bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Local Microstructural Variations on the Bendability of Aluminum Extrusions: Experiments and Crystal Plasticity Analyses
Authors: Håkon Frodal Bjørn , Lodgaard Lars , Langsrud Yngve , Børvik Tore , Sture Hopperstad Odd
Published: December 2022

The bendability of extruded profiles of an age hardenable aluminum alloy is investigated using mechanical tests on flat tensile specimens and bending specimens. Two profile geometries are considered, where the profiles exhibit different grain structures. The microstructure of the profiles in terms of the crystallographic texture and constituent particles is otherwise comparable. While the tensile properties are not that different for the two profiles, their bendability is strongly dependent on the grain structure and is about twice as high for one profile than for the other. A newly proposed coupled damage and single crystal plasticity model is used in finite element analyses of the mechanical tests to investigate the influence of the grain structure on the bending behavior, and the numerical results are compared to the experimental tests. The crystallographic texture and the grain morphology of the profiles, found by the electron back-scatter diffraction technique, are explicitly represented in the finite element models. The crystal plasticity simulations capture the difference in the bendability of the two profiles, and in agreement with the experiments predict a considerably higher bendability for one of the profiles. It is found that the grain structure affects the shear band formation in these profiles, but also the local texture where the shear bands are located is important for crack initiation and propagation as grains with certain crystallographic orientations may have a higher fracture resistance.
Static Response of Circular Cavities With a Non-Radial Crack Subject to Antiplane Stress
Authors: Lin Chi-Pu , Wiebe Richard , Chang Kao-Hao
Published: December 2022

Tunnels, pipelines, and other subterranean circular cavities are common components of modern infrastructure. In addition, seismic activities are common in many areas with pipelines, which may put these structures under unknown risk of fracture. A particular risk case of interest can be characterized as a plane strain problem with a circular cavity and crack in an infinite plane under antiplane stress. Antiplane, i.e., mode III, loading has seen less study relative to modes I and II due to the lower risk factor in structures that are especially vulnerable to fracture (e.g., in the automotive and aerospace industries), and the increase in complexity compared to modes I and II. The work here further explores this phenomenon on circular cavities, and particularly, the effect of non-radial cracks on the stress intensity factor via a parametric study. The study introduces a semi-analytical method and also uses commercial finite element software to further expand on the investigation.
Hybrid Multi-Scale Convolutional Long Short-Term Memory Network for Remaining Useful Life Prediction and Offset Analysis
Authors: Sharma Vedant , Sharma Deepak , Anand Ashish
Published: December 2022

Prognostic and health management (PHM) has become increasingly popular due to the requirement of improved maintenance techniques in the industry. Remaining useful life (RUL) estimation is an important parameter through which PHM can be utilized to implement timely and cost-effective maintenance. Due to recent advancements in sensor-based and other Industry 4.0 related technologies, data-driven methods for RUL estimation have become more prevalent and effective. In this paper, a novel data-driven method for sensor-based RUL estimation using a combination of multi-scale convolutional neural network (MS-CNN) and long short-term memory (LSTM) is proposed. The proposed hybrid multi-scale convolutional LSTM (HMCL) model is capable of extracting both spatial features of various scales and temporal features from the input data to provide accurate RUL predictions. L2 regularization and dropout techniques are used to reduce overfitting. The performance of the proposed model is evaluated using the C-MAPSS dataset. It achieves excellent performance as compared to other state-of-the-art methods making it a promising approach for sensor-based RUL prediction. Additionally, to discern the cause for occurrence of offsets, i.e., deviations in the model’s predictions with the true RUL value, an offset analysis is carried out. Through the analysis, an estimate on the location and cause of offsets is established and based on the sensory input data, offsets are identified using an SVM classification model. Despite being a simple classification model, it is able to achieve a decent performance in classifying the offsets.
Deep Learning Methods of Cross-Modal Tasks for Conceptual Design of Product Shapes: A Review
Authors: Li Xingang , Wang Ye , Sha Zhenghui
Published: December 2022

Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes through design search, creation, and integration. In this stage, product shape design is one of the most paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design modality to another, we see opportunities to develop artificial intelligence (AI) to assist the design of product shapes in a new paradigm. In this paper, we conduct a systematic review of the retrieval, generation, and manipulation methods for DLCMT that involve three cross-modal types: text-to-3D shape, text-to-sketch, and sketch-to-3D shape. The review identifies 50 articles from a pool of 1341 papers in the fields of computer graphics, computer vision, and engineering design. We review (1) state-of-the-art DLCMT methods that can be applied to product shape design and (2) identify the key challenges, such as lack of consideration of engineering performance in the early design phase that need to be addressed when applying DLCMT methods. In the end, we discuss the potential solutions to these challenges and propose a list of research questions that point to future directions of data-driven conceptual design.
Dynamic Cavitation Inception by Wave Propagation Across Solid–Fluid Interface With Varying Solid Surface Wettability
Authors: Kojima Tomohisa , Inaba Kazuaki
Published: December 2022

