Current Limitations Research Articles

Acoustic vortex-based dynamic lens for light focusing and steering.

This study explores a technique for light manipulation using an acoustic vortex generated by a high-intensity focused ultrasound transducer. The acoustic vortex forms a ring of bubble wall near the high-pressure region, creating a lens-like structure that can effectively focus a laser beam. The effects of varying acoustic pressures and dissolved oxygen content on the focused ultrasound and vortex waveforms were tested. Results showed that the vortex waveform could enhance the laser beam peak intensity by 55.6% and reduce its full width at half maximum from 1.16 mm to 0.91 mm. Additionally, the study demonstrated the capability to dynamically steer the laser beam at angles ranging from 0° to 0.7°, achieving precise control without the need for mechanical components. This technique offers a stable, real-time, and on-demand method for light manipulation, with potential applications in various liquid environments and heterogeneous media. The study also highlights current hardware limitations and suggests future improvements for optimizing parameters and further exploring related mechanisms.

Advanced biomaterials for diabetes healthcare and complication therapy: A review

With the rapid increase in the number of patients with diabetes, exploring more effective and convenient methods to lower blood sugar levels is becoming increasingly important. Biomaterials are a potential solution in this field, owing to their unique customizability and biocompatibility. These materials can be used in smart drug delivery systems to achieve precise control of insulin release, or as islet cell encapsulation materials to achieve effective transplantation of islet cells. Among these, new responsive biomaterials can automatically adjust the release of insulin according to real-time changes in blood sugar levels, thereby enabling personalized and automated treatment. In addition, biomaterials are used to develop noninvasive blood glucose monitoring technologies to further simplify diabetes management. Although these applications are still in the research or early pilot stage, their potential to improve diabetes treatment and the quality of life of patients is already evident. In this Review, we discuss the current progress, limitations, and potential of biomaterials for the treatment of diabetes and its complications.

A translational murine model of aseptic loosening with osseointegration failure.

An in vivo animal model of a weight-bearing intra-articular implant is crucial to the study of implant osseointegration and aseptic loosening caused by osseointegration failure. Osseointegration, defined as a direct structural and functional attachment between living bone tissue and the surface of a load-carrying implant, is essential for implant stability and considered a prerequisite for the long-term clinical success of implants in total joint arthroplasty. Compared to large animal models, murine models offer extensive genetic tools for tracing cell differentiation and proliferation. The 18- to 22-week-old C57BL/6J background mice underwent either press-fitted or loose implantation of a titanium implant, achieving osseointegration or fibrous integration. A protocol was developed for both versions of the procedure, including a description of the relevant anatomy. Samples were subjected to microcomputed tomography and underwent biomechanical testing to access osseointegration. Lastly, samples were fixed and embedded for histological evaluation. The absence of mineralized tissue and weakened maximum pull-out force in loose implantation samples indicated that these implants were less mechanically stable compared to the control at 4 weeks postoperation. Histological analysis demonstrated extensive fibrotic tissue in the peri-implant area of loose implantation samples and excellent implant osseointegration in press-fitted samples at 4 weeks. Both mechanically stable and unstable hemiarthroplasty models with either osseous ingrowth or a robust periprosthetic fibrosis were achieved in mice. We hope that this model can help address current limitations for in vivo study of aseptic loosening and lead to necessary translational benefits.

INTOMO operator for GNSS multi-source tomography based on 3D ray tracing technique

AbstractThe current GNSS meteorology literature focuses on ground-based and space-based GNSS observations separately, without exploring potential synergies. In this study, we propose combining the two data sources using GNSS tomography to overcome current limitations in (1) horizontal resolution of GNSS space-based, (2) low vertical resolution of GNSS ground-based tropospheric retrievals when the number of GNSS ground-based observations is limited and (3) instability of the tomography system due to a lack of observations traversing the atmosphere horizontally. Our study on the combination of GNSS ground-based and space-based presents an innovative way for data integration based on uncertainty estimation. The developed integrated tomography operator, based on 3D ray tracing principles, is tested on 30 days of simulated data with 101 ground stations and over 240 radio occultation events, using three different station layouts. The a priori data introduced into the tomography processing is from a deterministic model, while ray tracing uses the ERA5 reanalysis wet refractivity field to obtain input data for individual test cases. The results are verified by comparing tomography output to ERA5 reanalysis. We observed a decrease in tomography RMSE between 2% and 16% in the case of an integrated solution, depending on GNSS station layout and the number and geometry of radio occultation ray paths. We show that a single RO event during one processing epoch can shift the wet refractivity estimates by 2 to 5 ppm closer to the correct solution compared to ground-based-only GNSS tomography.

