematopoietic cell transplantation (allo-HSCT) is a reconstruction process of hematopoietic and immune functions that can be curative in patients with hematologic malignancies, but it carries risks of graft-versus-host disease (GVHD), thrombotic microangiopathy (TMA), Epstein–Barr virus (EBV) infection, cytomegalovirus infection, secondary hemophagocytic lymphohistiocytosis (sHLH), macrophage activation syndrome (MAS), bronchiolitis obliterans, and posterior reversible encephalopathy syndrome (PRES). Gastrointestinal graft-versus-host disease (GI GVHD), a common complication of allo-HSCT, is one of the leading causes of transplant-related death because of its high treatment difficulty, which is affected by preimplantation, antibiotic use, dietary changes, and intestinal inflammation. At present, human trials and animal studies have proven that a decrease in intestinal bacterial diversity is associated with the occurrence of GI GVHD. Metabolites produced by intestinal bacteria, such as lipopolysaccharides, short-chain fatty acids, and secondary bile acids, can affect the development of GVHD through direct or indirect interactions with immune cells. The targeted damage of GVHD on intestinal stem cells (ISCs) and Paneth cells results in intestinal dysbiosis or dysbacteriosis. Based on the effect of microbiota metabolites on the gastrointestinal tract, the clinical treatment of GI GVHD can be further optimized. In this review, we describe the mechanisms of GI GVHD and the damage it causes to intestinal cells and we summarize recent studies on the relationship between intestinal microbiota and GVHD in the gastrointestinal tract, highlighting the role of intestinal microbiota metabolites in GI GVHD. We hope to elucidate strategies for immunomodulatory combined microbiota targeting in the clinical treatment of GI GVHD.
Generalizations of Fuzzy q‐Ideals of BCI‐Algebras
Authors: Muhiuddin G. , Al-Kadi D. , Mahboob A. , Assiry A. , Alsubhi Abdullah
Published: May 2022

In this paper, we introduce the notion of (∈, ∈∨(κ∗, qκ))‐fuzzy q‐ideals of BCI‐algebras to propose a more general form of fuzzy q‐ideals of BCI‐algebras. We prove that (∈, ∈∨q)‐fuzzy q‐ideals and (∈∨(κ∗, qκ), ∈∨(κ∗, qκ))‐fuzzy q‐ideals are (∈, ∈∨(κ∗, qκ))‐fuzzy q‐ideals, but the converse assertion is not valid and examples are given to support this. It is proved that every (∈, ∈∨(κ∗, qκ))‐fuzzy q‐ideal is an (∈, ∈∨(κ∗, qκ))‐fuzzy ideal, but the converse need not be true in general and an example is provided. In addition, correspondence between (∈, ∈∨(κ∗, qκ))‐fuzzy q‐ideals and q‐ideals of BCI‐algebras is considered.
Effect of Micropower Vacuum Dressing on Promoting Wound Healing in Patients with I-II Diabetic Foot
Authors: Chen Cunren , Wang Xixiong , Liang Changli , Liu Haiwei
Published: May 2022

Objective. Discuss the effectiveness and value of micropower vacuum dressing (MVD) in promoting the healing of I-II grades diabetic foot wounds. Methods. Sixty patients diagnosed with diabetic foot ulcers and Wagner grades I-II were selected and randomly divided into the control group and experimental group, with 30 cases in each group. The control group was covered with conventional treatments and petrolatum gauze dressings, and the experimental group was treated with MVD on the basis of conventional reatments. The therapeutic effects of the two groups were observed, including healing rate, ulcer area reduction rate, ulcer healing time, dressing change times, ulcer recurrence rate, adverse events, and so on. Results. The healing rate (100%) of the experimental group was higher than that of the control group (56.7%); the wound reduction rate was higher than that of the control group (

P
<
0.05

); the healing time, the number of dressing changes, and the 1-month recurrence rate were all low in the control group (

P
<
0.05

). The incidence of adverse reactions in the experimental group (6.7%) was lower than that in the control group (46.7%) (

