Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

Non-volatile dynamically switchable color display via chalcogenide stepwise cavity resonators

High-resolution multi-color printing relies upon pixelated optical nanostructures,which is crucial to promote color display by producing nonbleaching colors,yet requires simplicity in fabrication and dynamic switching.Antimony trisulfide(Sb_(2)S_(3))is a newly rising chalcogenide material that possesses prompt and significant transition of its optical characteristics in the visible region between amorphous and crystalline phases,which holds the key to color-varying devices.Herein,we proposed a dynamically switchable color printing method using Sb_(2)S_(3)-based stepwise pixelated Fabry-Pérot(FP)cavities with various cavity lengths.The device was fabricated by employing a direct laser patterning that is a less timeconsuming,more approachable,and low-cost technique.As switching the state of Sb_(2)S_(3) between amorphous and crystalline,the multi-color of stepwise pixelated FP cavities can be actively changed.The color variation is due to the profound change in the refractive index of Sb_(2)S_(3) over the visible spectrum during its phase transition.Moreover,we directly fabricated sub-50 nm nano-grating on ultrathin Sb_(2)S_(3) laminate via microsphere 800-nm femtosecond laser irradiation in far field.The minimum feature size can be further decreased down to~45 nm(λ/17)by varying the thickness of Sb_(2)S_(3) film.Ultrafast switchable Sb_(2)S_(3) photonic devices can take one step toward the next generation of inkless erasable papers or displays and enable information encryption,camouflaging surfaces,anticounterfeiting,etc.Importantly,our work explores the prospects of rapid and rewritable fabrication of periodic structures with nano-scale resolution and can serve as a guideline for further development of chalcogenide-based photonics components.

Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

Predictors of Abnormal Vaginal Discharge among Women of Reproductive Age in Southeast Nigeria

Background: An abnormal vaginal discharge is a common complaint among women of reproductive age, and it can indicate serious conditions like pelvic inflammatory disease and cervical cancer. This study aimed to assess the predictors of abnormal vaginal discharge in women of reproductive age group in Imo State, Southeast Nigeria. Methods: A cross-sectional study was conducted among 368 women of reproductive age group attending the clinic at Federal University Teaching Hospital Owerri, in Imo State, Nigeria. Respondents were recruited using a systematic sampling technique. Data were collected using a pre-tested interviewer-administered questionnaire. Multivariable analysis was performed to determine predictors of abnormal vaginal discharge. Statistical significance was set at p Results: The mean age of the respondents was 30 ± 4.5 years. Predictors of abnormal vaginal discharge were: age 36 - 45 years (OR: 4.5;95% C.I: 1.023 - 8.967, p = 0.041), being a student (OR: 2.4: 95% C.I: 1.496 - 7.336, p = 0.003), use of oral contraceptives (OR: 3.4;95% C.I: 1.068 - 6.932, p = 0.010), use of water cistern (OR: 4.7;C.I: 1.654 - 5.210, p = 0.028) anal hygiene practices (OR: 2.7;95% C.I: 1.142 - 4.809, p Conclusion: These findings suggest that targeted sexual and reproductive health interventions should be provided to reduce the risk of abnormal vaginal discharge in women of reproductive age group.

Atomic layer deposition in advanced display technologies:from photoluminescence to encapsulation

Driven by the growing demand for next-generation displays,the development of advanced luminescent materials with exceptional photoelectric properties is rapidly accelerating,with such materials including quantum dots and phosphors,etc.Nevertheless,the primary challenge preventing the practical application of these luminescent materials lies in meeting the required durability standards.Atomic layer deposition(ALD)has,therefore,been employed to stabilize luminescent materials,and as a result,flexible display devices have been fabricated through material modification,surface and interface engineering,encapsulation,cross-scale manufacturing,and simulations.In addition,the appropriate equipment has been developed for both spatial ALD and fluidized ALD to satisfy the low-cost,high-efficiency,and high-reliability manufacturing requirements.This strategic approach establishes the groundwork for the development of ultra-stable luminescent materials,highly efficient light-emitting diodes(LEDs),and thin-film packaging.Ultimately,this significantly enhances their potential applicability in LED illumination and backlighted displays,marking a notable advancement in the display industry.

Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively.

Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population

Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status.Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed.Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953–0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05).Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

Flexible perovskite light-emitting diodes for display applications and beyond

The flexible perovskite light-emitting diodes(FPeLEDs),which can be expediently integrated to portable and wearable devices,have shown great potential in various applications.The FPeLEDs inherit the unique optical properties of metal halide perovskites,such as tunable bandgap,narrow emission linewidth,high photoluminescence quantum yield,and particularly,the soft nature of lattice.At present,substantial efforts have been made for FPeLEDs with encouraging external quantum efficiency(EQE)of 24.5%.Herein,we summarize the recent progress in FPeLEDs,focusing on the strategy developed for perovskite emission layers and flexible electrodes to facilitate the optoelectrical and mechanical performance.In addition,we present relevant applications of FPeLEDs in displays and beyond.Finally,perspective toward the future development and applications of flexible PeLEDs are also discussed.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

A Clinical Study to Assess the Effectiveness of Oral Combination Kit Therapy in Syndromic Management of Abnormal Vaginal Discharge (FEMINE Study) in Kinshasa, Democratic Republic of Congo

Background: Vaginal discharge is one of most common and nagging problems that women face. About 20% - 25% of women who visit gynecology department complain of vaginal discharge and leucorrhoea. An orally administered combination kit, containing 2 g secnidazole, 1 g azithromycin and 150 mg fluconazole (Azimyn FS Kit), has been successfully evaluated in clinical trials and used in several countries for management syndromic vaginal discharge due to infections. Methods: This is a longitudinal study which aimed to verify the clinical efficacy of the combined oral kit containing secnidazole, azithromycin and fluconazole (Azimyn FS Kit®) in the syndromic treatment of abnormal vaginal discharge in patients received in outpatient consultations in Kinshasa/DR Congo from March to September 2023. Results: Majority of patients had whitish vaginal discharge (51.6%) of average abundance (56.2%), accompanied by pruritus in 72.1% of cases, and dyspareunia in 23.5% of cases and hypogastralgia in 40.2% of cases. One week after treatment with the Azimyn FS® combined kit, at the greatest majority of patients (97.3%), abnormal vaginal discharge had decreased by more than 50% (84.1%). Two weeks after treatment with the Azimyn FS® combined kit, almost all patients (97.3%) no longer had abnormal vaginal discharge which had completely disappeared. Conclusion: A single dose of secnidazole, azithromycin and fluconazole in the form of an oral combi-kit (Azimyn FS Kit) has shown excellent therapeutic effectiveness in the syndromic treatment of abnormal vaginal discharge wherein patients were treated without diagnostic confirmation.

First record of abnormal body coloration in a rockfish Sebastes koreanus(Scorpaenoidei:Sebastidae)from coastal water of China based on morphological characteristics and DNA barcoding

The first record of abnormal body coloration in Sebastes koreanus Kim and Lee,1994,from the Yellow Sea of China,was documented based on morphological characteristics and DNA barcoding.The two rockfish specimens were collected from the coastal waters of Qingdao,China,and the whole body and all fins of them were red.Of the two red-colored rockfish,there were tiny deep red spots on each fin,2 red radial stripes behind and below the eyes and 1 large deep red blotch on the opercula,while the similar stripe and spot patterns are also present in the S.koreanus specimens with normal body coloration.The countable characteristics of the two specimens are in the range of the morphometry of S.koreanus.To further clarify the species identity and taxonomic status of the two specimens,DNA barcode analysis was carried out.The genetic distance between the red-colored rockfish and S.koreanus was 0,and the minimum net genetic distances between the red-colored rockfish and other Sebastes species except for S.koreanus were 3.0%,which exceeds the threshold of species delimitation.The phylogenetic analysis showed that the DNA barcoding sequences of the two red-colored rockfish clustered with the S.koreanus sequences.The above results of DNA barcode analysis also support that the two red-colored rockfish could be identified as the species of S.koreanus.The mechanism of color variation in S.koreanus is desirable for further research and the species could be an ideal model to study the color-driven speciation of the rockfishes.

High-grade serous carcinoma of the fallopian tube in a young woman with chromosomal 4q abnormality:A case report

BACKGROUND Few studies have reported an association between an increased risk of acquiring cancers and survival in patients with 4q deletion syndrome.This study presents a rare association between chromosome 4q abnormalities and fallopian tube highgrade serous carcinoma(HGSC)in a young woman.CASE SUMMARY A 35-year-old woman presented with acute dull abdominal pain and a known chromosomal abnormality involving 4q13.3 duplication and 4q23q24 deletion.Upon arrival at the emergency room,her abdomen appeared ovoid and distended with palpable shifting dullness.Ascites were identified through abdominal ultrasound,and computed tomography revealed an omentum cake and an enlarged bilateral adnexa.Blood tests showed elevated CA-125 levels.Paracentesis was conducted,and immunohistochemistry indicated that the cancer cells favored an ovarian origin,making us suspect ovarian cancer.The patient underwent debulking surgery,which led to a diagnosis of stage IIIC HGSC of the fallopian tube.Subsequently,the patient received adjuvant chemotherapy with carboplatin and paclitaxel,resulting in stable current condition.CONCLUSION This study demonstrates a rare correlation between a chromosome 4q abnormality and HGSC.UBE2D3 may affect crucial cancer-related pathways,including P53,BRCA,cyclin D,and tyrosine kinase receptors,thereby possibly contributing to cancer development.In addition,ADH1 and DDIT4 may be potential influencers of both carcinogenic and therapeutic responses.