Fluid–structure interaction (FSI) problems are important because they may induce serious damage to structures. In some FSI problems, the interaction mechanism is strongly dependent on the wave propagation across the solid–fluid interface. In this study, we attempted a quantitative evaluation of the effect of the solid surface wettability on the wave propagation across the solid–fluid interface with FSI in the case of longitudinal wave propagation vertically toward the interface. During the experiments, while the water was continuously compressed by the solid buffer motion, cavitation bubbles appeared being originated from the buffer–water interface as a result of the transmitted tensile wave propagating across the interface in a cycle. It was confirmed that interfacial boundary condition as wettability could change the wave transmission behavior owing to changes in the cavitation occurrence. It was also confirmed that the worse the wettability, the more severe the cavitation intensity, and the greater the difference between the energy lost by the buffer and the energy stored in the water. Consequently, the effect of the cavitation inception on the wave propagation at the solid–fluid interface with FSI could be quantitatively evaluated by considering the energy transferred from the solid to the water.
Turbine Vane Passage Cooling Experiments With a Close-Coupled Combustor-Turbine Interface Geometry—Part I: Describing the Flow
Authors: Nawathe Kedar P. , Nath Aaditya R. , Kim Yong W. , Simon Terrence W.
Published: December 2022

Due to the proximity of the first-stage gas turbine vanes to the combustor, coolant introduced to the combustor walls interacts with the endwall film coolant and changes the vane passage flow physics. Recent results show that combustor coolant contributes significantly to cooling the endwall and vane surfaces. In this paper, the traditional combustor-turbine interface was modified to improve overall cooling performance. The performance of this new injection cooling scheme on passage fluid dynamics and surface cooling is assessed. The first of this two-part paper reports detailed experimental tests that document secondary flows and coolant transport throughout the vane passage for four combustor coolant flowrates. The experimental facility imitates combustor coolant injection and engine-level turbulence and has a modified transition duct design, called the “close-coupled combustor-turbine interface.” The “impingement vortex” seen in previous studies with combustor cooling appears as the dominant secondary flow. It is observed in the present study over a wide range of flowrates, confirming its tie to the combustor coolant flowrate and not the combustor-turbine interface geometry. It was found, however, that the location and size of the impingement vortex are affected by coolant flowrate. Part II of this paper discusses the impact of the observed secondary flows on cooling vane passage surfaces.
Robust unsupervised Tursiops aduncus whistle enhancement based on complete ensembled empirical optimal envelope local mean decomposition with adaptive noise
Authors: Li Lei , Wang Qian , Qing Xin , Qiao Gang , Liu Xinyu , Liu Songzuo
Published: December 2022

Whistle enhancement is an essential preprocessing step in studying dolphin behavior and population distributions. We propose a robust unsupervised whistle enhancement scheme based on improved local mean decomposition using adaptive noise estimation and logarithmic spectral amplitude. First, to further mitigate the mode aliasing problem effect in whistle signal decomposition and achieve better spectral separation of modes, we present a complete ensembled empirical optimal envelope local mean decomposition with adaptive noise algorithm. According to the envelope characteristics of the whistle signals, the proposed algorithm optimally and adaptively decomposes the noisy signal into product functions (PFs) with amplitude and frequency modulation. Second, the whistle enhancement framework consists of the improved minima-controlled recursive averaging for adaptive noise estimation, optimally modified log-spectral amplitude for each noisy product function enhancement, and the Hurst index for reconstructing pure whistle signal estimations with the least damaged PFs. Finally, the proposed scheme is applied to a dataset of long calls from two Tursiops aduncus individuals. After constructing the pure whistle dataset, the experimental results show that the proposed scheme performs better than other compared whistle enhancement schemes under different signal-to-noise ratios.
A noise-based high-resolution time-reversal method for acoustic defect localization in water pipes
Authors: Gozum Murat M. , Nasraoui Saber , Grigoropoulos Georgios , Louati Moez , Ghidaoui Mohamed S.
Published: December 2022

Pressurized water supply pipeline systems (PWSPS) are quintessential to human development and sustenance, but suffer a multitude of unresolved defects (i.e., leaks, blockages, etc.) due to aging and inaccessibility. In this paper, the ubiquitous high-frequency background noise in PWSPS is harnessed to introduce a high-resolution, passive defect detection technique. The relation between the acoustic Green's and cross correlation functions for a pressurized water pipe is derived for the case of high frequency waves, i.e., acoustic wavelengths smaller than the pipe diameter. This relation is subsequently used to formulate a time-reversal technique for localizing anomalies such as small variations in pipe wall impedance and through-wall defects (i.e., leaks). It is shown that the derived relationship between the cross correlation and acoustic Green's functions enables very accurate defect detection and localization by measuring the background noise at two locations along a pipe. This is an important result given that (i) there is limited access to buried PWSPS to conduct high-frequency active defect detection, and (ii) traditional methods to actively probe pipes (e.g., valve maneuverer) are low-resolution (tens to hundreds of meters) and often result in pipe overloading and fatigue.
IFITM proteins assist cellular uptake of diverse linked chemotypes
Authors: Lou Kevin , Wassarman Douglas R. , Yang Tangpo , Paung YiTing , Zhang Ziyang , O’Loughlin Thomas A. , Moore Megan K. , Egan Regina K. , Greninger Patricia , Benes Cyril H. , Seeliger Markus A. , Taunton Jack , Gilbert Luke A. , Shokat Kevan M.
Published: December 2022

