Application Of Paradigm Research Articles

Ferroelectric Perovskite/MoS2 Channel Heterojunctions for Wide-Window Nonvolatile Memory and Neuromorphic Computing.

Ferroelectric materials commonly serve as gate insulators in typical field-effect transistors, where their polarization reversal enables effective modulation of the conductivity state of the channel material, thereby realizing non-volatile memory. Currently, novel 2D ferroelectrics unlock new prospects in next-generation electronics and neuromorphic computation. However, the advancement of these materials is impeded by limited selectivity and narrow memory windows. Here, new concepts of 2D ferroelectric perovskite/MoS2 channel heterostructures field-effect transistors are presented, in which 2D ferroelectric perovskite features customizable band structure, few-layered ferroelectricity, and submillimeter-size monolayer wafers. Further studies reveal that these devices exhibit unique charge polarity modulation (from n- to p-type channel) and remarkable nonvolatile memory behavior, especially record-wide hysteresis windows up to 177V, which enables efficient imitation of biological synapses and achieves high recognition accuracy for electrocardiogram patterns. This result provides a device paradigm for future nonvolatile memory and artificial synaptic applications.

Noninvasive brain–computer interfaces for children with neurodevelopmental disorders: Attention deficit hyperactivity disorder and autism spectrum disorder

Background:Brain–computer interface (BCI)-mediated neurofeedback training (BCI-NFT) has emerged as a highly promising treatment in the field of neurorehabilitation. Many previous studies have demonstrated the efficacy of BCI techniques in clinical rehabilitation, but children are largely neglected in BCI research. Purpose:This systematic review aimed to synthesize existing studies from technical and clinical application perspectives to identify the current state of research on noninvasive brain–computer interface (NBCI) technology in children with two major neurodevelopmental disorders, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Methods:Five relevant electronic databases were searched (PubMed, Web of Science, the Cochrane Library, Embase, and the Cumulative Index of Nursing and Allied Health Literature). The publication dates ranged from the inception of each database to June 2024. Randomized controlled trials (RCTs) investigating the use of NBCI technology in children with ASD or ADHD were included. Manual searches of the clinical trial registry platforms and the reference lists of reviews related to the study topic were also conducted. Two independent reviewers performed the literature screening, data extraction, and risk of bias assessment. Results:A total of 24 RCTs involving 1998 children with ASD or ADHD were included in this systematic review. With respect to input brain signals, functional magnetic resonance imaging (fMRI) (4.2%), electroencephalography (EEG) combined with fMRI (4.2%), and EEG combined with galvanic skin response (GSR) sensors (4.2%) were utilized in one study each. Seven studies employed EEG combined with electrooculogram (EOG) (29.1%), and the remaining fourteen studies used EEG alone (58.3%). Compared with those of the controls, significant improvements in both behavioral aspects and brain activity in patients were observed in eleven studies (45.8%). NBCI technology has a positive effect on both the behavioral and brain activity levels of children with ASD or ADHD, while it still faces challenges in the paediatric population, particularly in terms of signal processing and the unique cognitive and physiological developmental stages of children, which may complicate the application of these technologies in this population. Conclusion:It demonstrated that there has a high potential for NBCI application in the field of neurodevelopmental disorders. Future research should focus on developing advanced machine learning algorithms to improve neural signal decoding capabilities and on creating child-appropriate application paradigms to explore the long-term efficacy of these algorithms.

Enhanced voltage management method based on hybrid transformer for wind power integration applications

The rapid voltage fluctuations and even exceedances caused by power variability of wind generations significantly impede their application within the power system. This paper presents a method for replacing the electromagnetic transformer at the Point of Common Coupling of wind power delivery systems with a hybrid transformer (HT) to enhance its voltage management and grid integration capabilities. Considering the operation voltage in wind farm and scalability, a cascaded H-bridge based HT model has been established. The operation modes and a set of control strategies groups have been analyzed and proposed. The HTs operate by default in reactive power compensation mode to manage the dual-side voltage coordinately. In case of reactive power contention and circulation, this paper proposes an additional reactive power circulation suppression method. Once it exceeds the reactive voltage regulation limits due to the grid faults or changes of gird operation modes, HT adopts ratio fast regulation mode, maintaining the wind-farm side node voltage and preventing wind turbine tripping. The influence of HT leakage reactance has also been studied here. Finally, the effectiveness of the proposed method is validated in simulation, which provides a new paradigm for the operational application of HTs.