P
<
0.05

). Conclusion. MVD has significant effects in the treatment of I-II grades diabetic foot wounds and has few adverse reactions. It is an effective new method that can promote the growth of granulation tissue and epithelium and promote wound healing.
A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks
Authors: Ali Syed Asif , Sarfraz Mubashar , Ghauri Sajjad A. , Mahmood Asad , Basir Shahid , Kebedew Teweldebrhan Mezgebo , Alam Sheraz
Published: May 2022

The wireless sensor network’s (WSNs) lifetime is mainly dependent on the RE of the sensor nodes (SeN). In recent years, energy minimization in a WSN has been a prominent research topic, and numerous solutions have been proposed. This research focuses on the energy minimization of the SeNs where firstly, K-medoid clustering algorithm is applied to create clusters. Second, a weighted cluster head selection technique is used to choose a cluster head (CH) by integrating three independent weights associated with an SeN: energy, distance from the centroid, and distance from the sink node (SN). According to the energy level and distance from the SN and cluster’s centre, each node is assigned a constant weight. The simulation results are compared to existing methodologies, and the results show that the suggested network’s lifetime enhances.
circHtra1/miR-3960/GRB10 Axis Promotes Neuronal Loss and Immune Deficiency in Traumatic Brain Injury
Authors: Zheng Ping , Shu Liang , Ren Dabin , Kuang Zhucai , Zhang Yisong , Wan Jian
Published: May 2022

Circular RNAs (circRNAs) are abundant in the brain and contribute to central nervous system diseases; however, the exact roles of circRNAs in human traumatic brain injury (TBI) have not been established. In this study, we used a competing endogenous RNA (ceRNA) chipset as well as in vitro and in vivo assays to characterize differentially expressed circRNAs in TBI. We detected 3035 differentially expressed circRNAs in the severe TBI group, 2362 in the moderate group, and 433 in the mild group. A ceRNA network was constructed. The circRNA has_circ_0020269 (circHtra1) was significantly upregulated after brain insults and was correlated with the severity of injury. circHtra1 inhibited cell proliferation and promoted apoptosis, and its knockdown reversed these effects. Further analyses revealed that circHtra1 functions as a miR-3960 sponge and increases the expression of GRB10, which is involved in NK cell infiltration after TBI. circHtra1 was identified as a target of the IGF-1/ADAR1 axis. Reduced expression of ADAR1 (involved in A-to-I editing) after brain insults upregulated circHtra1. Our results show that circHtra1 promotes neuronal loss by sponging miR-3960 and regulating GRB10 and apoptosis during brain insults. In addition, A-to-I editing could regulate circRNA expression profiles after TBI, and circHtra1 is a potential therapeutic target.
Valorization of Water-Based Drill Cuttings through the Bio-Carbonation Approach
Authors: Tang Guowang , Huang Feng , Wang Guihe , Song Zhengyang , Jia Cangqin , Yu Peizhi , Sun Yongshuai
Published: May 2022

This study proposed a novel bio-carbonation method to recycle water-based drill cutting (WDC) to prepare samples, which contains reactive magnesia (MgO) cement (RMC), ground granulated blast furnace slag (GGBS), and fly ash (FA), with the adoption of microbially induced carbonate precipitation (MICP). Through the investigation of some parameters (i.e., GGBS content, FA content, and curing time), the microstructures and strength development of bio-carbonated RMC-based WDC samples were evaluated. The preliminary results revealed that bio-carbonated RMC-based WDC samples outperformed the control group (i.e., without bio-carbonation) in terms of the 28-day strength (i.e., 9.8 MPa versus 4.4 MPa), which can be assigned to formation of the carbonates, that is, hydrated magnesium carbonates (HMCs). Further, in addition to the identification of HMCs, the microstructural analysis also revealed a continuous carbonate network due to the presence of HMCs, which accounts for the strength boost of samples.
[Retracted] Application of TOPSIS Method Combined with Grey Relational Degree in the Selection of Snow‐Melting Agent Use Plan
Authors: Yang Haiyan , Huang Mingyang , Zhou Guangyu , Chen Xiaowen , Ming Ruitao
Published: May 2022

