Search Results (90)

Silicon (Si) and silicon carbide (SiC) are second- and third-generation semiconductor materials with excellent properties that are particularly suitable for applications in scenarios such as high temperature, high voltage, and high frequency. Si/SiC wafers face warpage and bending problems during production, which can seriously affect subsequent processing. Fast, accurate, and comprehensive detection of thickness, thickness variation, and flatness (including bow and warpage) of SiC and Si wafers is an industry-recognized challenge. Frequency scanning interferometry (FSI) can synchronize the upper and lower surfaces and thickness information of transparent parallel thin wafers, but it is still affected by multiple interfacial harmonic reflections, reflectivity asymmetry, and phase modulation uncertainty when measuring SiC thin wafers, which leads to thickness calculation errors and face reconstruction deviations. To this end, this paper proposes a high-precision facet reconstruction method for SiC/Si structures, which combines harmonic spectral domain decomposition, refractive index gradient constraints, and partitioning optimization strategy, and introduces interferometric signal “oversampling” and weighted fusion of multiple sets of data to effectively suppress higher-order harmonic interferences, and to enhance the accuracy of phase resolution. The multi-layer iterative optimization model further enhances the measurement accuracy and robustness of the system. The flatness measurement system constructed based on this method can realize the simultaneous acquisition of three-dimensional top and bottom surfaces on 6-inch Si/SiC wafers, and accurately reconstruct the key parameters, such as flatness, warpage, and thickness variation (TTV). A comparison with the Corning Tropel FlatMaster commercial system shows that this method has high consistency and good applicability. Full article

In image processing, the acquisition step plays a fundamental role because it determines image quality. The present paper focuses on the issue of blur and suggests ways of assessing contrast. The logic of this work consists in evaluating the sharpness of an image by means of objective measures based on mathematical, physical, and optical justifications in connection with the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. The sharpness of an image is usually assessed near objects’ boundaries, which encourages the use of gradients, with some major drawbacks. Within the LIP framework, it is possible to overcome such problems using a “contour detector” tool based on the notion of Logarithmic Additive Contrast (LAC). Considering a sequence of images increasingly blurred, we show that the use of LAC enables images to be re-classified in accordance with their defocus level, demonstrating the relevance of the method. The proposed algorithm has been shown to outperform five conventional methods for assessing image sharpness. Moreover, it is the only method that is insensitive to brightness variations. Finally, various application examples are presented, like automatic autofocus control or the comparison of two blur removal algorithms applied to the same image, which particularly concerns the field of Super Resolution (SR) algorithms. Such algorithms multiply (×2, ×3, ×4) the resolution of an image using powerful tools (deep learning, neural networks) while correcting the potential defects (blur, noise) that could be generated by the resolution extension itself. We conclude with the prospects for this work, which should be part of a broader approach to estimating image quality, including sharpness and perceived contrast. Full article

Quickly designing digital circuits that are both correct and efficient poses significant challenges. Electronics, especially those incorporating sequential logic circuits, are complex to design and test. While Electronic Design Automation (EDA) tools aid designers, they do not fully automate the creation of synthesisable circuits that can be directly translated into hardware. This paper introduces a system that employs Grammatical Evolution (GE) to automatically generate synthesisable Hardware Description Language (HDL) code for the Finite State Machine (FSM) of a Multi-Sequence Detector (MSD). This MSD differs significantly from prior work as it can detect multiple sequences in contrast to the single-sequence detectors discussed in existing literature. Sequence Detectors (SDs) are essential in circuits that detect sequences of specific events to produce timely alerts. The proposed MSD applies to a real-time vending machine scenario, enabling customer selections upon successful payment. However, this technique can evolve any MSD, such as a traffic light control system or a robot navigation system. We examine two parent selection techniques, Tournament Selection (TS) and Lexicase Selection (LS), demonstrating that LS performs better than TS, although both techniques successfully produce synthesisable hardware solutions. Both hand-crafted “Gold” and evolved circuits are synthesised using Generic Process Design Kit (GPDK) technologies at 45 nm, 90 nm, and 180 nm scales, demonstrating their efficacy. Full article