Applications of remote sensing technology in agriculture: A review of contributions to food security challenges and strategies in developing countries

In the agricultural sphere, particularly in developing countries where food security is still a major concern, remote sensing technology has also surfaced as an important tool. In this review, the usages of remote sensing in agricultural areas and its contributions to food security are surveyed; The overall framework is illustrated dynamically by indicating available image platforms that were instrumental for agriculture application paradigms. This research identifies certain areas where some selected case studies and existing literature are reviewed to serve as proof that remote sensing technology has been tremendously impactful. Areas like crop monitoring, yield prediction, or natural resource management have been very well reported by the literature review process adopted. This field analysis of related studies used highly sophisticated RS sensor data. The challenges like data availability, technological limitations, and socio-economic factors have been discussed besides the strategies, for instance, capacity building, policy interventions, and prospects for technological advancements. These results underscore the vitality and adoption of remote sensing technologies in agricultural practices and policies which is the key driver of improved food security in developing countries.

Zn/Cu Bi‐Single‐Atom Nanoplatform: Hexagonal Anti‐Tumor Warrior by ROS Amplification for DOX Resistance Reversal and Immune Activation

AbstractSevere resistance of doxorubicin (DOX) caused by drug efflux and immunosuppression has led to a high treatment failure risk of breast cancer (BC). Compared with the single atom nanozymes, the bi‐single‐atomic nanozymes obtained through sequential single‐atom synthesis strategy in different spaces of the same nanomaterial, can significantly improve the space utilization efficiency and catalytic capacity. In this study, bi‐single‐atom nanozymes (Zn/Cu‐BSAN‐DOX NPs) are designed to reverse BC DOX resistance by causing damage to mitochondria of mesenchymal stem cells (MSCs) and 4T1 cells through reactive oxygen species (ROS) amplification. In situ H2O2 self‐supply is improved by releasing DOX through activating the nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (NOXs). ROS amplification is triggered by enhanced chemodynamic therapy (CDT) and sonodynamic therapy (SDT) of Zn/Cu‐BSAN, which efficiently reverses the DOX resistance by breaking the hyaluronic acid (HA) barrier and DOX efflux. Furthermore, ROS amplification significantly promotes the immune improvement including DCs maturation, T cell activation, and the polarization of M1 macrophage. In summary, as “hexagonal” anti‐tumor warrior, Zn/Cu‐BSAN‐DOX NPs show great potential for multimodal DOX‐resistant BC therapy, which further expands the new preparation strategy and the clinical anti‐tumor application paradigm of single‐atom nanozymes.

A Novel Approach for Sentiment Analysis of a Low Resource Language Using Deep Learning Models

Sentiment analysis is a process of dealing with people's opinions, remarks, and comments to extract valuable insights from them. Sentiment analysis can be used for various purposes like market analysis, campaign monitoring, decision-making, etc. In recent years, there has been much research on sentiment classification, particularly in English. However, these existing approaches used for the English language cannot be applied to the Urdu language. The substantial rise in communication traffic, including audio, text, video, and pictures, has significantly shifted the Internet of Things (IoT) from scalar to Multimedia Internet of Things (MIoT). So far, the integration of MIoT and NLP systems has received less attention, but it has evolved as a novel research paradigm for smart applications. This article proposes deep learning techniques for sentence-level Urdu sentiment analysis (Urdu SA) for MIoT. Our approach consists of various phases, i.e., data gathering, text preprocessing, model training, testing, and evaluation. A data set of 25 thousand Urdu reviews are used for training the proposed models. This data set is built by scraping various Urdu blogs and social media platforms, and some part of the IMDB data set is used after translating it into the Urdu language. Native Urdu speakers do data annotation, and various preprocessing techniques, i.e., tokenization, stemming, etc., are applied. The two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM), are trained on preprocessed Urdu reviews to find their sentiments in this article. Both models are tested using various combinations of hyperparameters, and each model's accuracy and F1 scores are evaluated. The study results show that the LSTM model outperforms the CNN model by achieving a 96% accuracy and 91% F1 score.

