Current Limitations Research Articles

Near-field strong coupling and entanglement of quantum emitters for room-temperature quantum technologies

In recent years, quantum nanophotonics has forged a rich nexus of nanotechnology with photonic quantum information processing, offering remarkable prospects for advancing quantum technologies beyond their current technical limits in terms of physical compactness, energy efficiency, operation speed, temperature robustness and scalability. In this perspective, we highlight a number of recent studies that reveal the especially compelling potential of nanoplasmonic cavity quantum electrodynamics for driving quantum technologies down to nanoscale spatial and ultrafast temporal regimes, whilst elevating them to ambient temperatures. Our perspective encompasses innovative proposals for quantum plasmonic biosensing, driving ultrafast single-photon emission and achieving near-field multipartite entanglement in the strong coupling regime, with a notable emphasis on the use of industry-grade devices. We conclude with an outlook emphasizing how the bespoke characteristics and functionalities of plasmonic devices are shaping contemporary research directives in ultrafast and room-temperature quantum nanotechnologies.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Exploring AES Encryption Implementation Through Quantum Computing Techniques

A coming great revolution in technology is quantum computing, which opens new attacks on most of the developed cryptographic algorithms, including AES. These emerging quantum capabilities risk weakening cryptographic techniques, which safeguard a vast amount of data across the globe. This research uses Grover's algorithm to explore the vulnerabilities of the Advanced Encryption Standard to quantum attacks. By implementing quantum cryptographic algorithms and Quantum Error Correction on simulators and quantum hardware, the study evaluates the effectiveness of these techniques in mitigating noise and improving the reliability of quantum computations. The study shows that while AES is theoretically at risk due to Grover’s algorithm, which demonstrates a theoretical reduction in AES key search complexity, current hardware limitations and noise levels encountered in today’s quantum computers reduce the immediate threat and limit practical exploitation. The research also examines NTRU encryption, a quantum-resistant alternative, highlighting its robustness in quantum environments. The findings emphasize the need for further development in QEC and quantum-resistant cryptography to secure digital communications against future quantum threats. Future work will focus on advancing QEC techniques and refining quantum algorithms, addressing both hardware and theoretical advancements, including the potential use of high-capacity processors like Jiuzhang 3.0. These improvements will ensure the scalability of quantum-resistant systems to practical key sizes and usage scenarios.

Molecules in Motion: Unravelling the Dynamics of Vascularization Control in Tissue Engineering.

Significant progress has been made in tissue engineering (TE), aiming at providing personalized solutions and overcoming the current limitations of traditional tissue and organ transplantation. 3D bioprinting has emerged as a transformative technology in the field, able to mimic key properties of the natural architecture of the native tissues. However, most successes in the area are still limited to avascular or thin tissues due to the difficulties in controlling the vascularization of the engineered tissues. To address this issue, several molecules, biomaterials, and cells with pro- and anti-angiogenic potential have been intensively investigated. Furthermore, different bioreactors capable to provide a dynamic environment for in vitro vascularization control have been also explored. The present review summarizes the main molecules and TE strategies used to promote and inhibit vascularization in TE, as well as the techniques used to deliver them. Additionally, it also discusses the current challenges in 3D bioprinting and in tissue maturation to control in vitro/in vivo vascularization. Currently, this field of investigation is of utmost importance and may open doors for the design and development of more precise and controlled vascularization strategies in TE.

Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, this study demonstrates how it can be adapted for congested conditions, with the objective of improving the accuracy of traffic load models. To overcome the limitation of traditional Weigh-in-Motion (WIM) systems in capturing congested traffic, congested flow characteristics were inferred from available free-flow data. The cellular automata (CA) method was applied to generate realistic congested traffic scenarios, which were used as input for the probabilistic model. Key simulation parameters, such as cell length and vehicle weight distribution, were adjusted to reflect congested conditions. The results validate the model’s flexibility, showing how, with the adaptation of some parameters, it can simulate both free-flow and congested traffic patterns effectively. This research provides a basis for improving traffic load models used in the design and assessment of long-span bridges, addressing the current limitations in existing codes and standards.

