Single Domain Model Research Articles

Factor Structure and Measurement Invariance Across Sex of the Sport Concussion Assessment Tool Symptom Inventory.

Describe the factor structure of the 22-symptom Sport Concussion Assessment Tool (SCAT), using confirmatory factor analysis (CFA) for a priori hypothesized symptom domains. Prospective observational study. Collegiate student-athletes with concussion. Symptoms were collected via the SCAT symptom checklist. We created symptom domains based on previous literature, guided by clinical expertise. To determine which symptom grouping best represent the data, we used CFA and compared a single-domain model to 3- and 6-domains. We examined fit statistics to assess relative and absolute model fit. Motivated by differences in the prevalence of some individual symptoms by sex in our study, we also examined model invariance by sex to determine if symptoms were being measured as part of the same underlying construct(s). Among 1160 concussions (male, n = 667; female, n = 493) between 2015 and 2020, all 3 symptom structures seemed to fit the data well, with 3- and 6-domains fitting better than 1-domain. The 6-domain structure fit the data best with the following domains: headache, vestibulo-ocular, sensory, cognitive, sleep, and affective. All 3 structures showed configural and metric invariance by sex. We demonstrate that the SCAT symptom structure is best represented through 6 specific factors; however, the 3-factor model also demonstrated good fit. Key differences between the 3- and 6-domain models may make 1 model more appropriate than the other depending on the research question being addressed. Symptom structures were configurally and metrically invariant by sex, meaning that symptom measures represent symptom domain factors in the same way across sex.

Multi-Dimensional Modelling of Bioinspired Flow Channels Based on Plant Leaves for PEM Electrolyser

The Polymer Electrolyte Membrane Water Electrolyser (PEMWE) has gained significant interest among various electrolysis methods due to its ability to produce highly purified, compressed hydrogen. The spatial configuration of bipolar plates and their flow channel patterns play a critical role in the efficiency and longevity of the PEM water electrolyser. Optimally designed flow channels ensure uniform pressure and velocity distribution across the stack, enabling high-pressure operation and facilitating high current densities. This study uses flow channel geometry inspired by authentic vine leaf patterns found in biomass, based on various plant leaves, including Soybean, Victoria Amazonica, Water Lily, Nelumbo Nucifera, Kiwi, and Acalypha Hispida leaves, as a novel channel pattern to design a PEM bipolar plate with a circular cross-section area of 13.85 cm2. The proposed bipolar design is further analysed with COMSOL Multiphysics to integrate the conservation of mass and momentum, molecular diffusion (Maxwell–Stefan), charge transfer equations, and other fabrication factors into a cohesive single-domain model. The simulation results showed that the novel designs have the most uniform velocity profile, lower pressure drop, superior pressure distribution, and heightened mixture homogeneity compared to the traditional serpentine models.

Exploring RoBERTa model for cross-domain suggestion detection in online reviews

Detecting suggestions in online review requires contextual understanding of review text, which is an important real-world application of natural language processing. Given the disparate text domains found in product reviews, a common strategy involves fine-tuning bidirectional encoder representations from transformers (BERT) models using reviews from various domains. However, there hasn't been an empirical examination of how BERT models behave across different domains in tasks related to detecting suggestion sentences from online reviews. In this study, we explore BERT models for suggestion classification that have been fine-tuned using single-domain and cross-domain Amazon review datasets. Our results indicate that while single-domain models achieved slightly better performance within their respective domains compared to cross-domain models, the latter outperformed single-domain models when evaluated on cross-domain data. This was also observed for single-domain data not used for fine-tuning the single-domain model and on average across all tests. Although fine-tuning single-domain models can lead to minor accuracy improvements, employing multi-domain models that perform well across domains can help in cold start problems and reduce annotation costs.

Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures

Considering the significance of the frequency domain information in price series, we propose a decomposition-integration hedging model from the perspective of time–frequency domain to improve the hedging effectiveness and robustness. First, the returns series of spot and futures are decomposed and reconstructed into high frequency, medium frequency, and low frequency sequences to incorporate the frequency information into the hedging model. Then, we construct a model pool to fit the various characteristics of the data in each frequency domain and further extract the corresponding information. Finally, by taking four moments of investors risk appetite into consideration, the multi-objective optimization is employed to integrate the final hedging ratio. The empirical results indicate that the decomposition-integration hedging model is superior to the single time domain models in comprehensive evaluation indices. In addition, within a certain range of market fluctuations, the structure of our model has some characteristics that are compatible with reality. To recapitulate briefly, the hedging ratio of our model is mainly determined by the high frequency and medium frequency information under drastic market fluctuations. While when the market volatility is small, the hedging ratio largely depends on low frequency information.

Compact modelling of a thermo-mechanical finite element model of a microelectronic package

Thermo-mechanical behaviour of microelectronic components, when subjected to thermal cycling, plays a vital role in reliability assessments. This motivates the use of accurate numerical simulation tools. However, numerical simulations are time-consuming and thus, model order reduction methodologies are required. Most literature so far, focuses on single-domain model reduction approaches, that is, the reduction of only thermal or structural domain models. This paper extends existing work to order reduction of coupled linearized thermo-mechanical finite element models. Three thermo-mechanical reduction approaches are studied within the framework of the Krylov-basis reduction technique. This enables the examination of the most efficient approach to reduce the one-way coupled problem. The reduced order model is verified through the comparison with the full order model. The results show that the reduced model significantly decreases the computational costs while providing highly accurate results for temperature and deformation. This paper also emphasizes the potential of model order reduction in enabling system-level simulation of microelectronic components.

OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs

Different knowledge graphs for the same domain are often uniquely housed on the Web. Effectively linking entities from different graphs is critical for building an open and comprehensive knowledge graph. However, linking entities across different sources has thus far faced various challenges, including the increasingly large-scale volume of the data, the heterogeneity of the graphs, and the ambiguity of real-world entities. To address them, we propose a unified framework LinKG. Specifically, we decouple the problem into different linking tasks based on the unique properties of each type of entity. To link word sequence based entities, we propose an LSTM-based method to capture word dependencies. To link entities of large scale, we utilize the hashing technique and convolutional neural networks for scalable and accurate linking. To link ambiguous entities, we propose heterogeneous graph attention networks to leverage heterogeneous structural information. Finally, to validate the design choices of different LinKG modules, we characterize the relationships between different tasks based on the single-domain and multi-domain transfer models. Extensive experiments demonstrate the effectiveness of LinKG with an overall F1-score of 95.15%, based on which we deploy and release the Open Academic Graph (OAG)—the largest publicly available heterogeneous academic graph to date.

Phase field study on the flexoelectric response of dielectric–ferroelectric multilayers

We report a theoretical modeling of the flexoelectric response of dielectric–ferroelectric (DE–FE) multilayers based on phase field simulations in the framework of the Landau–Ginzburg–Devonshire (LGD) theory. The correlation between negative capacitance and flexoelectric response is revealed, and the single-domain and multi-domain models are compared. It shows that the dielectric layers drive the ferroelectric layer into a negative capacitance regime, and the flexoelectric response of the multilayer is maximal when the negative capacitance of the ferroelectric layer has a minimal absolute value. Moreover, the flexoelectric response peak will be shifted to a lower temperature by increasing the thickness of dielectric layer, indicating a possibility of achieving a stronger flexoelectric response at room temperature compared with that of pure ferroelectric. However, while the single-domain model shows that the flexoelectric response peak is simply shifted to a lower temperature with near constant peak value and width, the multi-domain model reveals a significant suppressing of the flexoelectric peak by the dielectric layer. This is attributed to the formation of the vortex domain state, which eases the depolarization effect and leads to large absolute value of negative capacitance of the ferroelectric layer. Our work provides new insights into flexoelectricity in ferroelectric heterostructures.