Robust and Scalable Federated Learning Framework for Client Data Heterogeneity Based on Optimal Clustering

Federated learning is a promising paradigm for applications across a variety of domains. However, there are some challenges that must be addressed in real-world scenarios, particularly the data heterogeneity among participating clients. Most existing studies primarily focus on the issue of non-independent and identically distributed data, but they do not consider the critical aspect of data quality heterogeneity. When low-quality data is contributed by some clients, the efficacy of models trained through the traditional approaches will be significantly compromised. Therefore, we propose ROSCFL, a robust and scalable federated learning framework for client data heterogeneity based on optimal clustering. We first develop a cluster contribution evaluation strategy based on the optimal clustering to quantify the contribution of each cluster. Next, we design a robust model aggregation strategy, which effectively mitigates the impact of low-quality data on the global model by optimizing weight allocation and client sampling. Finally, we introduce a client incorporation mechanism to enhance the scalability of ROSCFL. Extensive experiments have been conducted, and the results demonstrate that ROSCFL achieves strong robustness and scalability, particularly in scenarios wherein data distribution and quality heterogeneity coexist.

Stainless Steel Mesh in Electrochemistry: Comprehensive Applications and Future Prospects.

Stainless steel mesh (SSM) has emerged as a cornerstone in electrochemical applications owing to its exemplary versatility, electrical conductivity, mechanical robustness, and corrosion resistance. This state-of-the-art review delves into the diverse roles of SSM across a spectrum of electrochemical domains, including energy conversion and storage devices, water treatment technologies, electrochemical sensors, and catalysis. We meticulously explore its deployment in supercapacitors, batteries, and fuel cells, highlighting its utility as a current collector, electrode, and separator. The review further discusses the critical significance of SSM in water treatment processes, emphasizing its efficacy in supporting membranes and facilitating electrocoagulation, as well as its novel uses in electrochemical sensing and catalysis, which include electrosynthesis and bioelectrochemistry. Each section delineates the recent advancements, identifies the inherent challenges, and suggests future directions for leveraging SSM in electrochemical technologies. This comprehensive review showcases the current state of knowledge and articulates the novel integration of SSM with emerging materials and technologies, thereby establishing a new paradigm for sustainable and efficient electrochemical applications. Through critical analysis and insightful recommendations, this review positions itself as a seminal contribution, paving the way for researchers and practitioners to harness the full potential of SSM in advancing the electrochemistry frontiers.

3D printing of active mechanical metamaterials: A critical review

The emergence of mechanical metamaterials from 4D printing has paved the way for developing advanced hierarchical structures with superior multifunctionalities. In particular, 4D-printed mechanical metamaterials exhibit extraordinary mechanical performance by integrating multiphysics stimuli with advanced structures when actuated by external factors, thereby altering their shapes, properties, and functionalities. This critical review offers readers a comprehensive overview of the rapidly growing 4D printing technology for developing novel mechanical metamaterials. It provides essential information about the multifunctionalities of 4D-printed mechanical metamaterials, including energy absorption and shape-morphing behavior in response to physical, chemical, or mechanical stimuli. These capabilities are key to developing smart and intelligent structures for multifunctional applications such as biomedical, photonics, acoustics, energy storage, and thermal insulation. The primary focus of this review is to describe the structural and functional applications of mechanical metamaterials developed through 4D printing. This technology leverages the shape-shifting functions of smart materials in applications such as micro-grippers, soft robots, biomedical devices, and self-deployable structures. Additionally, the review addresses current progress and challenges in the field of 4D-printed mechanical metamaterials. In conclusion, recent developments in 4D-printed mechanical metamaterials could establish a new paradigm for applications in engineering and science.