Flywheel energy storage systems (FESSs) can reach much higher speeds with the development of technology. This is possible with the development of composite materials. In this context, a study is being carried out to increase the performance of the FESS, which is especially used in leading fields, such as electric power grids, the military, aviation, space and automotive. In this study, a flywheel design and analysis with a hybrid (multi-layered) rotor structure are carried out for situations, where the cost and weight are desired to be kept low despite high-speed requirements. The performance values of solid steel, solid titanium, and solid carbon composite flywheels are compared with flywheels made of different thicknesses of carbon composite on steel and different thicknesses of carbon composite materials on titanium. This study reveals that wrapping carbon composite material around metal in varying thicknesses led to an increase of approximately 10–46% in the maximum rotational velocity of the flywheel. Consequently, despite a 33–42% reduction in system mass and constant system volume, the stored energy was enhanced by 10–23%. It was determined that the energy density of the carbon-layered FESS increased by 100% for the steel core and by 65% for the titanium core. Full article

Background/objectives: Kawasaki disease is the leading cause of acquired heart disease in children within developed countries. Although treatment with intravenous immunoglobulin (IVIG) significantly reduces the incidence of coronary artery aneurysm (CAA), the risk of it persists, affecting long-term patient outcomes. While intensified primary treatment is recommended for patients at high risk of IVIG resistance or CAA development, a universally accepted predictive model for such resistance remains unestablished. This study aims to develop a machine learning model to predict the occurrence of CAAs prior to initiating IVIG therapy. Methods: Data from two nationwide epidemiological surveys conducted between 2012 and 2017 were analyzed, encompassing 17,189 patients with calculable coronary artery z-scores and Harada scores. Various supervised machine learning algorithms were applied to develop a model for predicting CAA. Afterward, unsupervised learning techniques were employed to explore the data’s inherent structure. Results: The Harada score’s receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.558. The highest AUC among the machine learning models was 0.661, achieved by the Light Gradient Boosting Machine. However, this model’s sensitivity was 0.615, and specificity was 0.647, indicating limited clinical applicability. Unsupervised learning revealed no distinct distribution patterns between patients with/without CAAs. Conclusions: Despite utilizing a large dataset to develop a machine learning-based prediction model for CAAs, the performance was unsatisfactory. Future studies should focus on enhancing predictive models by incorporating additional clinical data, such as acute-phase coronary artery diameter measurements, to improve accuracy and clinical utility. Full article

Machine learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality-enabled self-labeling method has been proposed to advance adaptive ML applications in cyber–physical systems, especially manufacturing, by automatically adapting and personalizing ML models after deployment to counter data distribution shifts. The unique features of the self-labeling method require a novel software system to support dynamism at various levels. This paper proposes the AdaptIoT system, comprising an end-to-end data streaming pipeline, ML service integration, and an automated self-labeling service. The self-labeling service consists of causal knowledge bases and automated full-cycle self-labeling workflows to adapt multiple ML models simultaneously. AdaptIoT employs a containerized microservice architecture to deliver a scalable and portable solution for small and medium-sized manufacturers. A field demonstration of a self-labeling adaptive ML application is conducted with a makerspace and shows reliable performance with comparable accuracy at 98.3%. Full article

Tocols are commonly known as vitamin E, which comprise tocopherols and tocotrienols. Although vegetable oils are natural sources of tocols, deodorizer distillates (DDs) are attractive feedstock due to their potential abundance from oil refining processes and economic price. Deep eutectic solvents (DESs) are a family of neoteric solvents that show promising performance for tocols extraction. Besides their characters occupying the green chemistry concept, this review presents the current research on the potential performances of DESs in extracting tocols selectively and efficiently from DDs. The application of DESs in tocols extraction is presented considering three different ways: mono-phasic, in situ DESs formation, and bi-phasic systems. The basic principles of intermolecular interactions (H-bond, van der Walls bond, and misfit interaction) between DESs or their components with tocols are discussed to understand the mechanism by which DESs selectively extract tocols from the mixture. This is mainly observed to be a function of the intrinsic properties of DESs and/or tocols, which could be beneficial for tuning the appropriate DESs for extracting tocols selectively and effectively under mild operation conditions. This review is expected to provide insight in the potential application of DESs in the extracting of natural compounds with a phenolic structure and also briefly discusses the toxicity of DESs. Full article

Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series’ short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by $12.5\%$. To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model’s robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models. Full article

Arbuscular mycorrhizal (AM) symbiosis, a mutually beneficial interaction between plant roots and AM fungi, plays a key role in plant growth, nutrient acquisition, and stress tolerance, which make it a major focus for sustainable agricultural strategies. This intricate association involves extensive transcriptional reprogramming in host plant cells during the formation of arbuscules, which are specialized fungal structures for nutrient exchange. The symbiosis is initiated by molecular signaling pathways triggered by fungal chitooligosaccharides and strigolactones released by plant roots, which act as chemoattractants and signaling molecules to promote fungal spore germination, colonization, and arbuscule development. Calcium spiking, mediated by LysM domain receptor kinases, serves as a critical second messenger in coordinating fungal infection and intracellular accommodation. GRAS transcription factors are key components that regulate the transcriptional networks necessary for arbuscule development and maintenance, while small RNAs (sRNAs) from both plant and fungi, contribute to modifications in gene expression, including potential bidirectional sRNA exchange to modulate symbiosis. Understanding the molecular mechanisms related to AM symbiosis may provide valuable insights for implementation of strategies related to enhancing plant productivity and resilience. Full article

(1) Background: To make recommendations on the most effective therapy options for Ductal Carcinoma of the Breast (DCIS) patients; (2) Methods: MEDLINE, EMBASE, Cochrane Library, PROSPERO databases, and main relevant guideline websites were searched. Draft versions of the guideline went through formal internal and external reviews, with a final approval by the Program in Evidence Based Care and the DCIS Expert Panel. The Grading of Recommendations, Assessment, Development, and Evaluation approach was followed; (3) Results: Based on the current evidence from the systematic review and this guideline authors’ clinical opinions, initial draft recommendations were developed to improve the management of patients with DCIS. After a comprehensive internal and external review process, ten recommendations and 27 qualifying statements were eventually made. This guideline includes recommendations for the primary treatment of DCIS with surgical treatment and/or radiation therapy and the management of DCIS after primary treatment for patients with DCIS, including DCIS with microinvasion (<1 mm through the duct); (4) Conclusions: The current guideline was created after a systematic review and a comprehensive internal and external review process. We believe this guideline provides valuable insights that will be useful in clinical decision making for health providers. Full article

Zygophyllum propinquum (Decne.) is a leaf succulent C₄ perennial found in arid saline areas of southern Pakistan and neighboring countries, where it is utilized as herbal medicine. This study investigated how growth, water relations, ion content, chlorophyll fluorescence, and antioxidant system of Z. propinquum change as salinity levels increase (0, 150, 300, 600, and 900 mM NaCl). Salinity increments inhibited total plant fresh weight, whereas dry weight remained constant at moderate salinity and decreased at high salinity. Leaf area, succulence, and relative water content decreased as salinity increased. Similarly, the sap osmotic potential of both roots and shoots declined as NaCl concentrations increased. Except for a transitory increase in roots at 300 mM NaCl, sodium concentrations in roots and shoots increased constitutively to more than five times higher under saline conditions than in non-saline controls. Root potassium increased briefly at 300 mM NaCl but did not respond to NaCl treatments in the leaf. Photosynthetic pigments increased with 300 and 600 mM NaCl compared to non-saline treatments, although carotenoids appeared unaffected by NaCl treatments. Except for very high NaCl concentration (900 mM), salinity showed no significant effect on the maximum efficiency of photosystem II photochemistry (Fv/Fm). Light response curves demonstrated reduced absolute (ETR^*) and maximum electron transport rates (ETR_max) for the 600 and 900 mM NaCl treatments. The alpha (α), which indicates the maximum yield of photosynthesis, decreased with increasing NaCl concentrations, reaching its lowest at 900 mM NaCl. Non-photochemical quenching (NPQ) values were significantly higher under 150 and 300 mM NaCl treatments than under non-saline and higher NaCl treatments. Electrolyte leakage, malondialdehyde (MDA), and hydrogen peroxide (H₂O₂) peaked only at 900 mM NaCl. Superoxide dismutase and glutathione reductase activities and glutathione content in both roots and shoots increased progressively with increasing salinity. Hence, growth reduction under low to moderate (150–600 mM NaCl) salinity appeared to be an induced response, while high (900 mM NaCl) salinity was injurious. Full article

