Reliable Performance Research Articles

Analysis of the Principle and Applications for High Voltage Device Based on SiC

The field of high-voltage electronics is rapidly evolving, with a particular focus on materials and devices that can withstand high voltages and temperatures while maintaining reliable performance. Silicon Carbide (SiC) is one such material that has been gaining significant attention due to its unique physical and chemical properties. This study will delve into the principle and applications of high-voltage devices based on SiC, exploring its potential for use in a range of power electronic systems. This paper will first explore the principle of SiC high-voltage devices, including a detailed analysis of the physical and chemical processes involved in their operation. Subsequently, this study will discuss the current state-of-the-art in SiC device fabrication and characterize the resulting devices based on their performance metrics. This will be followed by a discussion on the potential applications of SiC high-voltage devices in power electronics and beyond. Finally, this study will address the current limitations in SiC device research and development and explore possible future research directions that may lead to improved performance and reliability of SiC devices. By delving into the principle and applications of high-voltage devices based on SiC, this paper aims to provide a comprehensive understanding of the material’s unique properties and its potential for use in a range of power electronic systems. The analysis of current limitations and prospects will aid in identifying areas for future research and development efforts, leading to the advancement of SiC high-voltage devices for even more demanding applications.

Physics-Informed Neural Networks for the Condition Monitoring of Rotating Shafts.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis and the performance is compared to that of traditional optimization algorithms used for parameters estimation.

Finite element analysis of the mechanical performance of self-expanding endovascular stents made with new nickel-free superelastic β-titanium alloys

New Ni-free superelastic β-titanium alloys from the Ti–Zr–Nb–Sn system have been designed in this study to replace the NiTi alloy currently used for self-expanding endovascular stents. The simulation results, carried out by finite element analysis (FEA) on two β-type Ti–Zr–Nb–Sn alloys using a commonly used superelastic constitutive model, were in good agreement with the experimental uniaxial tension data. An ad-hoc self-expanding coronary stent was specifically designed for the present study. To assess the mechanical performance of the endovascular stents, a FEA framework of the stent deployed in the arterial system was established, and a simply cyclic bending loading was proposed. Six comparative simulations of three superelastic materials (including NiTi for comparison) and two arterial configurations were successfully conducted. The mechanical behaviours of the stents were analysed through stress localization, the increase in artery diameter, contact results, and distributions of mean and alternating strain. The simulation results show that the Ti–22Zr–11Nb–2Sn (at. %) alloy composition for the stent produces the largest contact area (9.92 mm2) and radial contact force (49.5 mN) on the inner surface of the plaque and a higher increase in the stenotic artery diameter (70 %) after three vascular bending cycles. Furthermore, the Ti–22Zr–11Nb–2Sn stent exhibited sufficient crimping capacity and reliable mechanical performance during deployment and cyclic bending, which could make it a suitable choice for self-expanding coronary stents. In this work, the implementation of finite element analysis has thus made it possible to propose a solid basis for the mechanical evaluation of these stents fabricated in new Ni-free superelastic β-Ti alloys.

Running-in behavior of dual-mobility cup during the gait cycle: A finite element analysis.

The running-in process is considered an essential aspect of the comprehensive wear process. The phenomenon of running-in occurs during the initial stages of wear in the prosthetic hip joint. Within the field of tribology, the running-in phenomenon of the hip joint pertains to the mechanism by which the contact surfaces of the artificial hip joint components are adjusted and a suitable lubricating film is formed. During the process of hip joint running-in, there is an interaction between the metal surface of the ball and the joint cup, which results in adjustments being made until a steady state is achieved. The achievement of desirable wear existence and reliable performance of artificial hip joint components are reliant upon the tribological running-in of the hip joint. Despite the establishment of current modeling approaches, there remains a significant lack of understanding concerning running-in wear, particularly the metal-on-polyethylene (MoP) articulations in dual-mobility cups (DMC). An essential aspect to consider is the running-in phase of the dual mobility component. The present study employed finite element analysis to investigate the running-in behavior of dual mobility cups, wherein femoral head components were matched with polyethylene liners of varying thicknesses. The analysis of the running-in phase was conducted during the normal gait cycle. The results of this investigation may be utilized to design a dual-mobility prosthetic hip joint that exhibits minimal running-in wear.

