Stack Architecture Research Articles

Niobium-Tungsten Bimetallic Oxide Electrodes with High Dual-Band Electrochromic Performance Prepared by Hydrothermal Method

Dual-band electrochromic (EC) smart windows, capable of selectively regulating visible (VIS) and near-infrared (NIR) light, are one of the most attractive candidates for significantly reducing the energy consumption of buildings. Niobium-tungsten bimetallic oxides exhibit a fast ion exchange rate and high cycle stability in the electrochemical field because of various structural chemistry in the pseudobinary system Nb2O5/WO3, showing excellent dual-band EC potential. Herein, we employ a facile hydrothermal technique to synthesize Nb18W16O93 (NWO) films with low temperature and facile procedure. The obtained NWO films exhibit impressive dual-band electrochromic performances, including high optical regulation (radiation blocking rates of 33.1% and 77.5% in the VIS and NIR band) and short coloring/bleaching time (12 s/2.6 s at 633 nm and 6.5 s/6.1 s at 1600 nm). These results demonstrate that NWO is a high-performance dual-band EC material and provide a practical strategy to synthesize the NWO electrodes with nanorod stacking architectures in a facile and low-cost way.

Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models

ABSTRACTA challenging problem for post-disaster emergency services is how to quickly and precisely acquire building damage with limited time and computation resources. With the development of unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNN), using drone video and CNNs to determine the building damage has become one effective solution. In this research, we propose a stacked lightweight damage assessment model to overcome the current challenge in getting fast and precise post-disaster information based on aerial video datasets and artificial intelligence. Specifically, we developed an instance-level recognition building damage dataset based on aerial video. Then, we proposed a stacked lightweight ShuffleNet architecture that includes location and classification models to assess the building damage state. With regards to the location model, the lightweight network achieves a reduction in the model training time by approximately 37% and in the detection time by 44%, achieving similar location accuracy. As for the classification model, the trained model can achieve classification accuracy of approximately 83% for different damage levels by optimizing ShuffleNet. For post-disaster emergencies with limited time and computation resources, the proposed framework provides a valuable solution to better balance fast and precise building damage assessment.

Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm

The risk of fraudulent activity has significantly increased with the rise in digital payments. To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized by the dandelion algorithm (S-AEKELM-DA) to detect fraudulent transactions. The primary objective was to enhance the kernel extreme learning machine (KELM) performance by integrating the dandelion technique into a stacked autoencoder kernel ELM architecture. This study aimed to improve the overall effectiveness of the proposed method in fraud detection by optimizing the regularization parameter (c) and the kernel parameter (σ). To evaluate the S-AEKELM-DA approach; simulations and experiments were conducted using four credit card datasets. The results demonstrated remarkable performance, with our method achieving high accuracy, recall, precision, and F1-score in real time for detecting fraudulent transactions. These findings highlight the effectiveness and reliability of the suggested approach. By incorporating the dandelion algorithm into the S-AEKELM framework, this research advances fraud detection capabilities, thus ensuring the security of digital transactions.

Data-driven approach to predict the fundamental period of steel-braced RC frames using stacked generalization machine learning models

Data-driven approach to predict the fundamental period of steel-braced RC frames using stacked generalization machine learning models

Autogenic and allogenic controls on turbidite lobe stacking pattern and architecture: The case of the Turonian to Coniacian deep‐water turbidite lobe complexes (Dønna Terrace, offshore Mid‐Norway)

ABSTRACTThe mechanisms controlling the stacking patterns of deep‐water turbidite lobes are currently open to a wide range of interpretations. A study of Turonian to Coniacian turbidite lobe complexes in the greater Marulk area (Dønna Terrace, Norwegian Sea) was undertaken to examine the balance and respective influences of various controlling factors using a large sediment core, well‐log and seismic dataset. A four‐tiered lobe hierarchy is described based on a detailed sedimentological study of three Cretaceous turbidite lobe systems, involving a variety of sedimentary processes and flow regimes. The inferred depositional stacking patterns were then used to identify autogenic and allogenic forcings on the large‐scale depositional architecture of turbidite lobes. Autogenic processes (best observed in core data) control the self‐regulation of sediment dispersal and the broad evolution of lobe sub‐environments. Conversely, allogenic forcings (best observed in well‐log data) regulate axial migration within the turbidite lobe succession, and control sediment pulses and ultimately the general evolutionary trend of the turbidite lobe complex. Beyond this, an updated approach is proposed here aiming at deciphering autogenic‐dominant and allogenic‐dominant processes at different hierarchical levels in both confined and unconfined turbidite lobe deposits, which may help with assigning appropriate inputs for geomodels for an improved understanding of the internal and external controls on lobe depositional architecture. It is advocated that this approach may eventually be used in other depositional systems, such as in deltaic complexes from coastal settings, both in actual and ancient sediments.

Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.

A novel hybrid deep fuzzy model based on gradient descent algorithm with application to time series forecasting

Deep fuzzy systems are widely used in time series forecasting tasks due to their excellent nonlinear transformation capabilities and interpretability. However, traditional optimization methods and fixed network structures cannot make the deep fuzzy system obtain the expected prediction accuracy and generalization ability. Therefore, a novel hybrid deep fuzzy model (HDFM) is proposed in this paper. Firstly, two types fuzzy modules, namely the type-1 TSK fuzzy module (T1TSKFM) and the interval type-2 TSK fuzzy module (IT2TSKFM), are respectively presented and designed in detail. And, the gradient expressions of the fuzzy parameters, including the antecedent and consequent parameters, are also derived in detail. Secondly, in order to optimize the fuzzy parameters and to further accelerate the module convergence, a novel parameter optimization strategy is presented, combining the gradient descent with the Regularization, the DropRule and the AdaBound algorithms. Thirdly, a new stacked hybrid deep fuzzy architecture is proposed, which can be automatically trained and constructed using the designed T1TSKFM and the IT2TSKFM. Then, the detailed data-driven learning and updating strategy are given in step by step way. In addition, both the layered structure interpretability and the fuzzy rule interpretability are respectively analyzed. This can guarantee that the proposed model not only has the better forecasting accuracy, but also has the higher interpretability. Finally, in order to verify the effectiveness of the proposed method, several comparative experiments are given. Experimental results show that the forecasting performance of the proposed model outperforms the other comparisons, such as the DIRM-DFM, the IT2DIRM-DFM, the DCFS, and the ANFIS method. At the same time, the proposed model has better interpretability and more flexible construction property.

Real-time Card Fraud Detection: A Stacked Ensemble Machine Learning Approach

As the world witnesses, Card fraud detection has become more and more crucial observes a rise in new and innovative techniques used by offenders. Machine learning and artificial intelligence are at the forefront of the various fraud detection technologies that credit card firms and financial institutions are utilizing. The Nilson Report's startling statistics demonstrate the scope of the issue. Global losses due to credit and debit card fraud increased to $40.6 billion in 2022, up from $34.7 billion the year before, with the United States being responsible for a large 36.8% of these losses. Additionally, younger people, especially those between the ages of 20 and 29, were disproportionately affected by credit card fraud. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. Our study used a dataset with the transaction histories of over 300,000 people from the UCI Machine Learning Repository. We determined the best methods through a multi-step procedure that included data pretreatment, dataset division, and model selection. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. The Stacked Ensemble model, SN Algorithm (SVM + Naive Bayes), beat other models according to the results in terms of important metrics. Impressive AUC, CA, F1, accuracy, and recall ratings demonstrated the system's effectiveness in preventing card theft.

AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention.

Antimicrobial peptides (AMPs) are small molecular polypeptides that can be widely used in the prevention and treatment of microbial infections. Although many computational models have been proposed to help identify AMPs, a high-performance and interpretable model is still lacking. In this study, new benchmark data sets are collected and processed, and a stacking deep architecture named AMPpred-MFA is carefully designed to discover and identify AMPs. Multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (LSTM) network and a convolutional neural network (CNN). The effectiveness of AMPpred-MFA is verified through five independent tests conducted in batches. Experimental results show that AMPpred-MFA achieves a state-of-the-art performance. The visualization interpretability analyses and ablation experiments offer a further understanding of the model behavior and performance, validating the importance of our feature representation and stacking architecture, especially the multihead attention mechanism. Therefore, AMPpred-MFA can be considered a reliable and efficient approach to understanding and predicting AMPs.

Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul

Previous heat risk assessments have limitations in obtaining accurate heat hazard sources and capturing population distributions, which change over time. This study proposes a diurnal heat risk assessment framework incorporating spatiotemporal air temperature and real-time population data. Daytime and nighttime heat risk maps were generated using hazard, exposure, and vulnerability components in Seoul during the summer of 2018. The hazard was derived from the daily extreme air temperatures obtained using the stacking machine learning model. Exposure was calculated using de facto population density, and vulnerability was assessed using demographic and socioeconomic indicators. The resulting maps revealed distinct diurnal spatial patterns, with high-risk areas in the urban core during the day and dispersed at night. Daytime heat risk was strongly correlated with heat-related illness ratios (R= 0.8) and accurately captured temporal fluctuations in heat-related illness incidence. The proposed framework can guide site-specific adaptation and response plans for dynamic urban heat events.

