Search Results (2,775)

The fabrication of stable, tailored domain patterns in ferroelectric crystals has wide applications in optical and electronic industries. All-optical ferroelectric poling by pulse laser irradiation has been developed recently. In this work, we studied the creation of the domain structures in MgO-doped lithium tantalate by focused irradiation with a femtosecond near-infrared laser. Cherenkov-type second harmonic generation microscopy was used for domain imaging of the bulk. We have revealed the creation of enveloped domains around the induced microtracks under the action of the depolarization field. The domain growth is due to a pyroelectric field caused by a nonuniform temperature change. The domains in the bulk were revealed to have a three-ray star-shaped cross-section. It was shown that an increase in the field excess above the threshold leads to consequential changes in domain shape from a three-ray star to a triangular and a circular shape. The appearance of comb-like domains as a result of linear scanning was demonstrated. All effects were considered in terms of a kinetic approach, taking into account the domain wall motion by step generation and kink motion driven by excess of the local field over the threshold. The obtained knowledge is useful for the all-optical methods of domain engineering in ferroelectrics. Full article

This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound’s frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model’s ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification. Full article

Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments. Full article

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM’s simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems. Full article

Cognitive decline is a public health concern affecting about 50 million individuals worldwide. Neuroticism, defined as the trait disposition to experience intense and frequent negative emotions, has been associated with an increased risk of late-life cognitive decline. However, the underlying biological mechanisms of this association remain unknown. This study investigated the relationship between genetic predisposition to neuroticism, computed by polygenic risk score (PRS), and performance in cognitive domains of reasoning, processing speed, visual attention, and memory in individuals over age 60. The sample consisted of UK Biobank participants with genetic and cognitive data available (N = 10,737, 4686 females; mean age = 63.4 ± 2.71). The cognitive domains were assessed at baseline for all participants and seven years later for a subset (N = 645, 262 females; mean age = 62.9 ± 2.44). Neuroticism PRS was not associated cross-sectionally with cognitive measures (p > 0.05). However, the trajectory of change for processing speed (β = 0.020; 95% CI = [0.006, 0.035], adjusted p = 0.0148), visual attention (β = −0.077; 95% CI = [−0.0985, −0.0553], adjusted p = 1.412 × 10⁻¹¹), and memory (β = −0.033; 95% CI = [−0.0535, −0.0131], adjusted p = 0.005) was significantly associated with neuroticism PRS. Specifically, a higher genetic predisposition to neuroticism was associated with less decline in these cognitive domains. This trend persisted after sensitivity analysis using complete cases, although it only remained nominally significant for visual attention. Full article

In the digital age, personalized recommendation systems have become crucial for information dissemination and user experience. While traditional systems focus on accuracy, they often overlook diversity, novelty, and serendipity. This study introduces an innovative recommendation system model, Time-based Outlier Aware Recommender (TOAR), designed to address the challenges of content homogenization and information bubbles in personalized recommendations. TOAR integrates Neural Matrix Factorization (NeuMF), Bidirectional Long Short-Term Memory Networks (Bi-LSTM), and Mean Shift clustering to enhance recommendation accuracy, novelty, and diversity. The model analyzes temporal dynamics of user behavior and facilitates cross-domain knowledge exchange through feature sharing and transfer learning mechanisms. By incorporating an attention mechanism and unsupervised clustering, TOAR effectively captures important time-series information and ensures recommendation diversity. Experimental results on a news recommendation dataset demonstrate TOAR’s superior performance across multiple metrics, including AUC, precision, NDCG, and novelty, compared to traditional and deep learning-based recommendation models. This research provides a foundation for developing more intelligent and personalized recommendation services that balance accuracy with content diversity. Full article