Prediksi Daya Listrik Pada Pembangkit Listrik Siklus Gabungan Berdasarkan Kondisi Lingkungan Menggunakan Metode Machine Learning

The utilization of machine learning methods in energy simulation enables the optimization of energy use and improves energy efficiency. In this research, the modeling of predicting power output was conducted under full load conditions in a Combined Cycle Power Plant (CCPP) based on the surrounding environmental conditions. Historical data of CCPP operation were used to model and predict power output under various environmental conditions. In this study, four machine learning algorithms, namely Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), were compared and evaluated for their performance. The evaluation metrics used to measure the model performance were Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Squared. The research results indicate that the Random Forest (RF) model achieved the best performance compared to other models with MAE of 2.314, RMSE of 3.372, and R-squared of 0.961. Additionally, the RF model also performed the best compared to other models in external testing with new data, where RF obtained values of MAE 2.579, RMSE 3.315, and R-squared 0.957. These results are consistent with the previous testing, indicating that RF has stable and reliable performance in predicting larger and more diverse datasets. This research contributes to understanding the potential application of machine learning in the power generation industry, especially in CCPP.

Thermo-hydraulic performance investigation of double pass solar air heater integrated with PCM-based thermal energy storage

The solar air heater (SAH) is one of the ways to utilize solar energy for the purpose of drying, greenhouse heating and air conditioning. However, the SAHs are unable to deliver reliable thermo-hydraulic performance as a result of the intermittent nature of solar energy and the low convective heat transfer coefficient. Thus, in the present study, a three-dimensional numerical investigation is performed to study the thermo-hydraulic performance of the double pass solar air heater (DPSAH) having semi-cylindrical PCM tubes and perforated baffles as roughness geometry. The parameters selected for this study include baffle height ratio (eb/H) from 0.4 and 0.8, tube radius ratio (rt/H) from 0.2 and 0.5, and Reynolds numbers (Re) from 3000 and 13,000. Experimental validation has been conducted to verify the numerical results, and it has been found to be well-validated with mean absolute errors of 6.5 % and 1.2 % for the Nusselt number (Nur) and friction factor (fr), respectively. The maximum Nusselt number (Nur) is found to be 200.08 at Re of 13,000, while the maximum friction factor (fr) is measured to be 0.6290 at Re of 3000, which corresponds to eb/H of 0.8 and rt/H of 0.3. The maximum heat transfer enhancement (Nur/Nus) in comparison to the smooth duct is found to be 5.08 times with a maximum friction factor increment (fr/fs) of 65.80 times, which corresponds to eb/H of 0.8, Re of 13,000 and rt/H of 0.3. The maximum thermo-hydraulic performance parameter (THPP) is observed to be 1.83 at Re of 13,000, rt/H of 0.2 and eb/H of 0.4. This study contributes to the design and optimisation of SAHs by incorporating thermal energy storage and roughness geometries.

Prediction and reliability analysis of reservoir lithology spatial distribution

Reliable lithology spatial distribution directly reflects the geological situation of the reservoir, which is the basis of stratigraphic correlation, sedimentary modeling, and other geological research. Under the condition of limited reservoir data, it is a challenging task to accurately depict the lithology spatial distribution and provide a quantitative reliability analysis of the results. In this study, we propose a flexible spatial distribution prediction and model reliability analysis method. Firstly, the method develops a spatially dependent deep Kriging technology to fit the heterogeneous characteristics of the reservoir lithology, and adopts the extracted spatial key information and related reservoir attributes to invert lithology spatial distribution intelligently. Then, it focuses on the real-time assimilation of non-Gaussian data in the reliability modeling and quantitatively analyzes the reliability of the prediction system under the non-Gaussian hypothesis. Finally, the method is applied to the actual heterogeneous reservoir, good results are achieved in the prediction accuracy, model fitting degree, model reliability, and time performance compared with other methods. The method is conducive to finding future mineral deposits locations and reducing exploration costs.

A multi-species pest recognition and counting method based on a density map in the greenhouse

Whiteflies and fruit flies are common pests that adversely affect greenhouse crops, so it is vital to control their numbers promptly. Current research involves setting up artificial monitoring devices with fixed yellow sticky traps (YSTs), for pest surveillance. However, these devices can only be installed in specific environments and additional interventions may impact the actual distribution of greenhouse pests. On the other hand, detection methods, which are the most commonly used, struggle to accurately detect overlapping or closely interacting small insects, which is a common occurrence in the presence of pests. In this paper, we propose a novel solution for the multi-species pest recognition and counting method based on a density map to tackle pest counting in the greenhouse. To reduce interference between different types of objects, we used two annotation maps to represent the distribution of each object separately. Then, two counting branches are designed to count the pests, respectively. Finally, the detected pests are mapped onto the original image to display their spatial positions. In addition, an adaptive sample equalization method is proposed to address the issue of class imbalance in images, and the atrous spatial pyramid pooling (ASPP) structure and the CBAM attention mechanism are added to the fruit fly branches to focus on learning fruit fly features from crowded edges of YSTs. When applied to split images, our method achieved reliable counting performance in counting both whiteflies and fruit flies, with R2 scores of 0.973 and 0.972, respectively. This method has been embedded into mobile devices to assist greenhouse workers in monitoring pest populations in the field. The experiments show that the proposed method provides a promising direction for counting multiple small target insects and monitoring pest populations in crowded images. At the same time, it demonstrates the potential of density map methods in various small target detection scenarios.

