Search Results (1,400,066)

In this paper, we demonstrate a comprehensive study of NF₃-based selective etching processes for inner spacer formation and for channel release, enabling stacked horizontal gate-all-around Si nanosheet transistor architectures. A cyclic etching process consisting of an oxidation treatment step and an etching step is proposed and used for SiGe selective etching. The cyclic etching process exhibits a slower etching rate and higher etching selectivity compared to the direct etching process. The cycle etching process consisting of Recipe 1, which has a SiGe etching rate of 0.98 nm/cycle, is used for the cavity etch. The process achieved good interlayer uniformity of cavity depth (cavity depth ≤ 5 ± 0.3 nm), while also obtaining a near-ideal rectangular SiGe etch front shape (inner spacer shape = 0.84) and little Si loss (0.44 nm@ each side). The cycle etching process consisting of Recipe 4 with extremely high etching selectivity is used for channel release. The process realizes the channel release of nanosheets with a multi-width from 30 nm to 80 nm with little Si loss. In addition, a selective isotropic etching process using NF₃/O₂/Ar gas mixture is used to etch back the SiN film. The impact of the O₂/NF₃ ratio on the etching selectivity of SiN to Si and the surface roughness of SiN after etching is investigated. With the introduction of O₂ into NF₃/Ar discharge, the selectivity increases sharply, but when the ratio of O₂/NF₃ is up to 1.0, the selectivity tends to a constant value and the surface roughness of SiN increases rapidly. The optimal parameter is O₂/NF₃ = 0.5, resulting in a selectivity of 5.4 and a roughness of 0.19 nm. Full article

Micro-sized particles of synthetic polymers (microplastics) are found in all parts of marine ecosystems. This fact requires intensive study of the degree of danger of such particles to the life activity of hydrobionts and needs additional research. It is evident that hydrobionts in the marine environment are exposed to microplastics modified by biotic and abiotic degradation. To assess the toxic potential of aging microplastic, comparative studies were conducted on the response of cytochemical and genotoxic markers in hemocytes of the mussel Mytilus trossulus (Gould, 1850) after exposure to pristine and photodegraded (UV irradiation) polystyrene microparticles (µPS). The results of cytochemical tests showed that UV-irradiated µPS strongly reduced metabolism and destabilized lysosome membranes compared to pristine µPS. Using a Comet assay, it was shown that the nuclear DNA of mussel hemocytes showed high sensitivity to exposure to both types of plastics. However, the level of DNA damage was significantly higher in mussels exposed to aging µPS. It is suggested that the mechanism of increased toxicity of photo-oxidized µPS is based on free-radical reactions induced by the UV irradiation of polymers. The risks of toxic effects will be determined by the level of physicochemical degradation of the polymer, which can significantly affect the mechanisms of toxicity. Full article

The accuracy requirements for short-term power load forecasting have been increasing due to the rapid development of the electric power industry. Nevertheless, the short-term load exhibits both elasticity and instability characteristics, posing challenges for accurate load forecasting. Meanwhile, the traditional prediction model suffers from the issues of inadequate precision and inefficient training. In this work, a proposed model called IWOA-CNN-BIGRU-CBAM is introduced. To solve the problem of the Squeeze-and-Excitation (SE) attention mechanism’s inability to collect information in the spatial dimension effectively, the Convolutional Block Attention Module (CBAM) is firstly introduced as a replacement. This change aims to enhance the ability to capture location attributes. Subsequently, we propose an improved Whale Optimization Algorithm (IWOA) that addresses its limitations, such as heavy reliance on the initial solution and susceptibility to local optimum solutions. The proposed IWOA is also applied for the hyperparameter optimization of the Convolutional Neural Network–Bidirectional Gated Recurrent Unit–Convolutional Block Attention Module (CNN-BiGRU-CBAM) to improve the precision of predictions. Ultimately, applying the proposed model to forecast short-term power demand yields results that show that the CBAM effectively addresses the problem of the SE attention mechanism’s inability to capture spatial characteristics fully. The proposed IWOA exhibits a homogeneous dispersion of the initial population and an effective capability to identify the optimal solution. Compared to other models, the proposed model improves R² by 0.00224, reduces the RMSE by 18.5781, and reduces MAE by 25.8940, and the model’s applicability and superiority are validated. Full article

This study investigates the factors that significantly constrain mood selection in Galician within uncertainty adverb constructions, applying a logistic regression model. This analysis identified several significant factors affecting the choice between the indicative and subjunctive moods, including the temporal context of the clause, the preceding adverb, and the interaction of fictional and nonfictional registers with the verb type of the predicate and the gender of the speaker/writer. Time reference and the preceding adverb emerged as primary factors conditioning mood choice, with present and future time frames and adverbs encoding weaker epistemic values significantly predisposing toward the subjunctive mood. This study also highlighted the influence of the gender of the speaker/writer, demonstrating the preference of women toward the indicative mood in fictional texts. Verb type and register interact in complex ways that only partially align with previous findings. The results of the present study contribute to a deeper understanding of syntactic variation in Galician, adding to a growing body of quantitative research on Galician grammar. Full article

