Conventional Limit Research Articles

Development of a machine‐learning model for microplastic analysis in an FT‐IR microscopy image

AbstractThe escalating concern regarding microplastics (MPs) in the environment has recently accentuated the need for comprehensive analyses across various matrices. Fourier Transfrom Infrared (FT‐IR) microscopy is widely used method for MP identification, but challenges arise due to the presence of secondary materials on real samples, causing inaccuracies in spectral matching. To tackle this issue, we propose a solution: a 1D‐convolution neural network (1D‐CNN) machine‐learning model classifying FT‐IR spectra into 16 polymer species. Using a dataset of 5413 spectra, with 80% (4330) for training and 20% (1083) for external testing, our method achieved 98.59% accuracy for cross‐validation and 92.34% for external validation. This study underscores the efficacy of machine learning in discerning polymer types among MPs, even in real samples tainted by secondary materials. The implementation of our 1D‐CNN model marks a significant leap in overcoming conventional method limitations, providing a robust tool for accurately unraveling MPs intricacies in environmental matrices.

Large-Scale Fabrication of Freestanding Polymer Ultrathin Porous Membranes for Transparent Transwell Coculture Systems.

Advancements in cell coculture systems with porous membranes have facilitated the simulation of human-like in vitro microenvironments for diverse biomedical applications. However, conventional Transwell membranes face limitations in low porosity (ca. 6%) and optical opacity due to their large thickness (ca. 10 μm). In this study, we demonstrated a one-step, large-scale fabrication of freestanding polymer ultrathin porous (PUP) membranes with thicknesses of hundreds of nanometers. PUP membranes were produced by using a gap-controlled bar-coating process combined with polymer blend phase separation. They are 20 times thinner than Transwell membranes, possessing 3-fold higher porosity and exhibiting high transparency. These membranes demonstrate outstanding molecular permeability and significantly reduce the cell-cell distance, thereby facilitating efficient signal exchange pathways between cells. This research enables the establishment of a cutting-edge in vitro cell coculture system, enhancing optical transparency, and streamlining the large-scale manufacturing of porous membranes.

Accurate nanoparticle size measurement by combining photometry and tracking analysis

Accurate determination of nanoparticle size is paramount in various fields, including molecular imaging, food safety, drug delivery, and nanobiotechnology. Conventional methods face limitations in accurately resolving different size distributions. Here, we introduce an approach to measure size distribution by combining photometry and tracking analysis of single particles. Our approach is based on a plasmonic dark field imaging system to image and track individual nanoparticles. Scattering intensity, spot size, and diffusion coefficient are quantified and fed into the machine learning model to establish the relationship with particle sizes. Compared with conventional methods, our approach shows improved accuracy in size measurement. To verify the universality of our method, we further demonstrate the sizing capability at diverse and complex conditions, enabling us to discriminate particle size differences within 20 nm in polydisperse systems.

Flux transfer circuits breaking conventional limit in transfer coefficient based on a negative inductance of a π-junction

We have demonstrated transfer coefficients breaking the conventional limit in flux transfer circuits (FTCs) by introducing a π-phase-shifted Josephson junction (π-junction), where the FTCs include an input/output inductor. According to the current-phase relationship of a π-junction, the π-junction behaves as an inductor with intrinsically negative kinetic inductance. When a single-π-junction superconducting quantum interference device (π-SQUID) in which a geometric inductor is placed in parallel with the π-junction is formed, a current flowing on the inductor, that is, the internal flux is increased against an input current or an input flux supplied externally to the π-SQUID in case that the π-SQUID shows no hysteresis in characteristics of internal-external flux. The FTC under investigation (π-FTC) is composed of two identical π-SQUIDs sharing a π-junction. The magnitude of the internal flux exceeds that of the external flux in the π-SQUID near zero external flux. Using this effect, the transfer coefficients are expected to be increased in the π-FTCs. Numerical analysis for π-FTCs reveals that the transfer coefficients exceed the conventional limit in a wide range of input currents corresponding to the input flux, although the negative kinetic inductance depends on the magnitude of the input. We made several π-FTCs for critical currents of the π-junctions of 50 πA and 60 πA. The output flux was measured by constructing a flux-locked loop. The experimentally obtained ratios of the transfer coefficients of the π-FTCs to the coefficient of the conventional FTC made on the same chip agree with the numerical results, which supports the negative kinetic inductances cause the increased coefficients breaking the conventional limit. Because the transfer coefficient is almost independent of input currents, we believe that the π-FTCs are applicable for strengthening not only couplings used in quantum annealers or SQUID sensors but also couplings used in superconductor digital circuits.