Innovative approach for predicting biogas production from large-scale anaerobic digester using long-short term memory (LSTM) coupled with genetic algorithm (GA)

An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD5, COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models.

Donepezil and Quercetin Simultaneous Estimation in Rat Plasma Using Developed Bioanalytical HPLC Method: Relevance in Pharmacokinetic Studies

The objective of this investigation was to create and apply a reliable reverse phase high performance liquid chromatography (RP-HPLC) technique for the concurrent measurement of donepezil (DPZ) and quercetin (QT) in rat blood samples for pharmacokinetic research. This is the first publication that introduces a method for DPZ and QT simultaneous determination in rat plasma by high-performance liquid chromatography (HPLC). Using a mobile phase of methanol and HPLC grade water (pH 2.8; adjusted with 0.05% v/v orthophosphoric acid) 45:55 v/v with 2-3 drops of triethylamine (TEA) in an isocratic elution mode at flow rate of 1.0ml/min, DPZ and QT were successfully separated chromatographically on a Hypersil gold C-18 column (250 mm × 4.6 mm, 5μm). The retention time for DPZ and QT was observed to be 6.3 and 12.3 minutes and was detected at an isobestic wavelength of 273 nm using a UV detector. The method was shown to be precise (%RSD < 2%), accurate (96–100%), and specific for the simultaneous detection of DPZ and QT. Several freeze-thaw cycles of the treated plasma samples did not significantly affect the analyte’s stability. The method’s applicability was subsequently confirmed through an oral pharmacokinetic investigation in rats. Since the results were deemed trustworthy, the validated RP-HPLC method may be used to simultaneously detect and quantify both drugs. The method worked well for evaluating the pharmacokinetic characteristics in wistar rats following a single oral dose of 5 mg/kg of QT and 10 mg/kg of DPZ. It is well acknowledged that the chromatographic process is straightforward, robust, accurate, exact, and repeatable.

RDCP: A Real Time Sea Fog Intensity and Visibility Estimation Algorithm

A number of accidents at sea are primarily caused by low visibility due to sea fog. Therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. Visibility meters utilize optical sensors rather than visional information, so that the measured visibility data occasionally includes errors. Moreover, visibility meters have significant costs so that it is not viable to install them at various locations. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior), which provides reliable estimation at a low cost by processing images captured from cameras, since they receive identical information as human eyes. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in Republic of Korea are used. Considering facilities in oceans, which are usually operated in power-limited environment, the processing time performance of PDCP is also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

Impact of wildfire smoke on ozone concentrations using a Generalized Additive model in Salt Lake City, Utah, USA, 2006–2022

ABSTRACT We investigated the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O3) at four sites in Salt Lake City (SLC), Utah for May to September for 2006–2022. Smoke days, which were identified by a combination of overhead satellite smoke detection and surface PM2.5 data and accounted for approximately 9% of the total number of days, exhibited O3 levels 6.8 to 8.9 ppb higher than no-smoke days and were predominantly characterized by high daily maximum temperatures and low relative humidity. A Generalized Additive Model (GAM) was developed to quantify the impact of wildfire contributions to O3. The GAM, which provides smooth functions that make the interpretation of relationships more intuitive, employed 17 predictors and demonstrated reliable performance in various evaluation metrics. The mean of the residuals for all sites was approximately zero for the training and cross-validation data and 5.1 ppb for smoke days. We developed three approaches to estimate the contribution of smoke to O3 from the model residuals. These generate a minimum and maximum contribution for each smoke day. The average of the minimum and maximum wildfire contributions to O3 for the SLC sites was 5.1 and 8.5 ppb, respectively. Between 2006 and 2022, an increasing trend in the wildfire contributions to O3 was observed in SLC. Moreover, trends of the fourth-highest MDA8 O3 before and after removing the wildfire contributions to O3 at the SLC Hawthorne site in 2006–2022 were quite different. Whereas the unadjusted data do not meet the current O3 standard, after removing the contributions from wildfires the SLC region is close to achieving levels that are consistent with meeting the O3 standard. We also found that the wildfire contribution during smoke days was particularly high under conditions of high temperature, high PM2.5 concentration, and low cloud fraction. Implications: In this study, we quantified the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O3) in Salt Lake City, Utah, using a Generalized Additive Model (GAM). The GAM results demonstrate the importance of wildfires as contributors to O3 air pollution. Our results suggest that states could use the GAM approach to assist in quantifying the wildfire contribution to MDA8 O3 under the U.S. EPA exceptional events rule. These findings also highlight the need for strategies to manage wildfires and their subsequent impacts on air quality in an era of climate warming.