Modelling preferential flow induced by dynamic changes of desiccation cracks: A comparative numerical study

Quantitative investigation on the preferential flow induced by desiccation cracks (PF-DC) remains a great challenge due to the soil shrinking-swelling behavior. This work presents a series of comparative numerical studies to investigate the accuracy and substitutability of different models in simulating the water flux, hydrological response and crack evolution induced by PF-DC. As a comparative study, an effective dynamic dual-permeability model (DDPM) we recently developed and validated was regarded as a benchmark model. Three numerical experiments were conducted to (i) compare the difference among the single-domain model (SDM), rigid dual-permeability model (RDPM) and DDPM; (ii) test the sensitivity of the DDPM to the shrinking-swelling parameters; (iii) test the rationality of a “lighter” dynamic DPM (LDPM) only considering the proportion changes of each domain while neglecting the variation of hydraulic properties. The results showed that compared to the DDPM, the SDM overestimated the water content under low-rainfall intensity while underestimating the water content under high-intensity rainfall and failed to capture the early increase of water content in deep soils induced by PF-DC. The RDPM greatly overestimated the total water content and water storage capacity of the crack domain, which was not suggested to be used in the surface runoff or flood forecast. The DDPM is overall not sensitive to the shrinking-swelling parameters, indicative of relatively loose accuracy requirements in measuring the soil shrinking-swelling parameters. The LDPM can be a tentative alternative option for the DDPM, but it is better not to use it to evaluate the surface runoff or use it under long-term extreme drought. In conclusion, the prediction errors without considering crack evolution and variation of hydraulic properties of each domain (RDPM) are the highest, then followed by the only considering crack evolution (LDPM) and uncertainties of shrinking-swelling parameters.

Decision curve analysis confirms higher clinical utility of multi-domain versus single-domain prediction models in patients with open abdomen treatment for peritonitis

BackgroundPrediction modelling increasingly becomes an important risk assessment tool in perioperative systems approaches, e.g. in complex patients with open abdomen treatment for peritonitis. In this population, combining predictors from multiple medical domains (i.e. demographical, physiological and surgical variables) outperforms the prediction capabilities of single-domain prediction models. However, the benefit of these prediction models for clinical decision-making remains to be investigated. We therefore examined the clinical utility of mortality prediction models in patients suffering from peritonitis with a decision curve analysis.MethodsIn this secondary analysis of a large dataset, a traditional logistic regression approach, three machine learning methods and a stacked ensemble were employed to examine the predictive capability of demographic, physiological and surgical variables in predicting mortality under open abdomen treatment for peritonitis. Calibration was examined with calibration belts and predictive performance was assessed with the area both under the receiver operating characteristic curve (AUROC) and under the precision recall curve (AUPRC) and with the Brier Score. Clinical utility of the prediction models was examined by means of a decision curve analysis (DCA) within a treatment threshold range of interest of 0–30%, where threshold probabilities are traditionally defined as the minimum probability of disease at which further intervention would be warranted.ResultsMachine learning methods supported available evidence of a higher prediction performance of a multi- versus single-domain prediction models. Interestingly, their prediction performance was similar to a logistic regression model. The DCA demonstrated that the overall net benefit is largest for a multi-domain prediction model and that this benefit is larger compared to the default “treat all” strategy only for treatment threshold probabilities above about 10%. Importantly, the net benefit for low threshold probabilities is dominated by physiological predictors: surgical and demographics predictors provide only secondary decision-analytic benefit.ConclusionsDCA provides a valuable tool to compare single-domain and multi-domain prediction models and demonstrates overall higher decision-analytic value of the latter. Importantly, DCA provides a means to clinically differentiate the risks associated with each of these domains in more depth than with traditional performance metrics and highlighted the importance of physiological predictors for conservative intervention strategies for low treatment thresholds. Further, machine learning methods did not add significant benefit either in prediction performance or decision-analytic utility compared to logistic regression in these data.

Abstract 5392: Universal immunohistochemistry positivity classification of cancer cells across multiple cancer types and antibodies using artificial intelligence