Remote sensing is a powerful technique for classifying and quantifying objects. However, the elaborate classification of objects in coastal waters with complex structures is still challenging due to the high possibility of class mixing. The classification through the hyperspectral images can be a reasonable alternative to problems related to such precise classification work because it has high spectral resolution over a wide bandwidth. This study introduced the results of the case study using a novel method to classify green algae on an artificial structure based on hyperspectral data and deep-learning models. The spectral characteristics of the attached green algae on the artificial structure were observed using a ground-based hyperspectral camera. The observed image had a total of three classes (concrete, dense green algae, and sparse green algae). A certain area of the image was used as learning data to create classification models for three classes. The classification models were created from one machine-learning (support vector machine, SVM) and two deep-learning models (convolutional neural network, CNN; and dense convolutional network, DenseNet). As a result, the performance for the classification results of green algae predicted from two deep-learning models was higher than that of the machine-learning model. Additionally, the deep-learning model successfully classified the interface area between concrete and green algae. This study suggests that the combination of hyperspectral data and deep learning could enable more precise classification of objects in coastal areas. Full article

Bloodstream infections (BSIs) and sepsis are a major cause of morbidity and mortality. Appropriate early antibiotic therapy is crucial for improving the survival of patients with sepsis and septic shock. T2 magnetic resonance (T2MR^®) technology may enable fast and sensitive detection of ESKAPEc pathogens directly from whole-blood samples. We aimed to evaluate concordance between the T2Bacteria^® Panel and standard blood culture and its impact on antibiotic therapy decisions. We conducted a single-centre retrospective study on patients with sepsis-induced hypotension or septic shock admitted to general, post-operative/neurosurgical, and cardiothoracic Intensive Care Units who were tested with the T2Bacteria^® Panel from January 2021 to December 2022. Eighty-five consecutively admitted patients were included, for a total of 85 paired tests. A total of 48 ESKAPEc pathogens were identified by the T2Bacteria^® Panel. The concordance rate between the T2Bacteria^® Panel and blood cultures was 81% (69/85), with 20 concordant-positive and 49 concordant-negative cases. For the 25 microorganisms grown from accompanying blood cultures, blood pathogen coverage by the T2Bacteria^® Panel was 88%. In this cohort of severely ill septic patients, the T2Bacteria^® Panel was highly concordant and was able to detect more ESKAPEc pathogens, with a significantly shorter turn-around time compared to conventional blood cultures. The T2Bacteria^® Panel also significantly impacted decisions on antibiotic therapy. Full article

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications. Full article

To enhance the dynamic performance and disturbance rejection capability of the permanent magnet synchronous motor speed control system, a novel speed control method based on a novel sliding mode control (NSMC) and load torque observer is proposed on the basis of model predictive current control (MPCC) with a sliding mode disturbance observer. First, on the basis of MPCC, the influence of parameters such as resistance, inductance, and flux linkage on MPCC is analyzed. To address the aggregated disturbance caused by parameter mismatches, a piecewise square-root switching function sliding mode disturbance observer (SMDO) is designed to enhance the robustness of the parameters. To address the poor dynamic performance and inadequate robustness resulting from the proportional-integral-controller (PI) velocity loop control in the MPCC, a novel NSMC velocity control method is proposed. This method utilizes the hyperbolic sine function and fractional-order integral sliding mode surface, resolving the dilemma faced by traditional slide mode controllers (SMC) in balancing fast response and reduced vibration. Additionally, to enhance the system’s disturbance rejection capability, a sliding mode torque observer (SMTO) is designed to continuously update the observed load torque value into the NSMC controller, achieving speed compensation control. Finally, through comparative experiments among the proportional integral controller (PI), SMC, NSMC, and NSMC + SMTO, the results indicate that the proposed NSMC + SMTO exhibits the best speed response, steady-state characteristics, and disturbance rejection capability. Full article