Innovative Materials for Lamination Encapsulation in Perovskite Solar Cells

AbstractPerovskite solar cells (PSCs), as the forefront of third‐generation solar technology, are distinguished by their cost‐effectiveness, high photovoltaic efficiency, and the flexibility of their bandgap tunability, positioning them as formidable contenders in the photovoltaic market. However, the stability of PSCs remains a significant barrier to their widespread commercialization. Lamination encapsulation is identified as a pivotal intervention to enhance the durability of PSCs under external environmental stress. This review initiates with an in‐depth exploration of the degradation phenomena in PSCs, triggered by environmental stressors such as water, oxygen, light, and heat. This analysis lays bare the degradation mechanisms, thereby highlighting the specific demands for effective encapsulation materials. Subsequently, the review presents a systematic discourse on the latest developments in encapsulation materials, dissecting their molecular structures and linking these to their physical properties and performance in encapsulation applications. The narrative concludes by charting potential future research pathways intended to refine and enhance the encapsulation performance of PSCs, offering several routes for overcoming current limitations and propelling PSCs toward their full commercial potential.

Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends.

Artificial intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small-cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical record (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal the potential to improve surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and perioperative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

Enantioselective photochemical reactions within the confined cavities of supramolecular assemblies

Compared to bulk solvents, reactions in the confined spaces of supramolecular self-assemblies feature rate acceleration, high efficiency and substrate selectivity. These advantages lead to efficient catalytic efficiency and excellent selectivity in enantioselective supramolecular photochemical transformations. During the last few years, enantioselective supramolecular photocatalysis has developed into one of the most powerful strategies to construct enantioenriched chiral compounds. In this review, the recent advances of enantioselective photochemical reactions taking place within the confined spaces of supramolecular assemblies are summarized, with an emphasis on the specific catalytic modes and chemical transformations. Organization of the data follows a subdivision according to supramolecular host and reaction type. At last, the current limitations and the future research orientation of this research field are discussed.

Investigation on Understanding the Numeracy Capacity of Intellectual Disabled Students using Enabling Technology Tools: Web Application, AR and UI/UX

The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To achieve this, we conducted semi-structured interviews and surveys with teachers working with students with ID, gaining insights into the current solutions, advantages, and limitations. Additionally, we used physical props as design probes in a co-design methodology to better understand and elicit the true needs of individuals with ID. The findings from this research will inform the development of a 2D/AR tool designed to make learning mathematics more engaging and effective for individuals with ID, contributing to the advancement of inclusive education practices. Enabling Technology plays a significant role in the numeracy ability among people with ID. Generative AI and Explainable AI shall further improve learning ability in the years to come.

Oncolytic Viruses as Reliable Adjuvants in CAR-T Cell Therapy for Solid Tumors

Although impactful scientific advancements have recently been made in cancer therapy, there remains an opportunity for future improvements. Immunotherapy is perhaps one of the most cutting-edge categories of therapies demonstrating potential in the clinical setting. Genetically engineered T cells express chimeric antigen receptors (CARs), which can detect signals expressed by the molecules present on the surface of cancer cells, also called tumor-associated antigens (TAAs). Their effectiveness has been extensively demonstrated in hematological cancers; therefore, these results can establish the groundwork for their applications on a wide range of requirements. However, the application of CAR-T cell technology for solid tumors has several challenges, such as the existence of an immune-suppressing tumor microenvironment and/or inadequate tumor infiltration. Consequently, combining therapies such as CAR-T cell technology with other approaches has been proposed. The effectiveness of combining CAR-T cell with oncolytic virus therapy, with either genetically altered or naturally occurring viruses, to target tumor cells is currently under investigation, with several clinical trials being conducted. This narrative review summarizes the current advancements, opportunities, benefits, and limitations in using each therapy alone and their combination. The use of oncolytic viruses offers an opportunity to address the existing challenges of CAR-T cell therapy, which appear in the process of trying to overcome solid tumors, through the combination of their strengths. Additionally, utilizing oncolytic viruses allows researchers to modify the virus, thus enabling the targeted delivery of specific therapeutic agents within the tumor environment. This, in turn, can potentially enhance the cytotoxic effect and therapeutic potential of CAR-T cell technology on solid malignancies, with impactful results in the clinical setting.