EFFECTS OF COVID-19 ANXIETY AND COVID-19 QUALITY OF LIFE ON COVID-19 BURNOUT IN PSYCHIATRIC NURSES

Nurses are key professionals in fighting the COVID-19 pandemic and they provide services beyond expectations. Although psychiatric nurses are suffering from anxiety and burnout they need to enhance and protect their quality of life. The major aim of the study was to investigate the effects of COVID-19 anxiety on COVID-19 quality of life and COVID-19 burnout, and the impact of COVID-19 quality of life on COVID-19 burnout on psychiatric nurses. The descriptive-relational design was used. The sample consisted of 159 nurses working in a state psychiatric hospital in İstanbul, determined by convenience sampling method. The data of the study was collected by using the socio-demographic form, COVID-19 Anxiety Scale, COVID-19 Life Quality Scale, and COVID-19 Burnout Scale. Statistically significant relationships were found between COVID-19 anxiety, COVID-19 life quality, and COVID-19 burnout. The increase in the anxiety of psychiatric nurses due to COVID-19 also increases the negative effect on their quality of life. COVID-19 anxiety positively affects burnout related to COVID-19. The increase in the negative effects of COVID-19 on the life quality of nurses causes an increase in burnout levels. The effect of COVID-19 on their life quality and the burnout based on COVID-19 was investigated in the research. Nurses are surpassing traditional boundaries, providing their services without constraints to confront the challenges posed by the COVID-19 pandemic. Their dedication knows no bounds as they work tirelessly to navigate through these difficult times, transcending conventional limits to offer essential care and support. Policymakers can alleviate the increasing physical and psychosocial burden of psychiatric nurses by providing financial and moral support. Additionally, medical, psychosocial, and legal resources should be provided to psychiatric nurses.

SnS2 nanosheets/Ag2S quantum dots heterojunction supported by YSZ nanofibers enable real-time room-temperature NO2 gas sensor

Air pollution has emerged as a critical global issue, necessitating the development of effective real-time gas monitoring technologies. However, conventional gas sensors face limitations such as high energy consumption and sluggish response/recovery kinetics. In this study, we report a novel SnS2/Ag2S heterojunction supported by yttria-stabilized zirconia nanofibers (YSZ@SnS2/Ag2S) through a facile electrospinning, hydrothermal and subsequent cation-exchange method. This hierarchical composite nanofiber consists of well-distributed Ag2S quantum dots (QDs) attached to vertical anchored SnS2 nanosheets, forming an interconnected network supported by YSZ nanofibers. The integrated sensor exhibited high response of 3.27 and swift response/recovery times of 12/27 s to 1 ppm NO2 at room temperature, respectively. Benefited from highly permeable three-dimensional continuous conductive nanofibers, along with the hierarchical nanosheets/QDs structure, the gas transmission/diffusion and number of reactive sites were ensured. Additionally, the interfacial built-in electric field of SnS2/Ag2S heterojunction further enhanced the electron transfer, carrier migration and gas-sensitive reaction kinetics. This study provides insights into the development of functional composite nanofibers and paves the way for the exploitation of real-time room-temperature gas sensing applications.