The Chinese magneto-biased superconducting current limiter has achieved long-term grid-connected demonstration, withstanding transmission line fault

The Chinese magneto-biased superconducting current limiter has achieved long-term grid-connected demonstration, withstanding transmission line fault

Synthetic data generation with hybrid quantum-classical models for the financial sector

Abstract Data integrity and privacy are critical concerns in the financial sector. Traditional methods of data collection face challenges due to privacy regulations and time-consuming anonymization processes. In collaboration with Banco BV, we trained a hybrid quantum-classical generative adversarial network (HQGAN), where a quantum circuit serves as the generator and a classical neural network acts as the discriminator, to generate synthetic financial data efficiently and securely. We compared our proposed HQGAN model with a fully classical GAN by evaluating loss convergence and the MSE distance between the synthetic and real data. Although initially promising, our evaluation revealed that HQGAN failed to achieve the necessary accuracy to understand the intricate patterns in financial data. This outcome underscores the current limitations of quantum-inspired methods in handling the complexities of financial datasets. Graphical abstract

Manganese-doped liquid metal nanoplatforms for cellular uptake and glutathione depletion-enhanced photothermal and chemodynamic combination tumor therapy

Chemodynamic therapy (CDT) involves the catalysis of in situ overexpressed hydrogen peroxide (H2O2) into highly toxic reactive oxygen species (ROS) to treat tumors. However, the efficacy of CDT is greatly hampered by limited cellular internalization efficiency, ROS scavenging by glutathione (GSH), and slow reaction rate. To overcome the current limitations of CDT, a manganese-doped and polyethylene glycol (PEG)-modified liquid metal (LM)-silica nanoplatform (labeled as Mn-LMOP) with varying stiffness is constructed to achieve synergistic photothermal therapy (PTT) and CDT, which can further induce immunogenic cell death (ICD) in tumors to enhance the anti-tumour effects. Significantly, benefiting from the increased stiffness, the Mn-LMOP nanoparticles (NPs) can enhance cellular uptake and lysosomal escape, and gradually accumulate in tumor sites. Moreover, manganese-doped NPs exhibite good photothermal effects and can rapidly reacte with intratumoral GSH to produce Mn2+, inhibiting GSH-mediated ROS clearance and promoting the efficiency of CDT. This combined treatment strategy can activate the immune response of the tumors, which holds the promise of photothermal/chemodynamic/immune multimodal therapeutic effects. This LM-based nanosystem will provide a paradigm for enhanced CDT/PTT combination anti-tumour efficacy. Statement of significanceChemodynamic therapy (CDT) is a promising drug-free treatment approach characterized by its low invasiveness and minimal side effect. However, CDT encounters challenges such as high levels of glutathione (GSH), low Fenton-like reaction rate, and inefficient cellular uptake in tumor tissues. Here, a manganese-doped liquid metal (LM) nanomaterial was designed to achieve synergistic photothermal therapy (PTT) and CDT. This innovative strategy enhanced cellular uptake by adjusting the mechanical property of nanoparticles (NPs) and facilitated the consumption of GSH, while simultaneously accelerating the Fenton-like reaction rate with the assistance of PTT-mediated hyperthermia. This combined CDT/PTT strategy also activated the immune response within the tumor, demonstrating significant therapeutic potential.

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites.

In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallography and cryo-electron microscopy have been used to unravel these structures, but they are often challenging, time-consuming and costly. Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. Nature 2021, 596, 583. Lane, T. J. Nature Methods 2023, 20, 170. Kryshtafovych, A., et al. Proteins: Structure, Function and Bioinformatics 2021, 89, 1607). This study focuses on predicting the dynamic changes that proteins undergo upon ligand binding, specifically when they bind to allosteric sites, i.e. a pocket different from the active site. Allosteric modulators are particularly important for drug discovery, as they open new avenues for designing drugs that can target proteins more effectively and with fewer side effects (Nussinov, R.; Tsai, C. J. Cell 2013, 153, 293). To study this, we curated a data set of 578 X-ray structures comprised of proteins displaying orthosteric and allosteric binding as well as a general framework to evaluate deep learning-based structure prediction methods. Our findings demonstrate the potential and current limitations of deep learning methods, such as AlphaFold2 (Jumper, J., et al. Nature 2021, 596, 583), NeuralPLexer (Qiao, Z., et al. Nat Mach Intell 2024, 6, 195), and RoseTTAFold All-Atom (Krishna, R., et al. Science 2024, 384, eadl2528) to predict not just static protein structures but also the dynamic conformational changes. Herein we show that predicting the allosteric induce-fit conformation still poses a challenge to deep learning methods as they more accurately predict the orthosteric bound conformation compared to the allosteric induce fit conformation. For AlphaFold2, we observed that conformational diversity, and sampling between the apo and holo state could be increased by modifying the MSA depth, but this did not enhance the ability to generate conformations close to the allosteric induced-fit conformation. To further support advancements in protein structure prediction field, the curated data set and evaluation framework are made publicly available.

GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies

Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

Study on Coordination of Different Types of DC Superconducting Current Limiter and DC Circuit Breaker for Electric Propulsion Aircraft

Study on Coordination of Different Types of DC Superconducting Current Limiter and DC Circuit Breaker for Electric Propulsion Aircraft

Electromagnetic Thermal Analysis on Variable Resistive-Type Superconducting Fault Current Limiter

Electromagnetic Thermal Analysis on Variable Resistive-Type Superconducting Fault Current Limiter

Impacts of Superconducting Fault Current Limiters on Undervoltage Protection for the Distribution Network

Impacts of Superconducting Fault Current Limiters on Undervoltage Protection for the Distribution Network

Recent advances in the application of artificial intelligence in age-related macular degeneration

Recent advancements in ophthalmology have been driven by the incorporation of artificial intelligence (AI), especially in diagnosing, monitoring treatment and predicting outcomes for age-related macular degeneration (AMD). AMD is a leading cause of irreversible vision loss worldwide, and its increasing prevalence among the ageing population presents a significant challenge for managing the disease. AI holds considerable promise in tackling this issue. This paper provides an overview of the latest developments in AI applications for AMD. However, current limitations include insufficient and unbalanced data, lack of interpretability in models, dependence on data quality and limited generality.

On the Kostant-Souriau prequantization of scalar fields with polysymplectic structures

In this paper, I present a novel, purely differential geometric approach to the quantization of scalar fields, with a special focus on the familiar case of Minkowski spacetimes. This approach is based on using the natural geometric structures of polysymplectic Hamiltonian field theory to produce an analog of the Kostant-Souriau prequantization map familiar from geometric quantization. I show that while the resulting operators are quite different from those of canonical quantum field theory, the approach is nonetheless able to reproduce a few of canonical quantum field theory's most fundamental results. I finish by elaborating the current limitations of this approach and briefly discussing future prospects.

Toward separation and purification of chlorogenic acid from Lonicerae Japonicae Flo (honeysuckle) using melamine–formaldehyde aerogel: A green and efficient approach

The separation and purification of chlorogenic acid (CA) from honeysuckle is a challenging yet significant task in the food industry. Traditional methods, such as precipitation and chromatography methods, often suffer from secondary pollution or low efficiency. This paper proposes a green and efficient adsorption method for separating CA from honeysuckle using melamine–formaldehyde aerogel (MFa) nanomaterial. The synthesis of MFa adsorbent with developed mesoporous material is crucial for achieving high efficiency. The optimal conditions for CA adsorption were determined to be an MFa dose of 0.03 g, a pH value of 3.72, and an adsorption temperature of 18.89 ℃. Under these conditions, a CA adsorption capacity of 151.84 mg/g and an adsorption ratio of 69.58 % were achieved, demonstrating the superior performance of the MFa method. Most notably, the purity of CA improved significantly from 10.50 % to 60.50 %, a marked enhancement improvement over traditional sorption resins. Additionally, the superior antioxidant effect of CA treated with the MFa method was confirmed at both the chemical assay and cytological levels. Affinity analysis and MD simulations revealed that CA adsorption is primarily driven by electrostatic interaction. This study not only addresses current limitations of honeysuckle but also broadens the application pontential of aerogel in food and medicine industries.

Device Applications Enabled by Bandgap Engineering Through Quantum Dot Tuning: A Review.