Abstract Background: Novel cancer therapeutic antibodies targeting tumor-specific antigens are in development. However, precise quantification of target protein expression on Tumor Cells (TC) from immunohistochemistry (IHC) is challenging. Here, we developed an artificial intelligence powered image analyzer, called Universal IHC (UIHC), which robustly detects and quantifies targets of interest expressed in multiple untrained cancer types and antibodies. Methods: UIHC model can detect TCs and classify IHC positivity across many antibody-cancer pairs, called “domains”. UIHC is trained on four domains: lung cancer (569K/107K) (No. of negative TC/positive TC), bladder cancer (270K/116K), and breast cancer stained with PD-L1 22C3 pharmDx (270K/96K), and breast cancer stained with HER2 4B5 (307K/256K); all annotated by board-certified pathologists. For comparison, we developed four AI models using only a single domain (SD). All models were evaluated on hold-out test sets divided in three groups: “in-domain” included the four training domains; “near-out-domain” contained PD-L1 SP142 and SP263 on lung cancer; “far-out-domain” contained 7 antibodies on 15 cancer types. Results: For cell detection, the harmonic means of the precision and recall (F1 score) averaged on TC- and TC+ (mF1), formatted as (UIHC; avg-SD [min, max]) were: “in-domain” (72.5%; 68.9% [67.1%-69.9%]), “near-out-domain” (64.8%; 61.6% [59.7%-65.2%]), and “far-out-domain” (70.3%; 62.5% [59.0%-65.9%]). At image level, cutoffs at 1% and 50% of TPS are applied for PD-L1 22C3 and SP263, thus 3-way accuracy is computed*. In far-out-domain, the mean TPS per antibody is used as a cutoff. UIHC outperforms SD on most antibodies (Table 1). Conclusion: We demonstrated that a UIHC, built by merging multiple domains during development, can be effective on untrained domains. UIHC can be applied to novel targets in future clinical research with minimal or no adaptation needed. Model performance of the proposed UIHC model and four SD models. *3-way acc 1%-50% TPS cutoff Hold-out test-set Cell detection performance(mF1) Tumor Proportion Score (TPS) classification performance(accuracy) Test set group Staining(antibodies) Cancer type Number of negative TC/positive TC UIHC model SD modelsAverage[min-max] UIHC model SD modelsAverage[min-max] In-domain test set PD-L1 22C3 Lung 37716/13792 76.7% 71.5%*[65.6%-75.9%] 80.8%* 77.3%*[75.7%-78.7%] Near-out-domaintest set PD-L1 SP263 Lung 524/485 64.1% 64.0%[57.6%-72.6%] 80.0%* 72.5%*[60.0%-80.0%] Far-out-domain test set TROP2 Pan-cancer 1243/3289 78.9% 70.7%[63.7%-78.4%] 94.0% 87.8%[87%-90%] MET 3493/1134 80.5% 77.7%[75.1%-80.4%] 96.0% 96.0%[92.0%-100.0%] Claudin18.2 2927/74 72.7% 63.4%[55.1%-73.3%] 94.0% 93.8%[81.3%-100.0%] DLL3 2973/3 68.9% 42.5%[40.3%-43.8%] 100% 92.4%[87.5%-94.0%] HER3 2768/138 60.9% 54.2%[44.0%-71.3%] 94.0% 93.9%[87.5%-88.0%] FGFR2 2358/438 50.8% 58.9%[52.8%-58.9%] 81.0% 87.9%[87.5%-88.0%] Ecadherin 443/912 79.2% 74.6%[72.1%-76.7%] 90.0% 82.5%[80.0%-90.0%] Citation Format: Biagio Brattoli, Mohammad Mostafavi, Sangjoon Choi, Taebum Lee, Seokhwi Kim, Wonkyung Jung, Soo Ick Cho, Jinhee Lee, Keunhyung Chung, Jeongun Ryu, Seonwook Park, Sergio Pereira, Seunghwan Shin, Chan-Young Ock. Universal immunohistochemistry positivity classification of cancer cells across multiple cancer types and antibodies using artificial intelligence. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5392.

Thermal stability of non-collinear antiferromagnetic Mn3Sn nanodot

D019-Mn3Sn, an antiferromagnet having a non-collinear spin structure in a kagome lattice, has attracted great attention owing to various intriguing properties such as large anomalous Hall effect. Stability of a magnetic state against thermal fluctuation, characterized in general by the thermal stability factor Δ, has been well studied in ferromagnetic systems but not for antiferromagnets. Here, we study Δ of the antiferromagnetic Mn3Sn nanodots as a function of their diameter D. To quantify Δ, we measure the switching probability as a function of the pulse-field amplitude and analyze the results based on a model taking account of two and sixfold magnetic anisotropies in the kagome plane. We observe no significant change in Δ down to D = 300 nm below which it decreases with D. The obtained D dependence is well explained by a single-domain and nucleation-mediated reversal models. These findings provide a basis to understand the thermal fluctuation and reversal mechanism of antiferromagnets for device applications.