The search for cell-permeable drugs has conventionally focused on low-molecular weight (MW), nonpolar, rigid chemical structures. However, emerging therapeutic strategies break traditional drug design rules by employing flexibly linked chemical entities composed of more than one ligand. Using complementary genome-scale chemical-genetic approaches we identified an endogenous chemical uptake pathway involving interferon-induced transmembrane proteins (IFITMs) that modulates the cell permeability of a prototypical biopic inhibitor of MTOR (RapaLink-1, MW: 1784 g/mol). We devised additional linked inhibitors targeting BCR-ABL1 (DasatiLink-1, MW: 1518 g/mol) and EIF4A1 (BisRoc-1, MW: 1466 g/mol), uptake of which was facilitated by IFITMs. We also found that IFITMs moderately assisted some proteolysis-targeting chimeras and examined the physicochemical requirements for involvement of this uptake pathway.
Liquid metal synthesis solvents for metallic crystals
Authors: Idrus-Saidi Shuhada A. , Tang Jianbo , Lambie Stephanie , Han Jialuo , Mayyas Mohannad , Ghasemian Mohammad B. , Allioux Francois-Marie , Cai Shengxiang , Koshy Pramod , Mostaghimi Peyman , Steenbergen Krista G. , Barnard Amanda S. , Daeneke Torben , Gaston Nicola , Kalantar-Zadeh Kourosh
Published: December 2022

In nature, snowflake ice crystals arrange themselves into diverse symmetrical six-sided structures. We show an analogy of this when zinc (Zn) dissolves and crystallizes in liquid gallium (Ga). The low-melting-temperature Ga is used as a “metallic solvent” to synthesize a range of flake-like Zn crystals. We extract these metallic crystals from the liquid metal solvent by reducing its surface tension using a combination of electrocapillary modulation and vacuum filtration. The liquid metal–grown crystals feature high morphological diversity and persistent symmetry. The concept is expanded to other single and binary metal solutes and Ga-based solvents, with the growth mechanisms elucidated through ab initio simulation of interfacial stability. This strategy offers general routes for creating highly crystalline, shape-controlled metallic or multimetallic fine structures from liquid metal solvents.
Rapid weed adaptation and range expansion in response to agriculture over the past two centuries
Authors: Kreiner Julia M. , Latorre Sergio M. , Burbano Hernán A. , Stinchcombe John R. , Otto Sarah P. , Weigel Detlef , Wright Stephen I.
Published: December 2022

North America has experienced a massive increase in cropland use since 1800, accompanied more recently by the intensification of agricultural practices. Through genome analysis of present-day and historical samples spanning environments over the past two centuries, we studied the effect of these changes in farming on the extent and tempo of evolution across the native range of the common waterhemp (
Amaranthus tuberculatus
), a now pervasive agricultural weed. Modern agriculture has imposed strengths of selection rarely observed in the wild, with notable shifts in allele frequency trajectories since agricultural intensification in the 1960s. An evolutionary response to this extreme selection was facilitated by a concurrent human-mediated range shift. By reshaping genome-wide diversity across the landscape, agriculture has driven the success of this weed in the 21st century.
Out of the ashes
Authors: Stone Richard
Published: December 2022

Slavutych was built after the nuclear meltdown at Chornobyl. The town, scarred by war, is planning a research revival
Human Listeriosis
Authors: Koopmans Merel M. , Brouwer Matthijs C. , Vázquez-Boland José A. , van de Beek Diederik
Published: December 2022

Listeria monocytogenesis a Gram-positive facultative intracellular pathogen that can cause severe invasive infections upon ingestion with contaminated food. Clinically, listerial disease, or listeriosis, most often presents as bacteremia, meningitis or meningoencephalitis, and pregnancy-associated infections manifesting as miscarriage or neonatal sepsis.
Multifaceted Interplay between Hfq and the Small RNA GssA in
Pseudomonas aeruginosa
Authors: Santoro Silvia , Paganin Costanza , Gilardi Sara , Brignoli Tarcisio , Bertoni Giovanni , Ferrara Silvia
Published: December 2022