Breaking conventional limits of silicon content in Fe-xSi magnetic alloys through additive manufacturing

Silicon steels, e.g., Fe-3.2% Si alloys, are widely used in energy conversion and transmission. Increasing the Si content can enhance electrical resistivity and reduce magnetic hysteresis loss, improving the energy efficiency. However, high silicon content decreases ductility and workability, limiting the Si content of the alloys that can be produced by conventional manufacturing. Instead, we used additive manufacturing by the direct energy deposition technique to produce high Si content Fe-Si alloys. Dense samples with up to 20% Si were successfully fabricated for the first time. A substantial change in saturation magnetization (from 90 to 209 emu/g) and a three-fold increase in hardness was observed with higher Si content. The electrical resistivity values tripled, enhancing the attractiveness of these higher Si content alloys. The yield strength, ultimate tensile strength also increased, from 71 to 545 MPa, and 91 to 567 MPa, respectively. The coercivity remained relatively unchanged in the range of 9.1 to 10.8 Oe. Our results demonstrate the potential of fabrication of bulk high Si content Fe-Si alloys via additive manufacturing.

Bi‐Directional Growth of Thin Films: Unlocking Anisotropic Ferromagnetism and Superconductivity

AbstractThe pursuit of breakthroughs in thin film technology drives the exploration of novel growth strategies for quantum materials that surpass conventional limitations. Departing from the prevailing unidirectional growth approach, the methodology allows for atomic precision in both the in‐plane and out‐of‐plane directions. To demonstrate the capabilities of this transformative technique, a bi‐directionally grown superlattice comprised of alternating LaMnO3 and SrMnO3 layers is engineered, enabling the emergence of interfacial ferromagnetism. By adopting this superlattice as a model system, the vast potential of the approach is highlighted through comprehensive analysis and characterization. Furthermore, the application of the method is extended to the growth of superconducting La1.84Sr0.16CuO4 thin films on various offcut substrates. Remarkably, these substrates induce an anisotropic critical current originating from two distinct mechanisms.

Lyrical prayer poetry by priest poets. K. Kravtsov’s and S. Kruglov’s poetic prayers

The article undertakes a comparative study of the lyrical prayer poetry authored by K. Kravtsov and S. Kruglov, both ordained priests as well as poets and each offering a personal interpretation of the canon of prayer, transforming it into a unique poetic genre. Going into the history of the phenomenon, V. Razumov provides a classification of the prayer genres proper, noting that the diversity of prayers can be grouped into five principal types: invective, supplication (including intercession on somebody’s behalf), penitence, thanksgiving, and praise. Those are the prayer intentions explored in Kruglov’s and Kravtsov’s poetry, with the addition of elements typical of contemporary as well as folk poetry (a stream of consciousness, a counting rhyme, intertextual references, a lyrical hero in a particular role, a civically-minded lyrical subject, etc.). Razumov finds that Kruglov extends the limits of the genre convention, whereas Kravtsov appears to transcend them. This difference notwithstanding, both poets write at the confluence of literary and liturgical traditions and undoubtedly enrich the tuning and intonation of modern poetry.

DDOS Attacks Detection via Deep Learning Approaches

Abstract: The proliferation of Distributed Denial of Service (DDoS) attacks presents a pressing challenge to network security. Conventional rule-based detection methods are increasingly inadequate against the evolving tactics employed by cyber adversaries. This journal proposes a novel approach integrating Support Vector Machines (SVM) into advanced machine learning architectures for fortified DDoS detection. The research methodology initiates with comprehensive data collection, gathering diverse network traffic scenarios and DDoS attack instances. This dataset becomes the foundation for subsequent phases, employing sophisticated feature engineering to extract vital patterns for model development. The feature selection process involves using feature engineering techniques including Data Collection, Model Development and SVM Integration, to extract the most discriminative and relevant features from the network traffic data. The primary innovation lies in the creation of a hybrid CNN-LSTM model, capable of discerning spatial and temporal dependencies within network data, thereby augmenting DDoS threat identification. The integration of SVM into machine learning and deep learning paradigms forms the crux of this study. By leveraging SVM's classification proficiency alongside the CNN-LSTM model's capabilities, a sophisticated DDoS detection system aims to surpass conventional rule-based limitations.