Metagenomic discovery of microbial eukaryotes in stool microbiomes.

Host-associated microbiota form complex microbial communities that are increasingly associated with host behavior and disease. While these microbes include bacterial, archaeal, viral, and eukaryotic constituents, most studies have focused on bacteria due to their dominance in the human host and available tools for investigation. Accumulating evidence suggests microbial eukaryotes in the microbiome play pivotal roles in host health, but our understandings of these interactions are limited to a few readily identifiable taxa because of technical limitations in unbiased eukaryote exploration. Here, we combined cell sorting, optimized eukaryotic cell lysis, and shotgun sequencing to accelerate metagenomic discovery and analysis of host-associated microbial eukaryotes. Using synthetic communities with a 1% microbial eukaryote representation, the eukaryote-optimized cell lysis and DNA recovery method alone yielded a 38-fold increase in eukaryotic DNA. Automated sorting of eukaryotic cells from stool samples of healthy adults increased the number of microbial eukaryote reads in metagenomic pools by up to 28-fold compared to commercial kits. Read frequencies for identified fungi increased by 10,000× on average compared to the Human Microbiome Project and allowed for the identification of novel taxa, de novo assembly of contigs from previously unknown microbial eukaryotes, and gene prediction from recovered genomic segments. These advances pave the way for the unbiased inclusion of microbial eukaryotes in deciphering determinants of health and disease in the host-associated microbiome.IMPORTANCEMicrobial eukaryotes are common constituents of the human gut where they can contribute to local ecology and host health, but they are often overlooked in microbiome studies. The lack of attention is due to current technical limitations that are heavily biased or poorly recovered DNA from microbial eukaryotes. We developed a method to increase the representation of these eukaryotes in metagenomic sequencing of microbiome samples that allows to improve their detection compared to prior methods and allows for the identification of new species. Application of the technique to gut microbiome samples improved detection of fungi, protists, and helminths. New eukaryotic taxa and their encoded genes could be identified by sequencing a small number of samples. This approach can improve the inclusion of eukaryotes into microbiome research.

The utility of ChatGPT in gender-affirming mastectomy education

IntroductionThe integration of AI such as ChatGPT in medicine has been showing promise in enhancing patient education. Gender-affirming mastectomy (GAM) is a surgical procedure designed to help individuals transition to their self-identified gender, playing a crucial role in mitigating psychological distress for many transmasculine and non-binary (TNB) patients. With increased demand and attention towards GAM, plastic and reconstructive surgeons may rely on AI-driven chatbots as an accessible, accurate, and patient-driven model for relevant details on this procedure. Specific Aim(s)This study aimed to assess the quality and readability of information provided by ChatGPT in response to frequently asked questions (FAQs) related to GAM. MethodsInspired by online forums and physician websites, 10 frequently asked questions (FAQs) about pre- and postoperative topics were submitted to ChatGPT and assessed using validated readability score measures and expert interpretation. ResultsThe average readability score was 16.0 ± 1, indicating a college or graduate reading level. Mean accuracy, comprehensiveness, and danger scores were 8.8 ± 0.5, 7.8 ± 0.7, and 2.2 ± 0.4, respectively. Although physicians appreciated ChatGPT's tone, patient autonomy, and advice to seek professional medical and mental help, they also cited instance of generic information, misinformation, support of debated techniques, and pathologization of gender dysphoria. ConclusionEven with its promise in providing accurate and comprehensive information on GAM, ChatGPT’s current limitations suggest caution as a supplementary tool to physician consultation.