Characterisation of community-dwelling older adults with poor appetite

PurposeA poor appetite affects up to 27% of community-dwelling older adults in Europe and is an early predictor of malnutrition. Little is known about the factors associated with poor appetite. The present study, therefore, aims to characterise older adults with poor appetite.MethodsAs part of the European JPI project APPETITE, data from 850 participants, aged ≥ 70 years of the Longitudinal Ageing Study Amsterdam (LASA) from 2015/16 were analysed. Appetite during the last week was assessed with a five-point scale and dichotomised into "normal" and "poor". Binary logistic regression was used to examine associations between 25 characteristics from 5 domains—physiological, emotional, cognitive, social, and lifestyle—and appetite. First, domain-specific models were calculated using stepwise backward selection. Second, all variables contributing to poor appetite were combined in a multi-domain model.ResultsThe prevalence of self-reported poor appetite was 15.6%. Fourteen parameters from all five single-domain models contributed to poor appetite and were entered into the multi-domain model. Here, female sex (total prevalence: 56.1%, odds ratio: 1.95 [95% confidence interval 1.10–3.44]), self-reported chewing problems (2.4%, 5.69 [1.88–17.20]), any unintended weight loss in the last 6 months (6.7%, 3.07 [1.36–6.94]), polypharmacy defined as ≥ 5 medications in the past 2 weeks (38.4%, 1.87 [1.04–3.39]), and depressive symptoms (Centre for Epidemiologic Studies Depression Scale without appetite item) (1.12 [1.04–1.21]) were associated with an increased likelihood of having poor appetite.ConclusionAccording to this analysis, older people with the characteristics described above are more likely to have a poor appetite.

Measurement of the activation volume in magnetic random access memory

Measuring thermal stability in magnetic random access memory devices is non-trivial. Recently, there has been much discussion on the appropriate model to use: single domain or domain wall nucleation. Of particular challenge is assessing the maximum size at which the single domain model can be assumed. Typically, this is estimated to be in the range of 20–30 nm based on a value of the exchange stiffness (Aex) that is assumed, estimated using indirect measurements or derived from significantly thicker films. In this work, it is proposed that this maximum size can be measured directly via the “activation volume” (Vact) or the “activation diameter” (Dact), which originates from the concept of magnetic viscosity. This is conducted by measuring, using the time dependence of magnetization at different applied fields, Dact in perpendicular magnetic tunnel junction pillars of varying effective anisotropy constant (Keff) and diameter. It is shown that the trend in Dact follows 1/Keff dependence, in good agreement with the analytic model for the critical diameter of coherent switching. Critically, it is also found that the smallest size for which a single domain, with coherent reversal, occurs is 20 nm. Thus, in devices with technologically relevant values of Keff, the macrospin model may only be used in 20 nm, or smaller, devices.

Effects of dynamic changes of desiccation cracks on preferential flow: experimental investigation and numerical modeling

Abstract. Preferential flow induced by desiccation cracks (PF-DC) has been proven to be an important hydrological effect that could cause various geotechnical engineering and ecological environment problems. Investigation on the PF-DC remains a great challenge due to the soil shrinking–swelling behavior. This work presents an experimental and numerical study of the PF-DC considering the dynamic changes of desiccation cracks. A soil column test was conducted under wetting–drying cycles to investigate the dynamic changes of desiccation cracks and their hydrological response. The ratios between the crack area and soil matrix area (crack ratio), crack aperture and depth were measured. The soil water content, matrix suction and water drainage were monitored. A new dynamic dual-permeability preferential flow model (DPMDy) was developed, which includes physically consistent functions in describing the variation of both porosity and hydraulic conductivity in crack and matrix domains. Its performance was compared to the single-domain model (SDM) and rigid dual-permeability model (DPM) with fixed crack ratio and hydraulic conductivity. The experimental results showed that the maximum crack ratio and aperture decreased when the evaporation intensity was excessively raised. The self-closure phenomenon of cracks and increased surficial water content was observed during low-evaporation periods. The simulation results showed that the matrix evaporation modeled by the DPMDy is lower than that of the SDM and DPM, but its crack evaporation is the highest. Compared to the DPM, the DPMDy simulated a faster pressure head building-up process in the crack domain and higher water exchange rates from the crack to the matrix domain during rainfall. Using a fixed crack ratio in the DPM, whether it is the maximum or the average value from the experiment data, will overestimate the infiltration fluxes of PF-DC but underestimate its contribution to the matrix domain. In conclusion, the DPMDy better described the underlying physics involving crack evolution and hydrological response with respect to the SDM and DPM. Further improvement of the DPMDy should focus on the hysteresis effect of the soil water retention curve and soil deformation during wetting–drying cycles.