Prototipe Kurikulum PBI dan IKMBKM Sesuai Permendikbudristek No.53 Tahun 2023

This research is based on the paradigm of curriculum application or implementation, so the research data analysis technique uses the ADDIE method, namely Analyze, Design, Development, Implementation, and Evaluation to answer the research problem questions: (a) How is the current prototype of the PBI study program curriculum?; (b) How is the prototype of the PBI study program curriculum according to Permendikbudristek No. 53 of 2023? Thus, the purpose of the study is to determine the current PBI curriculum prototype and the PBI curriculum prototype according to Permendikbudristek No. 53 of 2023. The research was carried out in the odd semester of the 2023/2024 academic year. The research results obtained were in the form of a prototype of the PBI study program curriculum development FKIP-UHKBPNP according to Permendikbudristek RI No. 53 of 2023. Furthermore, the agile development method and prototype of the results of the curriculum development were used to be implemented in the PBI study program FKIP-UHKBPNP. The definition of development in this study is a scientific way to obtain data based on initial data, so that it can be used to produce, develop, and validate products. Product validation is also confirmed to users which is carried out through FGD, zoom, or direct meetings. The state of the arts of the research is a documentation study of the current PBI curriculum prototype and curriculum development to produce a PBI curriculum prototype according to Permendikbudristek RI No. 53 of 2023. Thus, the novelty or newness as a research finding is the PBI curriculum prototype according to Permendikbudristek RI No. 53 of 2023 which will be implemented in the PBI study program FKIP-UHKBPNP.

Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?

Effective control of energy consumption in public buildings holds paramount significance for global sustainable development. However, uncertainty in occupant behavior during operational phase often leads to a substantial discrepancy between the designed and actual energy consumption. While there is existing research on occupant behavior in public buildings utilizing stochastic modeling, statistical modeling, data mining, and agent-based modeling methods, the connotations and formation mechanisms have not been systematically revealed, rendering these developed models weak in applicability and reliability in practical scenarios. Reinforcement learning, which can perceive aspects of external environment and learn from stochastic interactions spontaneously, has been distinguished as a promising method for occupant behavior modeling. This research aims to develop a paradigm for reinforcement learning application in occupant behavior modeling in public buildings. Semi-structured interviews and literature review were combined to collect relevant information, while reasons for using reinforcement learning and applicable algorithms were discerned through qualitative analysis. Finally, detailed frameworks were proposed and validated through focus groups, offering a comprehensive guide that will serve as a reference for future research. This includes steps for collecting occupant-related data, selecting appropriate algorithms, and training and deploying reinforcement learning agents effectively. This research innovatively proposed a “panorama” of reinforcement learning for occupant behavior modeling in public buildings, laying a solid foundation for indoor environment and energy efficiency improvement in public building operations.

Three-Dimensional Bioprinting: A Comprehensive Review for Applications in Tissue Engineering and Regenerative Medicine.

Since three-dimensional (3D) bioprinting has emerged, it has continuously to evolved as a revolutionary technology in surgery, offering new paradigms for reconstructive and regenerative medical applications. This review highlights the integration of 3D printing, specifically bioprinting, across several surgical disciplines over the last five years. The methods employed encompass a review of recent literature focusing on innovations and applications of 3D-bioprinted tissues and/or organs. The findings reveal significant advances in the creation of complex, customized, multi-tissue constructs that mimic natural tissue characteristics, which are crucial for surgical interventions and patient-specific treatments. Despite the technological advances, the paper introduces and discusses several challenges that remain, such as the vascularization of bioprinted tissues, integration with the host tissue, and the long-term viability of bioprinted organs. The review concludes that while 3D bioprinting holds substantial promise for transforming surgical practices and enhancing patient outcomes, ongoing research, development, and a clear regulatory framework are essential to fully realize potential future clinical applications.

Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable.

Fast, approximation-free molecular simulation of the SPC/Fw water model using non-reversible Markov chains

In a world made of atoms, computer simulations of molecular systems such as proteins in water play an enormous role in science. Software packages for molecular simulation have been developed for decades. They all discretize Hamilton’s equations of motion and treat long-range potentials through cutoffs or discretization of reciprocal space. This introduces severe approximations and artifacts that must be controlled algorithmically. Here, we bring to fruition a paradigm for molecular simulation that relies on modern concepts in statistics to explore the thermodynamic equilibrium with an exact and efficient non-reversible Markov process. It is free of all discretizations, approximations, and cutoffs. We explicitly demonstrate that this approach reaches a break-even point with traditional molecular simulation performed at high precision, but without any of its approximations. We stress the potential of our paradigm for crucial applications in biophysics and other fields, and as a practical approach to molecular simulation. We set out a strategy to reach our goal of rigorous molecular simulation.

Implementing Linked Art in a Multi-Modal Database for Cross-Collection Discovery

Yale University has implemented a knowledge graph based discovery system that brings together the various art, natural history, archival, conservation and bibliographic collections using Linked Open Usable Data standards such as Linked Art and IIIF. This system comprises more than 41 million records, which would expand to more than 2 billion RDF triples, and is thus at a similar scale to Europeana. This paper presents the lessons learned from the five year effort around the usability of linked data structures across the organization, the technologies needed to make use of the knowledge in a performant way, and the appropriate design paradigms for front end applications which make the graph easily and intuitively accessible to researchers and the public, including the necessity of consistency in data modeling, that records are an essential concept to maintain through multi-modal systems, and the use of hypertext and web caches to maintain the separation between systems.

A Brief Systematics Review and Visualization of Smart Cities based on Fog and Big Data Analysis Technology

The fog computing is based on IoT, and same as clouds are on the edge of a network between a device and the cloud, which can provide very close service to the device in real time. cloud computing frameworks also extend support to emerging application paradigms such as IoT, Fog computing, Edge, and Big Data through service and infrastructure. the ubiquitous deployment of sensors in smart cities requires a high-performance computing paradigm to support big data analysis with smart technologies and communications in IoT, providing location-awareness and latency-sensitive computing near the data sources (i.e., at the edge of the network). Cloud Computing paradigms widely used in enterprises to address the emerging challenges of big data analysis because of its scalable and distributed data management scheme. However, data centers in the Cloud faces great challenges on the burden of exploding amount of big data and the additional requirements of location awareness and low latency at the edge of network necessary for smart cites. the widely distributed sensor networks generate massive volume of data, which leads to a “Big Data “analysis challenge. The machine-to-machine(M2M) communication among massive numbers of sensors will dominate future communication network traffic, namely Internet of Things, instead of traditional Internet of Contents (IoC) in human-to-human and human-to-machine communication. In this review system where we took the research article around 33 from the different database and also taken 1987 datasets form the PubMed database from 1959 to 2024 (02 June 2024). In addition, depending on the different requirement which is partially processed the data. Then the gathered data is stored at the ledger unit through the smart cities based on the fog computing and big data technology. The hashing identity-based data encryption is a public key encryption which uses a digital signature to encrypt the data to prevent the information from the third party.