Scanless Spectral Imaging of Terahertz Vortex Beams Generated by High‐Resolution 3D‐Printed Spiral Phase Plates

Terahertz technology has experienced significant advances in the past years, leading to new applications in the fields of spectroscopy, imaging, and communications. This progress requires the development of dedicated optics to effectively direct, control and manipulate terahertz radiation. In this regard, 3D printing technologies have shown great potential, offering fast prototyping, high design flexibility, and good reproducibility. While traditional 3D printing techniques allow for the preparation of terahertz optical components operating at relatively low frequencies (<0.4 THz) due to their limited resolution, two‐photon polymerization lithography (TPL) exhibits high detail resolution and low surface roughness and can thus potentially enable the fabrication of high‐frequency terahertz devices. Here, as a proof of principle, spiral phase plates operating at 1 THz are designed and fabricated by means of TPL. Moreover, these samples are characterized via a rapid and scanless terahertz imaging technique customized to obtain a coherent hyperspectral analysis of the generated vortex beams at varying distances along propagation. Numerical simulations are also conducted for comparison with experiments, revealing a good agreement. Current limitations of the technique are found to be mainly related with terahertz loss in TPL polymers, and possible solutions are discussed.

Modulation of Plet1 expression by N-Acetylglucosamine through the IL-17A-MAPK pathway in an imiquimod-induced psoriasis mouse model.

Psoriasis (Ps) is a chronic inflammatory disorder marked by skin plaque formation, driven by immune dysregulation and genetic factors. Despite the available treatments, incidence of Ps is increasing in the dermatology patients. Novel strategies are crucial due to current treatment limitations. The interleukin 17 (IL-17) pathway is pivotal in Ps pathogenesis, however the expression of its putative target gene placenta expressed transcript 1 (Plet1) remains unstudied in Ps. Considering the potential anti-inflammatory properties of N-Acetylglucosamine (GlcNAc), our study explored its role in modulating Plet1 expression in an imiquimod (IMQ)-induced Ps mouse model. Our data demonstarted a significant reduction of inflammation and Psoriasis Area and Severity Index (PASI) scores, downregulation of growth factors (GFs), IL-17A, and MAPK expression after GlcNAc treatment. In addition, GlcNAc treatment reduced neutrophils, monocyte-dendritic cells (Mo-DC) and conventional T cells (Tcons) while increasing monocyte-macrophages (Mo-Macs) and regulatory T cells (Tregs). GlcNAc treatment also downregulated Plet1 overexpression in psoriatic mouse skin and in vitro, reduced proliferation and apoptosis in IL-17A stimulated human dermal fibroblasts (HDF), along with IL-17A and TGF-β mRNA expression. Together, these data suggest that, GlcNAc interferes with downstream mechanisms in IL-17 pathway and downregulating Plet1 expression, presenting a promising strategy for Ps treatment.

Machine learning-driven polygenic risk scores for bipolar disorder, depression, and panic disorder

Polygenic Risk Score (PRS) is a computational tech- nique that uses various genomic data to simultaneously analyze an individ- ual’s genetic risk for particular illnesses or traits. However, the traditional PRS computation has a few weaknesses, including its limited capacity to account for just a portion of trait variance, susceptibility to overfitting, and insufficient ability to discriminate among the larger population. Machine Learning (ML) methods offer a promising alternative to the traditional method by avoiding the problem of overfitting and improving accuracy. This study aims to develop an ML model for improved PRS calculation. We used the summary statistics for three mentals diseases, bipolar, depression, and panic disorder, from the Psychiatric Genomics Consortium (PGC) as a disease reference. We also obtained actual genotype data of individuals from OpenSNP, which includes both case and control samples. This data is used for predicting scores. The suggested approach, called Polygenic Risk Score Neural Network (PRSNN), calculates the PRS using weight vectors that estimate the relevance of each single nucleotide polymorphism (SNP) with a particular phenotype by deep learning model as an alternative to the traditional method. This study aims to develop a machine learning model, called PRSNN, for improved calculation of Polygenic Risk Scores (PRS). The PRSNN method outperforms the conventional method in identifying individuals at risk of mental disease. A novel deep-learning approach, named as PRSNN, is proposed for generating PRSs. The results demonstrate that it outperforms the traditional method of computing PRS for complex diseases. Further upgrades for this tool are required to overcome the current limitations, including lack of validation with external data from different ancestries, which may limit the applicability of the PRSNN method across diverse populations, and the small sample size, which may affect the results.