With the rapid development of China’s ice and snow industry, the use of snow‐melting agents as a nonpoint source pollution source is increasing, and the impact on the environment cannot be ignored. This paper expounds the agent, purpose, mechanism, and current situation of snow‐melting agents at home and abroad and analyzes the national standards, industry standards, and technical indicators of various provinces and cities. This paper puts forward the standard of snow‐melting agents, standardizes the production management and detection method of snow‐melting agents, improves the quality of snow‐melting agent products, and solves the adverse effects of environmental pollution of snow‐melting agents. In order to select an economical, effective, and environmentally friendly snow‐melting agent application plan that is suitable for cold mountainous areas, the article proposes a TOPSIS method evaluation model with the purpose of source control combined with grey correlation. Calcium, magnesium chloride, chloride salt mixed snow‐melting agent (1 : 1 : 1 ratio), and calcium and magnesium acetate are used to melt snow in four specific environments (high cold and high sensitivity, high cold and low sensitivity, low cold and high sensitivity, and low cold and low sensitivity). The pros and cons of the dosage regimen are evaluated. The combined weighting method is used to overcome the shortcomings of the individual subjective and objective weighting methods, and it is concluded that calcium and magnesium acetate is the optimal solution in high‐cold and high‐sensitivity areas, calcium chloride is the optimal solution in high‐cold and low‐sensitivity areas, and magnesium chloride is the optimal solution in low‐cold and high‐sensitivity areas. As the optimal solution, calcium chloride with low cold and low sensitivity is the optimal solution.
Highly Efficient Multicast over Surface Wave in Hybrid Wireless-Optical On-Chip Networks for IoT HPC
Authors: Zhu Shengyan , Zhou Caiqiu , Wang Yongjian
Published: May 2022

As an ideal platform for Internet of Things (IoT) High Performance Computing (HPC), the Network-on-Chip (NoC) implements the parallelized task processing of Intellectual Property (IP) cores. However, the data interaction of IoT HPC has increased exponentially, resulting in highly consumed energy and bandwidth of the electrical NoC. This problem can be solved by the Optical NoC (ONoC), but the flexibility should be further improved for the multicast message transmission. We design a novel hybrid wireless-optical on-chip network with an effective routing mechanism for the message multicast transmission of IoT HPC. We utilize the wireless single-hop transmission to reduce on-chip communication distance and to forward multicast messages. For the high attenuation on-chip wireless signal, we design an on-chip surface waveguide structure for the surface wave as a more reliable wireless transmission medium. A flexible routing mechanism is also proposed to ensure efficient coordination between optical and wireless layers, thus relieving the pressure of the single-layer message transmission. The simulation results show that, compared with the traditional ONoC using mesh topology, our scheme performs better in terms of average delay, average energy consumption, and network throughput.
Assessing the Field Curing Behavior of Cold Recycled Asphalt Mixtures
Authors: Graziani Andrea , Grilli Andrea , Mignini Chiara , Balzi Alex
Published: May 2022

Cold recycling techniques in road construction have been of rising interest because of huge environmental benefits in terms of energy saving, emission reduction, and preservation of natural resources. The improvement on selection and quality of the raw materials and binders as well as the enhancement of the knowledge on mixture performance are leading to the intensive use of cold recycling techniques in place of traditional ones. One of the main differences between cold and hot techniques is the evolutive development of performance, generally identified as curing process, due to the presence of water and hydraulic binders in the cold mixtures. Environmental-related factors such as temperature, humidity, wind, and rainfall significantly influence the curing process and, as consequence, the mixture properties over time. Several laboratory curing protocols have been developed by universities, research centers, and agencies, but a clear relationship between simulative procedures in laboratory and the field is still an open challenge. This paper aims at characterizing the short-term and midterm curing behavior of a cold recycled material (CRM) mixture. A CRM mixture was selected and characterized in laboratory, and the in-plant production procedure was validated. The CRM mixture was used to build the binder course of an instrumented pavement in the Republic of San Marino. Volumetric and mechanical testing on laboratory-produced specimens and cores were analyzed considering the temperature and moisture sensor measurements in field. Results address to a new insight for laboratory procedures and prediction models matching laboratory conditions and the real curing condition in field.