Optically levitated micro gyroscopes with an MHz rotational vaterite rotor

The field of levitated optomechanics has experienced significant advancements in manipulating the translational and rotational dynamics of optically levitated particles and exploring their sensing applications. The concept of using optically levitated particles as gyroscopes to measure angular motion has long been explored but has not yet been proven either theoretically or experimentally. In this study, we present the first rotor gyroscope based on optically levitated high-speed rotating particles. The gyroscope is composed of a micrometer-size ellipsoidal vaterite particle that is driven to rotate at MHz frequencies in a vacuum environment. When an external angular velocity is input, the optical axis deviates from its initial position, resulting in changes in the frequency and amplitude of the rotational signal. By analyzing these changes, the angular velocity of the input can be accurately detected, making it the smallest rotor gyroscope in the world. The angular rate bias instability of the gyroscope is measured to be 0.08°/s and can be further improved to as low as 10−9°/h theoretically by cooling the motion and increasing the angular moment of the levitated particle. Our work opens a new application paradigm for levitated optomechanical systems and may pave the way for the development of quantum rotor gyroscopes.

Flexible Low‐Voltage MXene Floating‐Gate Synaptic Transistor for Neuromorphic Computing and Cognitive Learning

AbstractNeuromorphic computing, inspired by the functionality of biological neural networks, has emerged as a promising paradigm for artificial intelligence applications, especially in the field of flexible electronics. Among the various artificial synaptic devices, floating‐gate synaptic transistors exhibit long‐term synaptic plasticity, but they face the challenge of achieving flexible compatibility. In this work, the first demonstration of a flexible MXene floating‐gate synaptic transistor is reported, which uses multiple layers of MXene as floating gates and MXene nanosheets as charge state modulators. The device shows excellent mechanical flexibility and can operate at low voltages, which improves its suitability for wearable electronic devices. It can also emulate Pavlovian conditioned reflexes under external stress, suggesting its potential for cognitive learning. Moreover, the device is utilized for handwritten digit recognition by simulating a fully connected neural network, achieving a high recognition accuracy of 92.0%. This demonstrates its practical applicability in neuromorphic computing. Besides, this research achieves the patterning of MXene and its application in flexible floating‐gate transistors. It provides a new solution for the integrated fabrication of flexible artificial synaptic devices.

Conformation-Switchable Polypeptides as Molecular Gates for Controllable Drug Release.

The control over secondary structure has been widely studied to regulate the properties of polypeptide materials, which is used to change their functions in situ for various biomedical applications. Herein, we designed and constructed enzyme-responsive polypeptides as gating materials for mesoporous silica nanoparticles (MSNs), which underwent a distorted structure-to-helix transition to promote the release of encapsulated drugs. The polypeptide conjugated on the MSN surface adopted a negatively charged, distorted, flexible conformation, covering the pores of MSN to prevent drug leakage. Upon triggering by alkaline phosphatase (ALP) overproduced by tumor cells, the polypeptide transformed into positively charged, α-helical, rigid conformation with potent membrane-penetrating capabilities, which protruded from the MSN surface to uncover the pores. Such a transition thus enabled cancer-selective drug release and cellular internalization to efficiently kill tumor cells. This study highlights the important role of chain flexibility in modulating the biological function of polypeptides and provides a new application paradigm for synthetic polypeptides with secondary-structure transition.

DeepCOVID: a deep learning approach for accurate COVID-19 detection in point-of-care lung ultrasound

Sickness still continued to spread through several countries when it first appeared in China. The number of COVID-19 cases is rising daily worldwide, posing a severe threat to the government and the populace. As a result of the virus’s rapid spread, doctors are having trouble recognizing positive cases. It is obvious that computer-based diagnosis must be developed to get results at a reasonable cost. The classic convolutional neural network (CNN) is used for this, utilizing the CT dataset, and the upgraded CNN model is used with the lung ultrasound (LUS) dataset. The CT and LUS COVID imaging datasets are compared in the model. The accuracy of both deep learning models is higher. We customized ResNet50, a pre-trained deep learning architecture, for a web application paradigm. First, we suggest a method for normalizing data that addresses its variability because it is collected in hospitals using various CT scanners and ultrasound machines. Second, we identify COVID-19 patients using U-Net segmentation and classification. The CNN architecture is added for deep learning purposes, and Res-Net 50 offers incredible accuracy.