Rapid preparation of dynamic-crosslinked nanocomposite hydrogel sensors with efficiency self-healing and adhesion properties for elderly health and sleep management

Multifunctional conductive hydrogels exhibit tremendous potential for applications in human–machine interfaces, flexible electronic devices and soft robotics. However, the time-consuming and energy-intensive preparation process, as well as the inefficient and unstable self-healing capabilities, limit their practicality and reusability. Herein, we utilized lignin sulfonate (LS)-Fe3+ dynamic redox system to induce the generation of ammonium persulfate (APS) radicals at room temperature. Additionally, the polymerization of acrylamide (AM), sodium acrylate (AAS), and 3-acrylamidophenylboronic acid (APBA) formed polymer networks through multiple dynamic crosslinking via the synergistic interactions of Fe3+ and cellulose nanocrystals (CNC). The dynamic and reversible boronic ester bonds, ion coordination bonds, and hydrogen bonds endowed the hydrogel with high stretchability (1170 %), low hysteresis and efficient self-healing (91.76 %, 2 h) capability. Interestingly, the intermediate catechol groups generated by the LS-Fe3+ dynamic catalytic system provided the hydrogel with repeatable and reliable adhesive performance. Thanks to the excellent ionically conductivity, the fabricated hydrogel-based sensors exhibited a wide sensing range (500 %), rapid response time (139 ms), and high sensitivity (GF = 8.98), that accomplished specific voice recognition and subtle body motion detection for real-time elderly health and sleep management. This multifunctional hydrogel is of great significance for efficiently developing prolonged lifespan wearable electronics and healthcare flexible devices.

High load, long cycle and flexible zinc-ion hybrid supercapacitors

Zinc-ion hybrid supercapacitor (ZIHS) combines the advantages of both batteries and capacitors to achieve high operating voltage, high energy and high power density.It is considered as a novel sustainable electrochemical energy storage device, but the problems of poor cycling stability and low energy density in most ZIHS still need to be solved. In this work, MnO2@ carbon cloth cathode with high load of 17.2 mg cm−2 was fabricated by in-situ deposition and hydrothermal treatment, and the active carbon extracted from banana peel was used as the anode. The assembled flexible ZIHS device exhibits a remarkable combination of high energy density and high specific capacity, attributed to the synergistic effect achieved by employing two distinct morphologies of MnO2 composites. Notably, the energy density of 674 μWh cm−2 can be maintained at a power density of 999 μW cm−2 In addition, the aqueous ZIHS retention rate was 94.56 % in 80,000 cycles. Therefore, the research results are of great significance to develop a safe, reliable and high electrochemical performance storage device.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein − ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4. Communicated by Ramaswamy H. Sarma

Error-Aware, Layer-Resolved Characterisation of Lithium-Ion Batteries Based on Ultrasonic Resonance and Parametric Analysis

With the prevalence of electrification in transportation and grid storage sectors, lithium-ion batteries (LIBs) are becoming one of the most fundamental energy storage elements and undergoing a profound trend of performance upscaling. The upscaled batteries demand next-level characterisation methodologies, and particularly appealing is the capability to resolve inner structural and morphological information physically, so as to identify the internal state and evolutions precisely and cost-effectively. In recent years, ultrasonic measurements have been proven as promising candidates for probing and inspecting LIBs. However, such approaches can only measure peripheral geometric features at a cell-level resolution and their characterisation performances tend to be subject to empirical configurations and sensitive to measurement conditions. To address these issues, this work reports an ultrasonic resonance-based method, featuring reliable characterisation performance, to realise layer-level robust inversion of the layered structure within LIBs. Firstly, a generalised theoretical model is introduced to capture the interaction characteristics between the ultrasound and the multilayer structure inside LIBs. By incorporating the captured characteristics into the analytic-signal procedure, a systematical framework is established to retrieve the stacking order, individual thickness, and local geometry information in a layer-resolved manner1, 2. Then, the relationship between the measurement parameters and the characterisation performance is analytically investigated. By considering both the temporal and spatial metrics, the performance evaluation indices are determined, with the critical measurement parameters recognised according to their physical significance. After that, the impacts of measurement parameters on the characterisation performance of LIBs are thoroughly studied by carrying out parametric analysis on the response signal simulated by the ultrasonic resonance model. Guided by such a relationship, an optimal strategy for parameter selection is developed to ascertain reliable characterisation, with its level of errors being predictable. Finally, experimental studies are carried out on different batteries with the ground truth obtained by the micro X-ray computed tomography, and the effectiveness and reliability of the proposed characterisation method are carefully validated. The proposed method demonstrates reliable performance in characterising layered structure within LIBs and could pave the way for developing performance-informed monitoring and prediction algorithms for state of health and state of safety estimations of LIBs. References M. Huang, N. Kirkaldy, Y. Zhao, Y. Patel, F. Cegla, and B. Lan, Journal of Energy Storage, 50 104585 (2022).R. A. Smith, L. J. Nelson, M. J. Mienczakowski, and P. D. Wilcox, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 65 (2), 231-243 (2018).

Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules

Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive ‘battery health map’ can be accurately identified using only rapid DC measurements.

Flexible Bioinspired Healable Antibacterial Electronics for Intelligent Human-Machine Interaction Sensing.

Flexible electronic sensors are receiving numerous research interests for their potential in electronic skins (e-skins), wearable human-machine interfacing, and smart diagnostic healthcare sensing. However, the preparation of multifunctional flexible electronics with high sensitivity, broad sensing range, fast response, efficient healability, and reliable antibacterial capability is still a substantial challenge. Herein, bioinspired by the highly sensitive human skin microstructure (protective epidermis/spinous sensing structure/nerve conduction network), a skin bionic multifunctional electronics is prepared by face-to-face assembly of a newly prepared healable, recyclable, and antibacterial polyurethane elastomer matrix with conductive MXene nanosheets-coated microdome array after ingenious templating method as protective epidermis layer/sensing layer, and an interdigitated electrode as signal transmission layer. The polyurethane elastomer matrix functionalized with triple dynamic bonds (reversible hydrogen bonds, oxime carbamate bonds, and copper (II) ion coordination bonds) is newly prepared, demonstrating excellent healability with highly healing efficiency, robust recyclability, and reliable antibacterial capability, as well as good biocompatibility. Benefiting from the superior mechanical performance of the polyurethane elastomer matrix and the unique skin bionic microstructure of the sensor, the as-assembled flexible electronics exhibit admirable sensing performances featuring ultrahigh sensitivity (up to 1573.05kPa-1 ), broad sensing range (up to 325kPa), good reproducibility, the fast response time (≈4ms), and low detection limit (≈0.98Pa) in diagnostic human healthcare monitoring, excellent healability, and reliable antibacterial performance.

Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images

Early and accurate detection of dysplasia in colorectal polyps can improve prognosis and increase survival chances. Recently, automated learning-based approaches using histopathological images have been adopted for improved classification of polyps. The supervised learning approaches do not provide a reliable classification performance due to limited annotated samples. But, in unsupervised learning, some hidden features are extracted from the unlabeled data which may not be effective in discriminating the complex patterns of the dataset. A generative adversarial network (GAN) is proposed in this work based on a semi-supervised framework for colorectal polyp classification using histopathological images. Our framework learns the discriminating features in an adversarial manner from the limited labeled and huge unlabeled data. In the supervised mode, the discriminator of the proposed model is trained to classify the real histopathological images, whereas, in the unsupervised mode, it tries to discriminate between real and fake images, similar to the classical GAN network. By training in unsupervised mode, the discriminator can identify and extract the subtle features from unlabeled images, to develop a generalized robust model. Our technique yielded classification accuracies of 87.50% and 76.25% using 25% and 50% majority voting schemes, respectively, on the UniToPatho dataset.

Geochemical modelling of heavy metal deposition in a geothermal heat exchanger

Heavy metal deposition and corrosion of carbon and low alloy steels exposed to geothermal two-phase fluids and brine solutions under acidic fluid conditions have been widely reported. Similarly, near neutral conditions can result in the precipitation of arsenic and antimony scales that can limit heat exchange. Geochemistry modelling has been undertaken to explain observed deposition and scaling in a two-phase geothermal fluid heat exchanger with a view to predicting the risk of heavy metal deposition and corrosion and heavy metal scaling in geothermal process fluids for new and existing energy facilities. Understanding of the geochemical parameters that control deposition, scaling and corrosion will allow the prediction of the risk of heavy metal deposition and scaling at the design stage for geothermal energy plants and assist in the selection of materials and processes that will give reliable long-term performance. The developed models have application in other industries for prediction of risk of scaling and corrosion and for process development support for example for the gettering of heavy metals from contaminated liquids and soils.