Genetically Engineered Mesenchymal Stem Cells Using Viral Vectors: A New Frontier in Anti-Angiogenic Therapy

Mesenchymal stem cells (MSCs) are adult stem cells that possess the remarkable ability to self-renew and differentiate into various cell lineages. Due to their regenerative potential, MSCs have emerged as the most commonly used stem cell type in clinical applications. Angiogenesis, the formation of new blood vessels, plays a critical role in several pathological conditions, including ocular neovascular diseases, cancer, and inflammatory disorders. Conventional anti-angiogenic therapies face limitations such as frequent visits for repeated doses, off-target effects and resistance development. Recent advances in genetic engineering techniques have opened up novel avenues in regenerative medicine. Genetically engineering MSCs using viral vectors presents a promising strategy to specifically target angiogenesis and enhance anti-angiogenic therapies' efficacy. Viral vectors, including lentiviruses, adeno-associated viruses and adenoviruses, provide an effective means of delivering therapeutic genes into MSCs, allowing the expression of a wide range of therapeutic agents, including anti-angiogenic proteins. This review explores the frontier of using genetically engineered MSCs delivered through viral vectors as a potent anti-angiogenic therapeutic approach. By leveraging the unique properties of MSCs and the targeted delivery capabilities of viral vectors, this approach initiates the potential to revolutionize anti-angiogenic therapy, offering new possibilities for the treatment of angiogenesis-related diseases.

Multimodal robotic music performance art based on GRU-GoogLeNet model fusing audiovisual perception.

The field of multimodal robotic musical performing arts has garnered significant interest due to its innovative potential. Conventional robots face limitations in understanding emotions and artistic expression in musical performances. Therefore, this paper explores the application of multimodal robots that integrate visual and auditory perception to enhance the quality and artistic expression in music performance. Our approach involves integrating GRU (Gated Recurrent Unit) and GoogLeNet models for sentiment analysis. The GRU model processes audio data and captures the temporal dynamics of musical elements, including long-term dependencies, to extract emotional information. The GoogLeNet model excels in image processing, extracting complex visual details and aesthetic features. This synergy deepens the understanding of musical and visual elements, aiming to produce more emotionally resonant and interactive robot performances. Experimental results demonstrate the effectiveness of our approach, showing significant improvements in music performance by multimodal robots. These robots, equipped with our method, deliver high-quality, artistic performances that effectively evoke emotional engagement from the audience. Multimodal robots that merge audio-visual perception in music performance enrich the art form and offer diverse human-machine interactions. This research demonstrates the potential of multimodal robots in music performance, promoting the integration of technology and art. It opens new realms in performing arts and human-robot interactions, offering a unique and innovative experience. Our findings provide valuable insights for the development of multimodal robots in the performing arts sector.

Tilapia-Derived Granular Hydrogel as a 3D Scaffold Promoting Rapid Wound Healing.

The skin, a crucial organ that protects the body, is vulnerable to external damage. Traditional tissue regeneration methods, including bulk hydrogels, aim to facilitate wound healing by interacting with host cells and providing a conducive environment. However, the nanoscale porosity of conventional hydrogels limits cell penetration and tissue regeneration. To overcome this, hydrogels composed of microgels have emerged as promising alternatives. In this study, we propose a granular hydrogel using decellularized tilapia skin. The tilapia skin-based microgels are cost-effective, immune-friendly, and have a high collagen content. Microgels based on the decellularized extracellular matrix of tilapia were successfully fabricated by using microfluidics. Through the assembly of these microgels using adhesive hyaluronic acid-catechol, the resulting 3D granular hydrogel scaffold facilitated enhanced cell growth, accelerated cell differentiation, and successful healing of full-thickness wounds in a mouse model. This study reveals the potential of tilapia skin-based granular hydrogel assembly in wound healing, overcoming conventional hydrogel limits.