The pathogenic lifestyle of the bacterium
Pseudomonas aeruginosa
, a leading cause of life-threatening infections in the airways of cystic fibrosis patients, is based on the fine regulation of virulence-associated factors. Regulatory small RNAs (sRNAs) and the RNA-binding protein Hfq are recognized key components within the
P. aeruginosa
regulatory networks involved in host-pathogen interaction.
Reduction of Redox Potential Exerts a Key Role in Modulating Gut Microbial Taxa and Function by Dietary Supplementation of Pectin in a Pig Model
Authors: Xu Rongying , Li Qiuke , Wang Hongyu , Su Yong , Zhu Weiyun
Published: December 2022

Collective studies indicate that pectin degradation promotes extensive microorganisms that can be involved in pectin degradation directly or indirectly, or benefit from the altered physiological conditions caused by pectin ingestions. Our study focuses on effects of pectin on gut microbial taxa and functions, as well as its interactions with altered environmental features.
Mechanism of Action of Isopropoxy Benzene Guanidine against Multidrug-Resistant Pathogens
Authors: Li Jie , Zhang Xiufeng , Han Ning , Wan Peng , Zhao Feifei , Xu Tiantian , Peng Xianfeng , Xiong Wenguang , Zeng Zhenling
Published: December 2022

As antibiotic discovery stagnates, the world is facing a growing menace from the emergence of bacteria that are resistant to almost all available antibiotics. The key to winning this race is to explore distinctive mechanisms of antibiotics.
The Pondosa fault zone: A distributed dextral-normal-oblique fault system in northeastern California, USA
Authors: Jobe Jessica Thompson , Briggs Richard , Gold Ryan , DeLong Stephen , Hille Madeline , Delano Jaime , Johnstone Samuel A. , Pickering Alexandra , Phillips Rachel , Calvert Andrew T.
Published: December 2022

The tectonic domains of Basin and Range extension, Cascadia subduction zone contraction, and Walker Lane dextral transtension converge in the Mushroom Rock region of northeastern California, USA. We combined analysis of high-resolution topographic data, bedrock mapping, 40Ar/39Ar geochronology, low-temperature thermochronology, and existing geologic and fault mapping to characterize an extensive dextral-normal-oblique fault system called the Pondosa fault zone. This fault zone extends north-northwest from the Pit River east of Soldier Mountain, California, into moderately high-relief volcanic topography as far north as the Bartle (California) townsite with normal and dextral offset apparent in geomorphology and fault exposures. New and existing 40Ar/39Ar and radiocarbon dating of offset lava flows provides ages of 12.4 ka to 9.6 Ma for late Cenozoic stratigraphic units. Scarp morphology and geomorphic expression indicate that the fault system was active in the late Pleistocene. The Pondosa fault zone may represent a dextral-oblique accommodation zone between north-south–oriented Basin and Range extensional fault systems and/or part of the Sierra Nevada–Oregon Coast block microplate boundary.
Erratum to: Specifics of Chemical Composition Origin of Surface Water in the Arctic Zone of Western Siberia
Authors: Soldatova E. A. , Ivanova I. S. , Kolubaeva Yu. V. , Sokolov D. A.
Published: December 2022

An Erratum to this paper has been published: https://doi.org/10.1134/S0016702922190016
UrinaryActinomycesinfection with a urachal remnant
Authors: Kashiwagi Riku , Watari Takashi , Oshiro Akiko , Kikuchi Koki
Published: December 2022

Urinary Gram staining is helpful in patients with complicated urinary retention, such as prolonged urinary retention and coexisting ureteral retention disease. If gram-positive rods are observed on urine Gram staining in a patient with urinary tract infection, an anaerobic culture of the urine may be helpful for early diagnosis and treatment ofActinomycesinfection.
Sex-related differences and hormonal effects in the Dry Eye Assessment and Management (DREAM) study
Authors: Zhao Megan , Yu Yinxi , Roy Neeta S , Ying Gui-shuang , Asbell Penny , Bunya Vatinee Y
Published: December 2022

Background/aims

To compare dry eye disease (DED) signs and symptoms between men and women, as well as between premenopausal and postmenopausal women, in the Dry Eye Assessment and Management (DREAM) study.

Methods

434 women and 101 men self-reported prior medical history and underwent a standardised DED assessment using the Ocular Surface Disease Index, Brief Pain Inventory, Tear Break-Up Time (TBUT)(s), Schirmer’s test 2 (mm/5 min), National Eye Institute-graded lissamine conjunctival staining, corneal staining, meibomian gland dysfunction evaluation and tear osmolarity (mOsms/L) at baseline, 6 months and 12 months. Multivariable linear regression models were used to compare these scores.