Clubroot, a significant soil-borne disease, severely impacts the productivity of cruciferous crops. The identification and development of clubroot resistance (CR) genes are crucial for mitigating this disease. This study investigated the genetic inheritance of clubroot resistance within an F₂ progeny derived from the cross of a resistant parent, designated “377”, and a susceptible parent, designated “12A”. Notably, “377” exhibited robust resistance to the “KEL-23” strain of Plasmodiophora brassicae, the causative agent of clubroot. Genetic analyses suggested that the observed resistance is controlled by a single dominant gene. Through Bulked Segregant Analysis sequencing (BSA-seq) and preliminary gene mapping, we localized the CR gene locus, designated as BraPb8.3, to a 1.30 Mb genomic segment on chromosome A08, flanked by the markers “333” and “sau332-1”. Further fine mapping precisely narrowed down the position of BraPb8.3 to a 173.8 kb region between the markers “srt8-65” and “srt8-25”, where we identified 22 genes, including Bra020861 with a TIR-NBS-LRR domain and Bra020876 with an LRR domain. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analyses confirmed that both Bra020861 and Bra020876 exhibit increased expression levels in the resistant parent “377” following inoculation with P. brassicae, thereby underscoring their potential as key genes implicated in BraPb8.3-mediated clubroot resistance. This study not only identifies molecular markers associated with BraPb8.3 but also enriches the genetic resources available for breeding programs aimed at enhancing resistance to clubroot. Full article

The rapid advancement of artificial intelligence (AI) technology has catalyzed unprecedented innovation in the healthcare industry, transforming medical practices and patient care. However, this progress brings significant ethical challenges, highlighting the need for a comprehensive exploration of algorethics—the intersection of algorithm design and ethical considerations. This study aimed to conduct a narrative review of reviews in the field of algorethics with specific key questions. The review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar. The review revealed a growing emphasis on integrating fairness, transparency, and accountability into AI systems, alongside significant progress in ethical AI development. The importance of collaboration between different domains of scientific production, such as social sciences and standardization (like the IEEE), and the development of guidelines is significantly emphasized, with demonstrated direct impact in the health domain. However, gaps persist, particularly in the lack of standardized evaluation methods and the challenges posed by complex sectors like healthcare. The findings underscore the need and importance for robust data governance to prevent biases and highlight the importance of cross-disciplinary collaboration in creating comprehensive ethical frameworks for AI. The field of algorethics has important applications in the health domain, and there is a significant increase in attention, with a focus on addressing issues and seeking both practical and theoretical solutions. Future research should prioritize establishing standardized evaluation practices for AI, fostering interdisciplinary collaboration, developing sector-specific ethical guidelines, exploring AI’s long-term societal impacts, and enhancing ethical training for developers. Continued attention to emerging ethical standards is also crucial for aligning AI technologies with evolving ethical principles. Full article

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net (‘bead’) architectures (termed ‘Θ-Net’ in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology). Full article

Recent studies revealed anomalous underscreening in concentrated electrolytes, and we suggest that the underscreened electrostatic forces between membrane proteins play a significant role in the process of self-assembly. In this work, we assumed that the underscreened electrostatic forces compete with the thermodynamic Casimir forces induced by concentration fluctuations in the lipid bilayer, and developed a simplified model for a binary mixture of oppositely charged membrane proteins with different preference to liquid-ordered and liquid-disordered domains in the membrane. In the model, like macromolecules interact with short-range Casimir attraction and long-range electrostatic repulsion, and the cross-interaction is of the opposite sign. We determine energetically favored patterns in a system in equilibrium with a bulk reservoir of the macromolecules. Different patterns consisting of clusters and stripes of the two components and of vacancies are energetically favorable for different values of the chemical potentials. Effects of thermal flutuations at low temperature are studied using Monte Carlo simulations in grand canonical and canonical ensembles. For fixed numbers of the macromolecules, a single two-component cluster with a regular pattern coexists with dispersed small one-component clusters, and the number of small clusters depends on the ratio of the numbers of the molecules of the two components. Our results show that the pattern formation is controlled by the shape of the interactions, the density of the proteins, and the proportion of the components. Full article