Power matching based current limitation method for grid forming converter under large disturbances

Grid forming (GFM) converter mimics the operational characteristics of the synchronous generator, serves as a voltage source that provides voltage and frequency support to the power system. However, due to its voltage source nature, the current of the GFM converter is highly determined by the external system, increasing the risk of overcurrent under large disturbances and potential damage to the converter. The conventional current reference limitation suffers from synchronization instability, which causes the converter to lose synchronization. To address this issue, this paper proposes a novel power matching based current limitation method, which maintains the synchronization of the GFM converter and also avoids overcurrent issues. Specifically, when a large disturbance occurs, considering the maximum output power of the GFM converter, the reference power of the converter is corrected using the frequency to match the output power in real time. Therefore, the output power of the converter is always its maximum value, with the current constrained to the maximum level allowed, avoiding overcurrent and instability. Finally, the superiority of the proposed method is verified by experiment and simulation.

Virtual development of competitive products made of customized ceramic composites

Ceramic matrix composites (CMCs) are commonly employed in situations where conventional materials face limitations or when they could provide substantial performance enhancements. However, when the latter condition is met, the increased performance must offset the higher costs associated with development and manufacturing. In this study, a methodological approach is introduced to concurrently reduce costs and optimize performance via virtual product development and tailored material customization. The effectiveness of this approach is demonstrated using an antenna strut in the context of space applications as an illustrative example.Moreover, strategies are proposed for the integration of pre-existing simulation models. Relevant diagrams are provided for CMC customization, alongside the introduction of a cost model based on the cost-performance ratio in contrast to a reference product. As a result, the study significantly enhances the competitiveness of CMC components compared to conventional materials, ultimately expanding their market potential.

GraphMoCo: A graph momentum contrast model for large-scale binary function representation learning

In the field of cybersecurity, the ability to compute similarity scores at the function level for binary code is of utmost importance. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale Binary Code Similarity Detection (BCSD). To overcome the aforementioned problems, we propose GraphMoCo: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. We adopt an unsupervised learning strategy. Our approach eliminates the need for manual labeling. By leveraging the intrinsic structural information at multiple levels of the binary code, our model could achieve higher accuracy with a simple CNN-based model. By introducing the preshuffle mechanism, the issue of information leakage in graph momentum update algorithm is mitigated. The evaluation results indicate that GraphMoCo exhibits superior performance compared to SOTA approaches in the function pair search task, showing an average improvement of 7% on AUC, and 10% on MRR and Recall@1. Furthermore, GraphMoCo achieves a MAP of 0.93 on the more challenging dataset 2, which comprises a larger function pool. In a real-world scenario, specifically in known vulnerability searching, GraphMoCo achieves a MRR that surpasses existing SOTA models by 5%.

The critical role of in-situ lithium fluoride in gel polymer electrolyte for high-performance rechargeable batteries

Lithium-ion (Li-ion) batteries are continuously being developed to enhance safety and energy density. One promising approach involves combining high nickel cathode material (NCM811) with Gel Polymer Electrolyte (GPE). However, achieving compatibility between these components remains a challenge. In this study, we investigate the use of Ethyl difluoroacetate (DFEA) to enhance the mechanical stability, ionic conductivity, and heat-resisting of polyethylene glycol dimethacrylate (PEGDMA) based GPE, thereby improving battery performance. The modified GPE/DFEA exhibits a smoother surface, increased Young's modulus (2.2 GPa), and high ionic conductivity (7.10 mS cm−1). Additionally, it enables the in-situ formation of a LiF-rich interface film on the electrode/electrolyte surfaces. Moreover, the Li||Li symmetrical cell with GPE/DFEA demonstrates stable cycling for 500 h without dendrite growth. Furthermore, Li|Cu asymmetrical cells using GPE/DFEA achieve an average Coulombic efficiency (CE) of 99.0% over 200 cycles at 1 mA cm−2, compared to only 62.2% for liquid electrolyte (LE). Importantly, the GPE/DFEA allows for operation beyond the conventional voltage limit of 4.25 V, reaching 4.38 V. It is successfully implemented in an Ah-level NCM811/graphite pouch cell, resulting in an energy density of 250 Wh/kg and improved cycle stability at 45 °C compared to standard electrolytes. Moreover, the GPE/DFEA-based batteries exhibit enhanced safety performance under conditions such as needle puncture and overcharge. The developed GPE can meet the practical requirements for battery applications.