Field-like torque-induced tunable zero-field spin-torque nano-oscillator

The spin-torque nano-oscillator (STNO), which is a novel type of nano-sized microwave oscillator driven by direct current, is considered as a promising candidate for future radio frequency (RF) transceivers owing to its scalability, nanoscale size and high frequency tunability. However, the potential application of STNO is limited because its stable oscillation requires an external magnetic field. In this work, the influences of the field-like torque and applied current intensity on the stable oscillation of STNO with a perpendicularly magnetized free layer are studied theoretically based on the macrospin model (also known as the single-spin or single-domain model) and the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation in the absence of magnetic field. It is demonstrated numerically that a stable oscillation of STNO can be observed when the ratio between the field-like torque and the spin torque is a negative value and larger than a certain value that depends on the damping coefficient and the current intensity, whose physical mechanism can be understood by the energy balance equation. Moreover, the frequency of stable oscillation of STNO can be modulated by the ratio between the field-like torque and the spin torque and also by the current intensity. Particularly, the larger the absolute value of the ratio between the field-like torque and the spin torque and the smaller the applied current intensity (above the critical current intensity), the more conducive it is to suppressing the formation of second and third oscillation frequencies, thereby enhancing the STNO’s “single-frequency” feature. Our findings provide a theoretical scheme for realizing a frequency tunable zero-field STNO, which may be useful for designing future RF transceivers.

Deception Detection Within and Across Domains: Identifying and Understanding the Performance Gap

NLP approaches to automatic deception detection have gained popularity over the past few years, especially with the proliferation of fake reviews and fake news online. However, most previous studies of deception detection have focused on single domains. We currently lack information about how these single-domain models of deception may or may not generalize to new domains. In this work, we conduct empirical studies of cross-domain deception detection in five domains to understand how current models perform when evaluated on new deception domains. Our experimental results reveal a large gap between within and across domain classification performance. Motivated by these findings, we propose methods to understand the differences in performances across domains. We formulate five distance metrics that quantify the distance between pairs of deception domains. We experimentally demonstrate that the distance between a pair of domains negatively correlates with the cross-domain accuracies of the domains. We thoroughly analyze the differences in the domains and the impact of fine-tuning BERT based models by visualization of the sentence embeddings. Finally, we utilize the distance metrics to recommend the optimal source domain for any given target domain. This work highlights the need to develop robust learning algorithms for cross-domain deception detection that generalize and adapt to new domains and contributes toward that goal.

Inclusion of slow infiltration in determining the influence time of saline sediments on reservoir water

The influence time of polluted sediments on water quality, as well as the influence degree, plays a key role in decision-making for sediment pollution control. However, factors that determine influence time, especially slow infiltration through sediments, are poorly known. Further, there is a lack of corresponding indexes and efficient models to accurately identify the critical moment in determining the influence time from the dynamic process of interaction between downward advection (infiltration) and upward diffusion at the sediment-water interface. Therefore, this article focuses on methods for determining the influence time of saline sediments on reservoir water salinisation. An alternative expression of Péclet number (Pe=uCD∂C/∂xx=0) is proposed to characterise the transient relative role of downward advection versus upward diffusion on the transport of non-reactive solutes; Pe = 1 can be used as an exact criterion for identifying the critical moment when saline sediments no longer affect the overlying water. We then developed a single-domain model that does not require an alternate run the two subdomains in solving processes, enabling to flexibly simulate sediment-water interactions, both effectively and efficiently. By combining this model with the alternative Pe, the influence time of saline sediments on reservoir water can be accurately determined. We used the planned Muguandao Coastal Reservoir, Qingdao, China, as an example to explore the effects of infiltration on the influence time. The influence time shortens with infiltration rates in a power function relationship and decreases from 970 years to 19.3 years in the rate range of (1–7) ×10−2 m/year. Furthermore, based on the simulation and sensitivity analysis, we suggest a simple formula (t=5.25D/u2) to directly predict the influence time of saline sediments on water salinisation for shallow mixed coastal reservoirs. The importance of understanding slow infiltration in determining the influence time is discussed.