Quantum dots (QDs) are becoming essential materials for future scientific and real-world applications, owing to their interesting and distinct optical and electrical properties compared to their bulk-state counterparts. The ability to tune the bandgap of QDs based on size and composition-a key characteristic-opens up new possibilities for enhancing the performance of various optoelectronic devices. These advances could extend to cutting-edge applications such as ultrawide-band or dual-band photodetectors (PDs), optoelectronic logic gates, neuromorphic devices, and security functions. This paper revisits the recent progress in QD-embedded optoelectronic applications, focusing on bandgap tunability. The current limitations and challenges in advancing and realizing QD-based optoelectronic devices are also discussed.

Unlocking the Rhythmic Power of Bacterial Cellulose: A Comprehensive Review on Green Energy Harvesting and Sustainable Applications

AbstractBacterial cellulose is a biodegradable and ecologically safe material that has the potential to convert mechanical vibrations into electrical energy. This review introduces green energy harvesting, a novel concept that harnesses natural processes to provide sustainable energy. A thorough overview of bacterial cellulose, covering its distinctive features, its biological origin, and its energy conversion process, is fully presented. The different materials and methods used to design and fabricate bacterial cellulose‐based energy harvesters are explored. Moreover, the various applications and benefits of these devices in the context of renewable energy are examined. The current challenges and limitations of this emerging field are identified and the possible avenues for future research are suggested. The significance of adopting eco‐friendly approaches in achieving a balance between human needs and environmental preservation is highlighted. By providing a comprehensive and critical assessment of bacterial cellulose as a green energy harvester, this review aims to motivate researchers, engineers, and policymakers to tap into the rhythmic potential of this natural material in building a more sustainable and resilient future.

Autocrine Motility Factor and Its Peptide Derivative Inhibit Triple-Negative Breast Cancer by Regulating Wound Repair, Survival, and Drug Efflux.

Triple-negative breast cancer (TNBC) presents a significant challenge in oncology due to its aggressive nature and limited targeted therapeutic options. This study explores the potential of autocrine motility factor (AMF) and an AMF-derived peptide as novel treatments for TNBC. AMF, primarily secreted by neoplastic cells, plays a crucial role in cancer cell motility, metastasis, and proliferation. The research demonstrates that AMF and its derived peptide inhibit TNBC cell proliferation by modulating cellular migration, redox homeostasis, apoptotic pathways, and drug efflux mechanisms. Dose-dependent antiproliferative effects were observed across three TNBC cell lines, with higher concentrations impairing cellular migration. Mechanistic studies revealed decreased glucose-6-phosphate dehydrogenase expression and elevated reactive oxygen species production, suggesting redox imbalance as a primary mediator of apoptosis. Combination studies with conventional therapeutics showed near-complete eradication of resistant TNBC cells. The observed reduction in p53 levels and increased intranuclear doxorubicin accumulation highlight the AMF/AMF peptide's potential as multidrug resistance modulators. This study underscores the promise of using AMF/AMF peptide as a novel therapeutic approach for TNBC, addressing current treatment limitations and warranting further investigation.

Enhanced Detection of Gastrointestinal Malignancies using Machine Learning-Optimized Liquid Biopsy: A Mini Review.

Gastrointestinal (GI) cancers represent some of the most common and lethal malignancies globally, underscoring the urgent need for improved diagnostic strategies. Traditional diagnostic methods, while effective to some degree, are often invasive and unsuit-able for regular screenings. This review article explores integrating machine learning (ML) with liquid biopsy techniques as a revolutionary approach to enhance the detection and monitoring of GI cancers. Liquid biopsies offer a non-invasive alternative for cancer detection through the analysis of circulating tumor DNA (ctDNA) and other biomarkers, which when combined with ML, can significantly improve diagnostic accuracy and patient outcomes. We conducted a comprehensive review of recent advancements in liquid biopsy and ML, focusing on their synergistic potential in the early detection of GI cancers. The review addresses the application of next-generation sequencing and digital droplet PCR in enhancing the sensitivity and specificity of liquid biopsies. Machine learning algorithms have demonstrated remarkable ability in navigating complex datasets and identifying diagnostically significant patterns in ctDNA and other circu-lating biomarkers. Innovations such as machine learning-enhanced "fragmentomics" and tomographic phase imaging flow cytometry illustrate significant strides in non-invasive cancer diagnostics, offering enhanced detection capabilities with high accuracy. The integration of ML in liquid biopsy represents a transformative step in the early detection and personalized treatment of GI cancers. Future research should focus on overcoming current limitations, such as the heterogeneity of tumor-derived genetic materials and the standardization of liquid biopsy protocols, to fully realize the potential of this technol-ogy in clinical settings.

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.