Analysis of the Efficacy of Prenatal B-Ultrasound in Diagnosing Fetal Abnormalities

Objective:To explore the positive significance of using prenatal B-ultrasound in diagnosing fetal abnormalities.Methods:A total of 200 pregnant women who visited Shaanxi Provincial People’s Hospital between January 2023 and January 2024 were recruited as the research subjects.All pregnant women received prenatal examinations.A retrospective analysis was carried out to analyze the positive significance of prenatal B-ultrasound examination in the diagnosis of fetal abnormalities.Results:Prenatal B-ultrasound examination detected 10 cases of fetal abnormalities,with a detection rate of 5.00%.When compared with the postnatal examination results of 5.50%,the difference was insignificant(P>0.05).Moreover,comparing the fetal limb abnormalities and cardiovascular abnormalities in prenatal B-ultrasound examination and postnatal examination,one case of congenital heart disease was missed in the prenatal B-ultrasound examination,and the others were consistent with the postnatal examination results,with a coincidence rate of 90.91%,indicating a high compliance rate.Conclusion:Fetal abnormalities have a great impact on mothers,babies,and families,and it is particularly important to strengthen diagnosis during this process.Prenatal B-ultrasound examination can improve the accuracy of diagnosis of fetal abnormalities and can be promoted in clinical practice as a basis for screening fetal abnormalities.

Tooth number abnormality:from bench to bedside

Tooth number abnormality is one of the most common dental developmental diseases,which includes both tooth agenesis and supernumerary teeth.Tooth development is regulated by numerous developmental signals,such as the well-known Wnt,BMP,FGF,Shh and Eda pathways,which mediate the ongoing complex interactions between epithelium and mesenchyme.Abnormal expression of these crutial signalling during this process may eventually lead to the development of anomalies in tooth number;however,the underlying mechanisms remain elusive.In this review,we summarized the major process of tooth development,the latest progress of mechanism studies and newly reported clinical investigations of tooth number abnormality.In addition,potential treatment approaches for tooth number abnormality based on developmental biology are also discussed.This review not only provides a reference for the diagnosis and treatment of tooth number abnormality in clinical practice but also facilitates the translation of basic research to the clinical application.

Time-sequential color code division multiplexing holographic display with metasurface

Color metasurface holograms are powerful and versatile platforms for modulating the amplitude,phase,polarization,and other properties of light at multiple operating wavelengths.However,the current color metasurface holography can only realize static manipulation.In this study,we propose and demonstrate a multiplexing metasurface technique combined with multiwavelength code-division multiplexing(CDM)to realize dynamic manipulation.Multicolor code references are utilized to record information within a single metasurface and increase the information capacity and security for anticracks.A total of 48 monochrome images consisting of pure color characters and multilevel color video frames were reconstructed in dual polarization channels of the birefringent metasurface to exhibit high information density,and a video was displayed via sequential illumination of the corresponding code patterns to verify the ability of dynamic manipulation.Our approach demonstrates significant application potential in optical data storage,optical encryption,multiwavelengthversatile diffractive optical elements,and stimulated emission depletion microscopy.

Metasurfaces for near-eye display applications

Virtual reality(VR)and augmented reality(AR)are revolutionizing our lives.Near-eye displays are crucial technologies for VR and AR.Despite the rapid advances in near-eye display technologies,there are still challenges such as large field of view,high resolution,high image quality,natural free 3D effect,and compact form factor.Great efforts have been devoted to striking a balance between visual performance and device compactness.While traditional optics are nearing their limitations in addressing these challenges,ultra-thin metasurface optics,with their high light-modulating capabilities,may present a promising solution.In this review,we first introduce VR and AR near-eye displays,and then briefly explain the working principles of light-modulating metasurfaces,review recent developments in metasurface devices geared toward near-eye display applications,delved into several advanced natural 3D near-eye display technologies based on metasurfaces,and finally discuss about the remaining challenges and future perspectives associated with metasurfaces for near-eye display applications.