Results

Women experienced significantly worse DED signs than men with lower Schirmer’s test scores (9.27 vs 12.16; p<0.001), higher corneal staining scores (3.59 vs 2.70; p=0.006) and worse composite DED sign scores (0.52 vs 0.40; p<0.001). Postmenopausal women experienced significantly worse DED signs than premenopausal women with higher corneal staining scores (3.74 vs 2.58, p<0.001), higher conjunctival staining scores (2.80 vs 2.22, p<0.001), higher tear osmolarity (304 vs 299, p=0.004), lower TBUT (3.37 vs 3.93, p=0.047), worse meibomian gland dysfunction (3.05 vs 2.62, p=0.04) and worse composite DED sign scores (0.54 vs 0.42, p<0.001). There were no significant differences in DED symptoms between sex and between premenopausal and postmenopausal women (all p≥0.08).

Conclusion

In the DREAM study, women experienced more severe DED signs than men. Further, postmenopausal women presented with more severe DED signs than premenopausal women. Elucidating these differences may improve DED diagnosis and provide future direction in understanding sex-related differences in DED.

Trial registration number

NCT02128763.
Changing faces of mitochondrial disease: autosomal recessivePOLGdisease mimicking myasthenia gravis and progressive supranuclear palsy
Authors: Elwan Menatalla , Schaefer Andrew M , Craig Kate , Hopton Sila , Falkous Gavin , Blakely Emma L , Taylor Robert W , Warren Naomi
Published: December 2022

Background

Mitochondrial disorders are known to cause diverse neurological phenotypes which cause a diagnostic challenge to most neurologists. Pathogenic polymerase gamma (POLG) variants have been described as a cause of chronic progressive external ophthalmoplegia, which manifests with ptosis, horizontal and vertical eye movement restriction and myopathy. Autosomal dominant progressive external ophthalmoplegia is rarely associated with Parkinsonism responsive to levodopa.

Methods

We report a case of a 58-year-old man who presented with an eye movement disorder then Parkinsonism who made his way through the myasthenia then the movement disorder clinic.

Results

A diagnostic right tibialis anterior biopsy revealed classical hallmarks of mitochondrial disease, and genetic testing identified compound heterozygous pathogenic gene variants in thePOLGgene. The patient was diagnosed with autosomal recessivePOLGdisease.

Conclusions

It is important to maintain a high index of suspicion of pathogenicPOLGvariants in patients presenting with atypical Parkinsonism and ophthalmoplegia. Patients withPOLG-related disease will usually have ptosis, and downgaze is typically preserved until late in the disease. Accurate diagnosis is essential for appropriate prognosis and genetic counselling.
Going high and low: on pluralism and neutrality in human embryology policy-making
Authors: Ismaili M'hamdi Hafez , Rivron Nicolas C , Asscher Eva CA
Published: December 2022

Formulating sound and acceptable embryo research policy remains challenging especially in a pluralistic world. This challenge has acquired a new dimension of complexity with the advent of so-called embryo models, which are derived from stem cells. In this article, we present a normative strategy to facilitate the process of sound policy-making in the field of human embryology. This strategy involves seeking neutral agreements on higher level theories and doctrines as well as seeking agreements on the level of concrete policy proposals. We call this strategy: going high and low. By going high and low, the plurality of reasonable moral and epistemic convictions of stakeholders involved in the domain of human embryology is respected while the process of policy-making in this area is improved.
2-Undecanone alleviates asthma by inhibiting NF-κB pathway
Authors: Song Guihua , Yu Suping , Zhang Yan , Sun Mengmeng , Zhang Bingxue , Peng Minghao
Published: December 2022

Asthma is characterized by airway inflammation and remodeling. 2-Undecanone (methyl nonyl ketone), a volatile organic compound originating from Houttuynia cordata, has the potential to ameliorate inflammatory diseases. This study aimed to explore potential benefits of 2-undecanone in asthma. 2-Undecanone (100, 200, or 400 mg/kg) was administered intragastrically to ovalbumin (OVA)-challenged BALB/c mice. Lung tissues were collected to observe histopathological changes, and bronchoalveolar lavage fluid (BALF) was collected for the detection of inflammatory cells and cytokine production. The results showed that 2-undecanone ameliorated OVA-induced pathologic changes of lungs, including reducing inflammatory cell infiltration, goblet cell hyperplasia, and airway smooth muscle thickness. The number of inflammatory cells and the levels of IL-4, IL-5, IL-13, and IgE in BALF were decreased by 2-undecanone in asthmatic mice. Furthermore, abnormal activation of NF-κB pathway in lung tissues of asthmatic mice was impeded by 2-undecanone. In vitro, 2-undecanone (12.5, 25, or 50 µM) suppressed platelet-derived growth factor-BB-induced proliferation and migration of primary airway smooth muscle cells (ASMCs), and inhibited the switching of ASMCs from contractile phenotype to synthetic phenotype. Consistently, 2-undecanone blocked NF-κB activation in ASMCs. Collectively, 2-undecanone relieves asthma through alleviating airway inflammation and remodeling, and this beneficial effect is achieved by inhibiting NF-κB pathway.
A review of the genetic, physiological, and agronomic factors influencing secondary dormancy levels and seed vigour in Brassica napus L.
Authors: Brown Caroline H. , Gulden Rob H. , Shirtliffe Steve J. , Vail Sally L.
Published: December 2022