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection. Full article

Microbiota tryptophan metabolism and the biosynthesis of indole derivatives play an important role in homeostasis and pathogenesis in the human body and can be affected by the gut microbiota. However, studies on the interplay between gut microbiota and tryptophan metabolites in patients undergoing dialysis are lacking. This study aimed to identify the gut microbiota, the indole pathway in tryptophan metabolism, and significant functional differences in ESRD patients with regular hemodialysis. We performed the shotgun metagenome sequencing of stool samples from 85 hemodialysis patients. Using the linear discriminant analysis effect size (LEfSe), we examined the composition of the gut microbiota and metabolic features across varying concentrations of tryptophan and indole metabolites. Higher tryptophan levels promoted tyrosine degradation I and pectin degradation I metabolic modules; lower tryptophan levels were associated with glutamate degradation I, fructose degradation, and valine degradation modules. Higher 3-indoxyl sulfate concentrations were characterized by alanine degradation I, anaerobic fatty acid beta-oxidation, sulfate reduction, and acetyl-CoA to crotonyl-CoA. Contrarily, lower 3-indoxyl sulfate levels were related to propionate production III, arabinoxylan degradation, the Entner–Doudoroff pathway, and glutamate degradation II. The present study provides a better understanding of the interaction between tryptophan, indole metabolites, and the gut microbiota as well as their gut metabolic modules in ESRD patients with regular hemodialysis. Full article

Museums are called to respond to contemporary challenges and contribute to the formation of more progressive societies. One of the major challenges of our time is climate change and sustainability, and museums could construct cocreated narratives around climate and implement targeted actions for the audiences through their sensorial and affective experiences. More specifically, Geology and Paleontology museums are repositories of knowledge on geoheritage and geoconservation, recognized for their scientific, pedagogical, cultural, and recreational values. The educational potential of geological and paleontological exhibits could be used in informal education activities regarding the climate emergency and the need for sustainability. Taking the above into consideration, this study focuses on both the creation and the evaluation of an educational program for the Natural History Museum of Arsakeio Campus in Psychicko. According to our findings, students really enjoyed their experience in the Museum, gained knowledge regarding mass extinctions driven by climate change, and suggested ways to tackle the current problems and promote our planet’s sustainability. Full article

Most accidents and serious incidents of commercial air transport helicopters occur during standard flight phases, whereby a main cause is pilots’ situational awareness. Enabling pilots to better assess their situational awareness can make an important contribution in reducing the risk of fatal accidents. One approach is to examine a pilot’s gaze behavior with the help of eye tracking. This paper reports the results of a case study with eye tracking measurements during real flight and simulator studies of a standard mission profile. The general gaze behavior is characterized by a dominant, external view, and the airspeed and altitude indicator as the most important flight instruments. A real-world applicability of gaze data obtained in the simulator could be shown. Full article

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss. Full article

Background: To design a novel anomaly detection and localization approach using artificial intelligence methods using optical coherence tomography (OCT) scans for retinal diseases. Methods: High-resolution OCT scans from the publicly available Kaggle dataset and a local dataset were used by four state-of-the-art self-supervised frameworks. The backbone model of all the frameworks was a pre-trained convolutional neural network (CNN), which enabled the extraction of meaningful features from OCT images. Anomalous images included choroidal neovascularization (CNV), diabetic macular edema (DME), and the presence of drusen. Anomaly detectors were evaluated by commonly accepted performance metrics, including area under the receiver operating characteristic curve, F1 score, and accuracy. Results: A total of 25,315 high-resolution retinal OCT slabs were used for training. Test and validation sets consisted of 968 and 4000 slabs, respectively. The best performing across all anomaly detectors had an area under the receiver operating characteristic of 0.99. All frameworks were shown to achieve high performance and generalize well for the different retinal diseases. Heat maps were generated to visualize the quality of the frameworks’ ability to localize anomalous areas of the image. Conclusions: This study shows that with the use of pre-trained feature extractors, the frameworks tested can generalize to the domain of retinal OCT scans and achieve high image-level ROC-AUC scores. The localization results of these frameworks are promising and successfully capture areas that indicate the presence of retinal pathology. Moreover, such frameworks have the potential to uncover new biomarkers that are difficult for the human eye to detect. Frameworks for anomaly detection and localization can potentially be integrated into clinical decision support and automatic screening systems that will aid ophthalmologists in patient diagnosis, follow-up, and treatment design. This work establishes a solid basis for further development of automated anomaly detection frameworks for clinical use. Full article