Single and Hybrid-Ensemble Learning-Based Phishing Website Detection: Examining Impacts of Varied Nature Datasets and Informative Feature Selection Technique

To tackle issues associated with phishing website attacks, the study conducted rigorous experiments on RF, GB, and CATB classifiers. Since each classifier was an ensemble learner on their own; we integrated them into stacking and majority vote ensemble architectures to create hybrid-ensemble learning. Due to ensemble learning methods being known for their high computational time costs, the study applied the UFS technique to address these concerns and obtained promising results. Since the scalability and performance consistency of the phishing website detection system across numerous datasets is critical to combating various variants of phishing website attacks, we used three distinct phishing website datasets (DS-1, DS-2, and DS-3) to train and test each ensemble learning method to identify the best-performed one in terms of accuracy and model computational time. Our experimental findings reveal that the CATB classifier demonstrated scalable, consistent, and superior accuracy across three distinct datasets (attained 97.9% accuracy in DS-1, 97.36% accuracy in DS-2, and 98.59% accuracy in DS-3). When it comes to model computational time, the RF classifier was discovered to be the fastest when applied to all datasets, while the CATB classifier was discovered to be the second quickest when applied to all datasets.

A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data

Smart grid is envisaged as a power grid that is extremely reliable and flexible. The electrical grid has wide-area measuring devices like Phasor measurement units (PMUs) deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability. The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data. This research proposes a transmission line fault detection and classification (FD&C) system based on an auto-encoder neural network. A comparison between a Multi-Layer Extreme Learning Machine (ML-ELM) network model and a Stacked Auto-Encoder neural network (SAE) is made. Additionally, the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models. With substantially shorter testing time, the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79% accuracy. The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms. To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio (SNR) ranging from 10 dB to 40 dB. The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.

A 1-A Switching LDO With 40-mV Dropout Voltage and Fast DVS

This brief presents a 1-A fully-integrated switching LDO for digital loads. By using 4-phase 200-MHz pulse-width modulation (PWM), it can significantly reduce the output ripple and allow using a smaller output capacitor. Distinctive from the prior dual-loop structure, we adopt a single feedback loop with a wide bandwidth error amplifier (EA) using a quasi-Type-III compensation to improve the dynamic voltage scaling (DVS) rate and to reduce the transient recovery time. Meanwhile, compared with the prior stacked power transistor architecture, the single-PMOS with auxiliary constant current (ACC) control can reduce by 4-fold the power transistor size, and decreases the driver current by at least 2.5 times. Fabricated in 28-nm bulk CMOS, the proposed LDO measures a 1-A load capability with a 40-mV dropout voltage. Moreover, the measured load regulation is 2.2 mV/A, and the line regulation is 1.8 mV/V. In addition, the regulator obtains a fast DVS speed of 4V/μs and a fast load transient recovery time of <50ns.

Towards Critical Industrial Wireless Control: Prototype Implementation and Experimental Evaluation on URLLC

Ultra reliable low latency communication (URLLC) is a promising technology to enable critical industrial wireless control, such as tactile sensing and motion control. This article presents a novel software-defined URLLC prototype and establishes a human-in-loop robotic teleoperation platform for experimental evaluations. First, by studying the control and communication requirements of robotic teleoperation, the key challenges for URLLC-based industrial wireless control are analyzed. Then, the prototype development including protocol stack and hardware architecture design is introduced in detail, and extensive experiments are performed to evaluate the communication latency and control transparency for robotic teleoperation. Experimental results show that the prototype can realize millisecond-level latency with ultra-high reliability, and support wireless teleoperation with similar transparency as wired teleoperation. Finally, challenges and future research issues towards 6G are discussed.