Dormancy in canola ( Brassica napus L.) is a complicated process due to many overlapping and interacting factors affecting the absolute dormancy levels. It is unknown if seed dormancy plays a role in the poor stand establishment of planted canola but given that germination and dormancy are two ends of the same continuum, it has been suggested that dormancy may be a culprit of poor establishment. This review synthesizes literature pertaining to secondary dormancy in B. napus and the interaction of genetic, physiological, environmental, and agronomic factors. Seed germination and vigour and the interaction with dormancy are also addressed. The persistence of volunteer canola seed in the soil seedbank is a result of the induction of canola seed into secondary dormancy under adverse environmental conditions such as low temperature or low light. Genetics is a major influencing factor on absolute secondary dormancy (∼50%) in canola. Plant hormones abscisic acid and gibberellic acid and their interactions also influence dormancy with highly dormant genotypes having increased abscisic acid concentration in the seed. Seed sugars, seed storage proteins, glucosinolate content, and growth habit are all additional factors affecting absolute dormancy in B. napus. Furthermore, maternal environmental conditions affect dormancy levels. In addition to genetic, physiological, and environmental factors, farming practices such as harvest timing, and tillage regimes can influence secondary dormancy of canola seed that has entered the seedbank unintentionally. Given the documented high heritability of secondary dormancy, it is feasible to reduce secondary dormancy in canola cultivars; however, consideration of all interacting factors must be given.
MODULATION OF CARBON EMISSIONS AND CLEAN ENERGY SOURCES: A NONPARAMETRIC GRANGER CAUSALITY-BASED EVIDENCE FROM ASEAN ECONOMIES
Authors: LIU XIAOXI , CHENG FANGZE , AHMAD ISHTIAQ , HAIDER JAHANZAIB , OMAR ABDULLAH BIN
Published: December 2022

This study uses a dataset from 1990 to 2020 to investigate the impact of renewable energy sources in preventing further environmental degradation in the Association of Southeast Asian Nations (ASEAN) economies. This research aims to investigate using renewable energy sources to lower CO2 emissions in the area. In this investigation, a unique method known as nonparametric Granger causality was employed by using the quantiles approach. The findings showed a strong causal influence of green energy on the returns of CO2 emission at high quantiles of conditional distribution in six ASEAN countries. In addition, the results demonstrated a substantial causal connection between the use of green energy and the erratic levels of CO2 emission across the board, except in Indonesia. Because of this, this study suggested that the administration and officials introduce private and public investment toward green energy to lessen the environmental collapse occurring in ASEAN countries.
Optical potential for the elastic scattering of 6Li projectile on 1p-shell nuclei
Authors: Xu Yongli , Su Xinwu , Han Yinlu , Sun Xiaojun , Zhang Donghong , Cai Chonghai
Published: December 2022

A set of global optical potential parameters describing the elastic scattering of 6Li projectile on 1p-shell nuclei is obtained by fitting the experimental data of elastic scattering angular distributions for 9Be, [Formula: see text]B, [Formula: see text]B, [Formula: see text]C, [Formula: see text]C, [Formula: see text]C, [Formula: see text]N, [Formula: see text]O and [Formula: see text]O targets with incident energies from 4.0,MeV to 210.0,MeV and with 6Li and 7Li targets from 2.0,MeV to 156.0,MeV. The comparison is performed between the results calculated using the derived optical potential and the existing experimental data. The calculations agree well with the experimental data in the whole energy region except from 20,MeV to 50,MeV at backward angles, where there is an upward trend for the experimental data of angular distributions. Besides, the inelastic scattering contributions of the first excited states are deduced by the present global optical potential. The results are also in good agreement with the experimental data. In order to improve the description of elastic scattering, the inelastic scattering contributions of the first excited states are considered in the calculations between 20 and 50,MeV. A satisfactory fit is obtained between them.
A Deep Learning-Based Method for Automatic Abnormal Data Detection: Case Study for Bridge Structural Health Monitoring
Authors: Ye Xijun , Wu Peirong , Liu Airong , Zhan Xiaoyu , Wang Zeyu , Zhao Yinghao
Published: December 2022