Proteases represent common targets in combating infectious diseases, including COVID-19. The 3-chymotrypsin-like protease (3CLpro) is a validated molecular target for COVID-19, and it is key for developing potent and selective inhibitors for inhibiting viral replication of SARS-CoV-2. In this review, we discuss structural relationships and diverse subsites of 3CLpro, shedding light on the pivotal role of dimerization and active site architecture in substrate recognition and catalysis. Our analysis of bioinformatics and other published studies motivated us to investigate a novel catalytic mechanism for the SARS-CoV-2 polyprotein cleavage by 3CLpro, centering on the triad mechanism involving His41-Cys145-Asp187 and its indispensable role in viral replication. Our hypothesis is that Asp187 may participate in modulating the pK_a of the His41, in which catalytic histidine may act as an acid and/or a base in the catalytic mechanism. Recognizing Asp187 as a crucial component in the catalytic process underscores its significance as a fundamental pharmacophoric element in drug design. Next, we provide an overview of both covalent and non-covalent inhibitors, elucidating advancements in drug development observed in preclinical and clinical trials. By highlighting various chemical classes and their pharmacokinetic profiles, our review aims to guide future research directions toward the development of highly selective inhibitors, underscore the significance of 3CLpro as a validated therapeutic target, and propel the progression of drug candidates through preclinical and clinical phases. Full article

Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation. Full article

Composite materials and structural optimization are important research topics in heat transfer enhancement. The current evaluation parameter for the conductive heat transfer capability of composites is effective thermal conductivity (ETC); however, this parameter has not been studied or analyzed for its applicability to different heat transfer models and composite structures. In addition, the optimized composite structures of a specific object will vary when different optimization methods and criteria are employed. Therefore, it is necessary to investigate a suitable method and parameter for evaluating the heat transfer capability of optimized composites under different heat transfer models. Therefore, this study analyzes and summarizes three typical conductive heat transfer models: surface-to-surface (S-to-S), volume-to-surface (V-to-S), and volume-to-volume (V-to-V) models. The equivalent thermal conductivity ( ) is proposed to evaluate the conductive heat transfer capability of topology-optimized composite structures under the three models. A validated simulation method is used to obtain the key parameters for calculating . The influences of the interfacial thermal resistance and size effect on  are considered. The results show that the composite structure optimized for the V-to-S and V-to-V models has a  value of only 79.4 W m⁻¹ K⁻¹ under the S-to-S model. However, the  values are 233.4 W m⁻¹ K⁻¹ and 240.3 W m⁻¹ K⁻¹ under the V-to-S and V-to-V models, respectively, which are approximately 41% greater than those of the in-parallel structure. It can be demonstrated that  is more suitable than the ETC for evaluating the V-to-S and V-to-V heat transfer capabilities of composite structures. The proposed  can serve as a characteristic parameter that is beneficial for heat transfer analysis and composite structural optimization. Full article

Yeasts resistant to antifungals have become an increasing risk to human health. One of the best antimicrobial properties is reported to be present in silver nanoparticles (AgNPs); however, little is known about the antimicrobial potential of AgNPs produced using thermophilic bacteria. How AgNPs cause cell death is different depending on the type of the cell, and the mode of death induced is cell-type specific. Apoptosis, one of the types of regulated cell death, can be extremely useful in the fight against infection because surrounding cells that have phagocytic activity can efficiently absorb the apoptotic bodies formed during apoptosis. In the course of this work, for the first time, comprehensive antifungal studies of AgNPs were performed using thermophilic Geobacillus spp. bacteria against Candida guilliermondii, also with the addition of the model yeast Saccharomyces cerevisiae. The determined minimal inhibitory concentrations (MICs) were 10 μg/mL against C. guilliermondii and 50 μg/mL against S. cerevisiae for Geobacillus sp. strain 25 AgNPs, and for Geobacillus sp. 612 the MICs were 5 μg/mL and 25 μg/mL, respectively. It was shown for the first time that the exposure of the yeast cells leads to caspase activation in both S. cerevisiae and C. guilliermondii after exposure to Geobacillus spp. AgNPs. Also, a statistically significant change in the number of cells with permeable membranes was detected. Moreover, it was shown that the antimicrobial effect of the AgNPs is related to ROS generation and lipid peroxidation in C. guilliermondii yeast. Full article

A robust body of research suggests that entrepreneurial activities benefit from financial development and external financing access. However, there is a gap in understanding how and the extent to which the accessibility to financial services is associated with entrepreneurial activity. Based on an unbalanced panel of 2104 Brazilian municipalities spanning 2010–2021 and comprising 23,769 municipality-year observations, our results not only confirm that bank accessibility, proxied by the number of bank branches in a municipality, is positively correlated with the number of firms but also that the relationship is nonlinear, being stronger for larger firms. By estimating a model using first differences, we find a positive causal impact of an additional bank branch on the number of firms in a municipality of 0.2% (about 26 extra firms on average). Our study contributes to the literature by corroborating that access to external financing services shapes entrepreneurial activities. Full article