Reconciling Asgardarchaeota Phylogenetic Proximity to Eukaryotes and Planctomycetes Cellular Features in the Evolution of Life.

The relationship between the three domains of life—Archaea, Bacteria, and Eukarya—is one of Biology’s greatest mysteries. Current favored models imply two ancestral domains, Bacteria and Archaea, with eukaryotes originating within Archaea. This type of models has been supported by the recent description of the Asgardarchaeota, the closest prokaryotic relatives of eukaryotes. However, there are many problems associated with any scenarios implying that eukaryotes originated from within the Archaea, including genome mosaicism, phylogenies, the cellular organization of the Archaea, and their ancestral character. By contrast, all models of eukaryogenesis fail to consider two relevant discoveries: the detection of membrane coat proteins, and of phagocytosis-related processes in Planctomycetes, which are among the bacteria with the most developed endomembrane system.Consideration of these often overlooked features and others found in Planctomycetes and related bacteria suggest an evolutionary model based on a single ancestral domain. In this model, the proximity of Asgard and eukaryotes is not rejected but instead, Asgard are considered as diverging away from a common ancestor instead of on the way toward the eukaryotic ancestor. This model based on a single ancestral domain solves most of the ambiguities associated with the ones based on two ancestral domains. The single-domain model is better suited to explain the origin and evolution of all three domains of life, blurring the distinctions between them. Support for this model as well as the opportunities that it presents not only for reinterpreting previous results, but also for planning future experiments, are explored.

COMSOL simulation of microwave plasma polishing on different surfaces

The characteristics of the surface and substrate have a significant role in the life and efficiency of the optical device. COMSOL® Multiphysics is a software program, which is applied to model the radio frequency (RF)-excited dielectric barrier for helium (processing gas) and oxygen (reactive gas). The software is flexible enough to accommodate multiple partial differential equations (PDEs) in a single domain model. This Multiphysics software is focused on finite element modelling program. The composition of the gas, the pressure, the configuration of the electrodes, and the excitation power of the RF are analyzed with regard to the formation of oxygen radicals and their consistency of allocation in the chamber. A computer program has been established to plan and optimize the microwave plasma physics utilized for the polishing of the surface of an optical specimen. The COMSOL® Multiphysics software is used for the simulation of microwave plasma coupled with an electromagnetic wave. COMSOL can easily simulate multiphysics problems. In the current study, a new method of producing Helium and Oxygen by microwave plasma is researched, evaluated, and simulated. The results show the distribution of density, temperature, and potential of electrons for two different specimens in the plasma chamber.

Multi-Domain Aspect Extraction Using Bidirectional Encoder Representations From Transformers

Deep learning and neural language models have obtained state-of-the-art results in aspects extraction tasks, in which the objective is to automatically extract characteristics of products and services that are the target of consumer opinion. However, these methods require a large amount of labeled data to achieve such results. Since data labeling is a costly task, there are no labeled data available for all domains. In this paper, we propose an approach for aspect extraction in a multi-domain transfer learning scenario, thereby leveraging labeled data from different source domains to extract aspects of a new unlabeled target domain. Our approach, called MDAE-BERT (Multi-Domain Aspect Extraction using Bidirectional Encoder Representations from Transformers), explores neural language models to deal with two major challenges in multi-domain learning: (1) inconsistency of aspects from target and source domains and (2) context-based semantic distance between ambiguous aspects. We evaluated our MDAE-BERT considering two perspectives (1) the aspect extraction performance using F1-Macro and Accuracy measures; and (2) by comparing the multi-domain aspect extraction models and single-domain models for aspect extraction. In the first perspective, our method outperforms the LSTM-based approach. In the second perspective, our approach proved to be a competitive alternative compared to the single-domain model trained in a specific domain, even in the absence of labeled data from the target domain.