Ideally, the monitoring data collected by the Structural health monitoring (SHM) system should purely reflect the structure status. However, sensors deployed in the field can be very vulnerable to extreme conditions such as heavy rainfall, which results in large quantities of anomalous data and unavoidably leads to the inaccuracy of structural condition assessment and even false alarms. To automatically identify whether the collected data are abnormal or not, a novel deep learning-based data anomaly detection technique combining the time-frequency method and the Convolutional Neural Network (CNN) is proposed in this paper. First, the original time-series data of the SHM system were converted to the red green blue (RGB) images by using the wavelet scalograms. Subsequently, the GoogLeNet deep neural network is applied to construct a classification model by incorporating the generated 2D images. In order to evaluate the performance of the proposed technique, the SHM data (containing seven abnormal patterns) lasting for one month of a long-span cable-stayed bridge were utilized for experimental validation. The results indicate that compared with traditional deep neural network methods, the data anomaly identification accuracy can be improved by using the proposed technique. Different types of data anomaly patterns can be accurately identified, even in the case of small samples. The proposed technique exhibits good accuracy and can be integrated into advanced SHM systems with high fidelity and intelligence.
Distribution of Interspike Intervals of a Neuron with Inhibitory Autapse Stimulated with a Renewal Process
Authors: Shchur O. , Vidybida A.
Published: December 2022

In this paper, we study analytically the impact of an inhibitory autapse on neuronal activity. In order to do this, we formulate conditions on a set of non-adaptive spiking neuron models with delayed feedback inhibition, instead of considering a particular neuronal model. The neuron is stimulated with a stochastic point renewal process of excitatory impulses. Probability density function (PDF) [Formula: see text] of output interspike intervals (ISIs) of such a neuron is found exactly without any approximations made. It is expressed in terms of ISIs PDF for the input renewal stream and ISIs PDF for that same neuron without any feedback. Obtained results are applied to a subset of neuronal models with threshold 2 when the time intervals between input impulses are distributed according to the Erlang-2 distribution. In that case, we have found explicitly the model-independent initial part of ISIs PDF [Formula: see text] defined at some initial interval [Formula: see text] of ISI values.
FUZZY LOGIC-BASED MODELING OF A CENTRIFUGAL BLOOD PUMP PERFORMANCE VIA EXPERIMENTAL DATA OF NEWTONIAN AND NON-NEWTONIAN FLUIDS
Authors: ONDER AHMET , GUZEL MUHAMMED HUSEYIN , INCEBAY OMER , SEN MUHAMMED ARIF , YAPICI RAFET , KALYONCU METE
Published: December 2022

Using fuzzy logic methods, some complex experiments that are not possible due to critical limitations can be simulated in a short time. In this study, experimental data of Newtonian 40% aqueous glycerin solution (GS) and non-Newtonian 600[Formula: see text]ppm aqueous xanthan gum solution (XGS) working fluids were used to model the hydraulic performance of a centrifugal blood pump. A novel fuzzy logic-based model (FLM) for modeling the hydraulic performance of the pump model is proposed. In the proposed model, there are two inputs which are flow rate and impeller rotational speed and one output which is head pressure. In FLM, the range for flow rate is 1–7.8[Formula: see text]L/min in GS and 1–8[Formula: see text]L/min in XGS, and for head pressure 50–245[Formula: see text]mmHg in GS and 50–215[Formula: see text]mmHg in XGS. In addition, impeller rotational speed range is 2700–3600[Formula: see text]rpm for both fluids. The estimated results with FLM were validated with the experimental results and it was seen that the FLM was compatible with the experimental results with an accuracy of 96.25%. These results imply that the developed FLM is acceptable and can be used to assist in determining the performance of blood pumps.
Stationary distribution and probability density function of a stochastic waterborne pathogen model with logistic growth
Authors: Liu Yue , Wei Jize
Published: December 2022

Waterborne disease threatens public health globally. Previous studies mainly consider that the birth of pathogens in water sources arises solely by the shedding of infected individuals. However, for free-living pathogens, intrinsic growth without the presence of hosts in environment could be possible. In this paper, a stochastic waterborne disease model with a logistic growth of pathogens is investigated. We obtain the sufficient conditions for the extinction of disease and also the existence and uniqueness of an ergodic stationary distribution if the threshold [Formula: see text]. By solving the Fokker–Planck equation, an exact expression of probability density function near the quasi-endemic equilibrium is obtained. Results suggest that the intrinsic growth in bacteria population induces a large reproduction number to determine the disease dynamics. Finally, theoretical results are validated by numerical examples.
SkyWatch: A Passive Multistatic Radar Network for the Measurement of Object Position and Velocity
Authors: Randall Mitch , Delacroix Alex , Ezell Carson , Kelderman Ezra , Little Sarah , Loeb Abraham , Masson Eric , Watters Wesley Andrés , Cloete Richard , White Abigail
Published: December 2022

Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project’s suite of electromagnetic sensors and used to separate known aerial objects from those exhibiting anomalous kinematics. SkyWatch, a passive multistatic radar system based on commercial broadcast FM radio transmitters of opportunity, is a network of receivers spaced at geographical scales that enables estimation of the 3D position and velocity time series of objects at altitudes up to 80[Formula: see text]km, horizontal distances up to 150[Formula: see text]km, and at velocities to [Formula: see text][Formula: see text]km/s ([Formula: see text][Formula: see text]Mach). The receivers are designed to collect useful data in a variety of environments varying by terrain, transmitter power, relative transmitter distance, adjacent channel strength, etc. In some cases, the direct signal from the transmitter may be large enough to be used as the reference with which the echoes are correlated. In other cases, the direct signal may be weak or absent, in which case a reference is communicated to the receiver from another network node via the internet for echo correlation. Various techniques are discussed specific to the two modes of operation and a hybrid mode. Delay and Doppler data are sent via internet to a central server where triangulation is used to deduce time series of 3D positions and velocities. A multiple receiver (multistatic) radar experiment is undergoing Phase 1 testing, with several receivers placed at various distances around the Harvard–Smithsonian Center for Astrophysics (CfA), to validate full 3D position and velocity recovery. The experimental multistatic system intermittently records raw data for later processing to aid development. The results of the multistatic experiment will inform the design of a compact, economical receiver intended for deployment in a large-scale, mass-deployed mesh network. Such a network would greatly increase the probability of detecting and recording the movements of aerial objects with anomalous kinematics suggestive of Unidentified Aerial Phenomena (UAP).
An Exploratory Study on the Impact of the Construction Industry on Climate Change
Authors: Elhegazy Hosam , Zhang Jiansong , Amoudi Omar , Zaki Joliviana Nashaat , Yahia Mohamed , Eid Mohamed , Mahdi Ibrahim
Published: December 2022

In the last decade, the construction industry in Egypt has been booming, and many mega projects are under construction. The lack of awareness of the construction industry’s impact on climate change could be very harmful in terms of CO2 emission, water and soil pollution, etc. This paper aims to explore the critical factors in the Egyptian construction industry that contributes to climate change. Moreover, given the shared features between the construction industry in Egypt and those in other countries, this research could also shed light on the general impacts of the construction industry on the various aspects of climate change. So, to achieve this aim, an intensive literature review was carried out to identify various factors contributing to climate change within the construction industry. This is followed by conducting 11 interviews with construction experts to explore any further factors throughout the lifecycle of a construction project. The identified factors from the literature review and the interviews were used to design a questionnaire survey to collect construction professionals’ opinions on the impact of these factors on climate change in Egypt. 48 valid responses were received. The collected data were statistically analyzed to rank and determine the criticality level of these factors. The results revealed that the most significant factors influencing climate change are the impact of industrial construction on climate change, the use of primary renewable energy as raw materials during the construction and use phases, and the effect of heavy civil and highway construction on climate change. The results also show that managing these factors requires considerable awareness and proactive actions during the project life cycle and pre-construction stage. The findings could inform decision-makers and construction professionals to raise awareness and make informed decisions to handle these key factors and minimize their potential contribution to climate change. Therefore, it can be recommended that construction clients may involve a climate change management plan as a requirement of tender documents.
How to Avoid Using Too Much of a Good Thing
Authors: Yaffe Philip
Published: December 2022

Each "Communication Corner" essay is self-contained; however, they build on each other. For best results, before reading this essay and doing the exercise, go to the first essay "How an Ugly Duckling Became a Swan," then read each succeeding essay.

We live in a visual age, which has led many public speakers to believe every formal speech they give requires visual aids. This is too simplistic. Visual aids are supposed to aid the speech, not distract from it, i.e., they are useful when they are useful and not useful when they aren't.
Conco-ERNIE: Complex User Intent Detect Model for Smart Healthcare Cognitive Bot
Authors: Zhang Bolin , Tu Zhiying , Hang Shaoshi , Chu Dianhui , Xu Xiaofei
Published: December 2022

The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient’s utterance. To this end, we create a high-quality annotated
Chinese Medical query (utterance) dataset,

CMedQ

(about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model
Conco-ERNIE
, using
concept co-occurrence
patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on
CMedQ
demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot,
MedicalBot
. To provide knowledge support for
MedicalBot
, we also build a
Chinese medical graph,

CMedKG

(about 45k entities and 283k relationships).
Evaluating the Predictive Performance of Positive- Unlabelled Classifiers
Authors: Saunders Jack D. , Freitas Alex A.
Published: December 2022

Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been written on the subject of evaluating these methods. Many popular standard classification metrics cannot be precisely calculated due to the absence of fully labelled data, so alternative approaches must be taken. This short commentary paper critically reviews the main PU learning evaluation approaches and the choice of predictive accuracy measures in 51 articles proposing PU classifiers and provides practical recommendations for improvements in this area.
Open challenges for Machine Learning based Early Decision-Making research
Authors: Bondu Alexis , Achenchabe Youssef , Bifet Albert , Clerot Fabrice , Cornuejols Antoine , Gama Joao , Hebrail Georges , Lemaire Vincent , Marteau Pierre-Francois
Published: December 2022

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper.
Federated Graph Machine Learning
Authors: Fu Xingbo , Zhang Binchi , Dong Yushun , Chen Chen , Li Jundong
Published: December 2022

Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many realworld scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.