Strain modulation in crumpled Si nanomembranes: Light detection beyond the Si absorption limit.

Although Si is extensively used in micro-nano electronics, its inherent optical absorption cutoff at 1100-nm limits its photonic and optoelectronic applications in visible to partly near infrared (NIR) spectral range. Recently, strain engineering has emerged as a promising approach for extending device functionality via tuning the material properties, including change in optical bandgap. In this study, the reduction in bandgap with applied strain was used for extending the absorption limit of crystalline Si up to 1310 nm beyond its intrinsic bandgap, which was achieved by creating the crumpled structures in Si nanomembranes (NMs). The concept was used to develop a prototype NIR image sensor by organizing metal-semiconductor-metal-configured crumpled Si NM photosensing pixels in 6 × 6 array. The geometry-controlled, self-sustained strain induction in Si NMs provided an exclusive photon management with shortening of optical bandgap and enhanced photoresponse beyond the conventional Si absorption limit.

Quantum velocity limits for multiple observables: Conservation laws, correlations, and macroscopic systems

How multiple observables mutually influence their dynamics has been a crucial issue in statistical mechanics. We here introduce a new concept, “quantum velocity limits,” to establish a quantitative and rigorous theory for nonequilibrium quantum dynamics for multiple observables. Quantum velocity limits are universal inequalities for a vector that describes velocities of multiple observables. They elucidate that the speed of an observable of our interest can be tighter bounded when we have knowledge of other observables, such as experimentally accessible ones or conserved quantities, compared with conventional speed limits for a single observable. Moreover, quantum velocity limits are conceptually distinct from the conventional speed limits because we need to introduce the velocity vector and solve an optimization problem for multiple variables to obtain them. We first derive an information-theoretical velocity limit in terms of the generalized correlation matrix of the observables and the quantum Fisher information. The velocity limit has various novel consequences: (i) conservation law in the system, a fundamental ingredient of quantum dynamics, can improve the velocity limits through the correlation between the observables and conserved quantities; (ii) speed of an observable can be bounded by a nontrivial lower bound from the information on another observable, while most of the previous speed limits provide only upper bounds; (iii) there exists a notable nonequilibrium tradeoff relation, stating that speeds of uncorrelated observables, e.g., anticommuting observables, cannot be simultaneously large; (iv) velocity limits for local observables in locally interacting many-body systems are described by the fluctuation of a local Hamiltonian, which is convergent even in the thermodynamic limit, with a nontrivial finite-size correction. Moreover, we discover another distinct velocity limit for multiple observables on the basis of the local conservation law of probability current, which becomes advantageous for macroscopic transitions of multiple quantities. Our newly found velocity limits ubiquitously apply not only to unitary quantum dynamics but to classical and quantum stochastic dynamics, offering a key step towards universal theory of far-from-equilibrium dynamics for multiple observables. Published by the American Physical Society 2024

How to establish the relationship between wetting parameters and the mechanical properties of a flexible thin film

Conventional testing methods face limitations in identifying the mechanical properties of flexible film coatings. In this study, the mechanical properties of the polydimethylsiloxane (PDMS) flexible film coating were obtained by analyzing the dynamic wetting process of droplets on the surface. Results showed that the variation of contact angle can be divided into three regions during wetting, and the deformation height of the PDMS film increased rapidly and then decreased. Furthermore, a new model including interfacial tensions and the elastic force was proposed to establish the relationship among contact angle, deformation height and elastic modulus of the coating. The elastic modulus of the coating obtained from this model had an error of less than 10 % compared to the actual value.