Solid Oxide Cell Technology for Power Generation, Hydrogen Production and Energy Storage

The progress in maturation of solid oxide cell technology has led to development of new applications that would explode its presence in many areas, far beyond power generation. The solid oxide cell technology has the potential to have a formidable presence in production of hydrogen and, eventually, long duration storage of electric power.Following the successful operation of a 200 kW SOFC system under a project supported by Department Energy/NETL, FuelCell Energy (FCE) is pursing the development of SOC based plant configurations from subMW to electrolysis, and further, to energy storage.Multi-faceted evolution of the technology has been underway since the earlier demonstration of SOFC power plant. FCE has developed state-of-the-art lightweight Compact SOFC Architecture (CSA) stacks that are packaged in compact modules with adaptability for use in a variety of configurations and capacities. The CSA stacks can operate directly on a variety of fuels - natural gas, biogas, and hydrogen- without any modification. FCE’s existing fuel cell pilot manufacturing line for CSA cells and stacks includes robotics and automation such as cell screen printing, interconnect subassembly, seal application, QC, as well as stack assembly and conditioning. Via a design for manufacturing approach, as well as focus on minimization of raw material, recent detailed cost studies show a path to low factory stack production cost (<100/kW) at high volumes (1,000 MW/year). The large market existing for power generation equipment, in the range of 200-300kW, is a significant driver for development of high efficiency SOFC products that would easily cater to early-adopters prior to wide-spread acceptance. The accelerated interest in hydrogen as the fuel source will widen the market for SOFC deployment even more. Under a project supported by DOE, FCE is working on design of MW-class SOFC power plants as future extension of the subMW SOFC plant products.FCE is developing a first-of-a-kind 250kW Solid Oxide Electrolyzer Cell (SOEC) system with the hydrogen production capacity of 150kg/day. The overarching goal of the project is to verify that the integration of Solid Oxide Electrolysis Cell (SOEC) systems within nuclear plants will maximize the plants’ efficiency and flexibility and will increase their revenue by switching between electric power generation and hydrogen production. Hybrid nuclear-hydrogen production operations are expected to help the present and future nuclear plants diversify and increase profitability.The 250kW SOEC system is planned to be demonstrated and operated at Idaho National Laboratory (INL). The project will culminate in verification and validation testing and solidify SOEC technology as a low cost and efficient means for hydrogen production integrated within the nuclear power plant environment. The SOEC system will be interfaced with a High-Level front-end Controller (HLC) simulating communications from a nuclear plant and the electric grid. The HLC will determine an optimized hydrogen production schedule to meet all contractual obligations, while maximizing revenue from the integrated operations.FCE is also developing energy storage systems based on the Company’s Solid Oxide Fuel Cell (SOFC) technology. Reversible Solid Oxide Fuel Cell (RSOFC) technology is suitable for medium to long-duration energy storage achieving high round trip electric efficiencies near 70% (electricity-in to electricity-out) at an expected levelized cycle cost of ≤ $0.05 / kWh-cycle.FCE is currently conducting operational tests of an RSOFC prototype system to accomplish the of validation and verification of engineering/pilot-scale RSOFC technology in a relevant environment. A bread-board pilot demonstration system is being utilized to verify cell materials and stack design improvements as well as to validate power electronics and system control strategies that will be utilized for optimization of efficiency, transient response, and lifetime characteristics.

(Invited) Electrolyser Manufacturing Scale-up Versus Design Development for Meeting Green H2 Targets

Cummins-Hydrogenics pioneered our MW-scale PEM electrolysis cell stack architecture in 2012 and has built and operated multiple sites since, including the 20 MW Bécancour facility for Air Liquide from 2020. Lessons learned from these projects provide a springboard for large-scale manufacturing and deployment of PEM electrolysers worldwide and Cummins has announced GW-scale manufacturing deployment in Europe, N America and Asia. This is required to meet the growing global demand for PEM electrolysers, which derives from the targets and subsidies for green hydrogen production announced by many governments.Concurrent to this manufacturing scale-up, significant work on cost reduction and technology advancement is ongoing resulting in anticipated cost decreases from catalyst loading (Ir<150g/MW), efficiency improvements (<46 kWh/kg) and higher current density, all of which need to be achieved for commercial viability. Driving down the Ir-loading of PEM electrolysers while increasing current densities will remain the primary driver of CapEx reductions in the short to medium term but the deployment of large-scale manufacturing also allows for significant economies of scale. Recycling and regeneration of used components will also become a larger issue and new processes need to be implemented for each stack component.Moving from a project-based design methodology to design for volume manufacturing, all while maintaining the fast pace of technology development, makes for significant challenges and can lead to processing waste and inefficiencies. Rigorous testing and validation protocols are needed across the industry in order to successfully scale-up while implementing technology improvements necessary to meet TCO targets. This talk will focus on the juxtaposition of these challenges and ways to mitigate future risks.

Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue

To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a “multimodal spectroscopic scan” (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49–52) for IHD, 47 (IQR 45–50) for DCM and 36 (IQR 33–52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm−1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts.Table 1Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined “stack” (LR + SGD + SVM)AUCPrecisionRecallSpecificity(a) NIR model Logistic regression0.9800.9440.9330.967 SGD0.5500.2810.4000.700 SVM0.8400.8060.8000.900 Stack0.9330.7940.8000.900(b) Raman model Logistic regression0.9850.9400.9290.960 SGD0.8920.8690.8570.932 SVM0.9920.9400.9290.960 Stack0.9540.8690.8570.932(c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients Logistic regression0.9750.8410.8280.917 SGD0.8470.8030.7930.899 SVM0.9710.8530.8280.917 Stack0.9610.8530.8280.917(d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients Logistic regression0.9610.9690.9660.984 SGD0.9440.9670.9660.923 SVM1.0001.0001.0001.000 Stack1.0000.9440.9310.969Bold values indicate values obtained from the stack algorithm and used for analyses

A Stacking Machine Learning Method for IL-10-Induced Peptide Sequence Recognition Based on Unified Deep Representation Learning

Interleukin-10 (IL-10) has anti-inflammatory properties and is a crucial cytokine in regulating immunity. The identification of IL-10 through wet laboratory experiments is costly and time-intensive. Therefore, a new IL-10-induced peptide recognition method, IL10-Stack, was introduced in this research, which was based on unified deep representation learning and a stacking algorithm. Two approaches were employed to extract features from peptide sequences: Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). After feature fusion and optimized feature selection, we selected a 1900-dimensional UniRep feature vector and constructed the IL10-Stack model using stacking. IL10-Stack exhibited excellent performance in IL-10-induced peptide recognition (accuracy (ACC) = 0.910, Matthews correlation coefficient (MCC) = 0.820). Relative to the existing methods, IL-10Pred and ILeukin10Pred, the approach increased in ACC by 12.1% and 2.4%, respectively. The IL10-Stack method can identify IL-10-induced peptides, which aids in the development of immunosuppressive drugs.

Presence of bicontinuous microemulsion-type domains and dielectrically inert interfacial water layers in lamellar gel-stabilized oil-in-water emulsions

HypothesisLamellar gels are widely formulated in household and cosmetic products because of their eminent ability to improve long-term stability of thermodynamically unstable oil-in-water emulsions. However, despite long study, how and why membrane internal structure and membrane-membrane interactions are modified by the presence of polar and nonpolar oils remains elusive. ExperimentsUsing small- and wide-angle X-ray scattering, dielectric spectroscopy, and field-emission transmission electron microscope, we investigate intermembrane interactions and water-mediated microscopic interfacial properties in lamellar gels and lamellar gel-stabilized oil-in-water emulsions based on cetyltrimethylammonium chloride and 1-hexadecanol. FindingsReducing membrane surface charge density enhances undulation fluctuation disorder, resulting in a crossover of dominant interactions from electrostatic double-layer repulsion to Helfrich interaction. Oil-emulsification induces similar structural impacts to the reduced 1-hexadecanol ratio, confirming preferential dissolution of higher-alcohol in oil phases. An emerging Teubner-Stray scattering component upon emulsification of nonpolar oil evidences that oil droplets and lamellar gels are indirectly connected via bicontinuous microemulsion-type domains. Dielectric spectra reveal strikingly small water permittivity in the lamellar gel and emulsion samples, which is quantitatively explained by a cumulative effect of a dielectrically inert interfacial thin water layer (<1nm) and a highly polarizable bulk-like water layer. This phenomenon appears to be intrinsic to diverse lamellar stack architectures.

Pulse Field Magnetization (PFM) With Sectioned HTS Stacked Tape Structures for Electrical Machine

High temperature superconducting (HTS) stacks of superconducting tapes provide a solution for all-superconducting electrical machines since they could work as trapped-field magnets. Most experiments with these stacks use pieces of tapes with relatively small areas as wider tapes are more difficult to acquire, therefore in this paper we investigate substitutions for these wider tapes to be implemented in superconducting electrical machines. With different stack architectures and substrate materials made by SuperOx, AMSC and THEVA, the magnetizing effect using pulse field magnetization (PFM) would be different. To explore the differences of this effect, sectioned stacks are tested in a modified synchronous machine designed by ASuMED under liquid nitrogen conditions. By changing different sectioned stacks of tape and testing them under the same condition, the characteristics of the trapped flux, magnetization and demagnetization could be affected, different stacks are simulated with COMSOL finite element models using an electromagnetic-thermal coupled model, and different stacks are tested with different charging voltages.