In the field of Meta-Learning, traditional methods for addressing few-shot learning problems often rely on leveraging prior knowledge for rapid adaptation. However, when faced with insufficient data, meta-learning models frequently encounter challenges such as overfitting and limited feature extraction capabilities. To overcome these challenges, an innovative meta-learning approach based on Sparse Dictionary and Consistency Learning (SDCL) is proposed. The distinctive feature of SDCL is the integration of sparse representation and consistency regularization, designed to acquire both broadly applicable general knowledge and task-specific meta-knowledge. Through sparse dictionary learning, SDCL constructs compact and efficient models, enabling the accurate transfer of knowledge from the source domain to the target domain, thereby enhancing the effectiveness of knowledge transfer. Simultaneously, consistency regularization generates synthetic data similar to existing samples, expanding the training dataset and alleviating data scarcity issues. The core advantage of SDCL lies in its ability to preserve key features while ensuring stronger generalization and robustness. Experimental results demonstrate that the proposed meta-learning algorithm significantly improves model performance under limited training data conditions, particularly excelling in complex cross-domain tasks. On average, the algorithm improves accuracy by 3%. Full article

As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents significant challenges due to its volume and complexity. This systematic literature review surveys patent retrieval techniques over the past decade, focusing on ‘prior art’ and ‘novelty’ searches. Adhering to the PRISMA 2020 guidelines, our research includes 78 pertinent articles selected from a corpus of 1441, providing an in-depth overview of recent advancements, emerging trends, challenges, and future directions in the field of patent prior art retrieval. The review addresses six research questions: defining the current state of the art, evaluating the efficacy of various approaches, examining commonly used patent data collections, exploring the impact of semantic search and natural language processing (NLP) technologies, identifying frequently used components of patent documents, and discussing ongoing challenges in the domain of patent prior art search and retrieval. Our findings highlight the growing use of NLP to enhance the precision and comprehensiveness of patent searches, particularly on the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and the United States Patent and Trademark Office (USPTO) databases. Despite advancements, the specialized and technical nature of patent language continues to pose significant challenges in achieving high accuracy in patent retrieval. Full article

Traditional bearing fault diagnosis methods struggle to effectively extract distinctive, domain-invariable characterizations from one-dimensional vibration signals of high-speed train (HST) bearings under variable load conditions. A deep migration fault diagnosis method based on the combination of a domain-adversarial network and signal reconstruction unit (CRU) is proposed for this purpose. The feature extraction module, which includes a one-dimensional convolutional (Cov1d) layer, a normalization layer, a ReLU activation function, and a max-pooling layer, is integrated with the CRU to form a feature extractor capable of learning key fault-related features. Additionally, the fault identification module and domain discrimination module utilize a combination of fully connected layers and dropout to reduce model parameters and mitigate the risk of overfitting. It is experimentally validated on two sets of bearing datasets, and the results show that the performance of the proposed method is better than other diagnostic methods under cross-load conditions, and it can be used as an effective cross-load bearing fault diagnosis method. Full article

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios that are hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert-in-the-loop validation to develop named entity recognition (NER) systems for an under-resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, potentially providing a valuable resource for further research and application in multilingual clinical settings. Full article

While Kerala’s transition from an agrarian to a service-oriented economy is widely acknowledged, discussions are most often confined to material domains, overlooking overall life satisfaction, which is critical to pursue any profession. This state-wide community-based cross-sectional survey was conducted to gather data from farmers residing in three geographical zones of Kerala, India, North, South and Central, to understand their life satisfaction and how it correlates to their access to resources and social support. From each zone, we randomly chose two districts, and from each district, two panchayats. From each panchayat, we chose one ward to identify a total of 580 eligible farmers. Structured interviews were conducted using door-knock surveys to elicit information from the respondents using a set of standardized questionnaires. The results suggest that the respondents had a mean age of 54.5, with 19.8% being over 65, indicating demographic ageing in the farming sector. Only 46% chose farming as their full-time occupation. Farmers with higher levels of education who owned large plots of land experienced life satisfaction. Interpersonal relationships and social support were significant determinants of life satisfaction, as these factors were pivotal in their access to formal and informal services. With social support being pivotal in life satisfaction, it is imperative to change the social mindset towards farming. Additionally, the government should promote advanced technologies and high-yielding agricultural practises to transform the economic landscape of Kerala in favour of agriculture, which is imperative for the food security of the state. Full article