A Review on Balancing Genomic Selection Efforts for Allogamous Plant Breeding

Genomic selection (GS) represents a paradigm shift in allogamous plant breeding, leveraging genome-wide marker data to predict the genetic potential of breeding candidates with unprecedented accuracy. This comprehensive review explores the evolution, principles, and applications of GS, highlighting its transformative impact on breeding efficiency and crop improvement. GS uses dense SNP markers to capture the genetic architecture of complex traits, surpassing traditional marker-assisted selection in predictive power. The integration of GS in breeding programs for crops like maize and perennial ryegrass has demonstrated significant gains in traits such as yield, disease resistance, and stress tolerance. Despite its advantages, GS faces challenges including high genotyping costs, the need for large training populations, and the complexity of genetic architectures. Economic constraints and ethical concerns about genetic diversity and data access also pose barriers. Emerging technologies such as AI, machine learning, high-throughput phenotyping, and genome editing hold promise for enhancing GS's accuracy and efficiency. Future strategies should focus on optimizing resource allocation, integrating GS with conventional breeding methods, and fostering collaborative efforts for data sharing and capacity building. The long-term impact of GS is profound, potentially accelerating breeding cycles, enhancing genetic diversity, and developing climate-resilient crops. Collaborative initiatives and open-access genomic resources will be crucial in overcoming current limitations and ensuring that GS benefits global agriculture. As GS continues to evolve, it promises to drive sustainable agricultural practices and improve food security, meeting the challenges posed by climate change and a growing global population. This review underscores the critical role of GS in modern plant breeding and its potential to revolutionize crop improvement.

Designing methacrylic anhydride-based hydrogels for 3D bioprinting

Methacrylic anhydride-based hydrogels, derived by introducing methacryloyl groups to various polymer side chains, are promising bioinks for 3D printing in medical applications. These hydrogels combine the inherent biocompatibility and therapeutic benefits of their parent polymers with the unique photocrosslinking properties conferred by the methacryloyl groups, allowing the precise control over their mechanical properties through light-curing parameters. Using three-dimensional (3D) biological compression, these hydrogels serve as bioinks for producing scaffolds with optimal porosity, facilitating cell adhesion, proliferation, and differentiation. This technology enables the precise spatial distribution of bioactive substances, offering targeted therapeutic treatment and controlled release. This review delved into recent advancements in bioprinting technologies, outlined the preparation of methacrylic anhydride-based bioinks, and summarized factors influencing the resulting biological and mechanical properties of bioscaffolds. Additionally, the properties and applications of methylpropionic anhydride-based hydrogels in various medical fields were discussed, addressing current limitations and future challenges in integrating these hydrogels with 3D bioprinting for clinical applications.

Designing an artificial intelligence-powered video assistant referee system for team sports using computer vision

This paper investigates the efficacy of an AI-powered Video Assistant Referee (VAR) system in enhancing officiating accuracy, efficiency, and consistency in team sports. Employing a combination of artificial intelligence and computer vision, the system was tested in a local championship in Almaty, involving eight football teams. Through the analysis of decision-making accuracy, time efficiency, and consistency across officiating scenarios, the study employed chi-squared tests, paired t-tests, and Cohen's Kappa statistics to quantitatively assess improvements over traditional VAR systems. Results indicated that the AI-powered VAR system significantly increased the accuracy of decisions and reduced the decision-making time, thereby maintaining the fluidity of gameplay. Although the system also demonstrated enhanced consistency in officiating decisions, it highlighted areas needing further refinement to handle complex game situations effectively. The findings suggest that AI integration into sports officiating can substantially benefit the fairness and dynamics of team sports, provided that ongoing technological advancements continue to address current limitations. This study contributes to the growing body of knowledge on the intersection of technology and sports, offering a framework for future enhancements in digital officiating systems.

Tensor Network State Algorithms on AI Accelerators.

We introduce novel algorithmic solutions for hybrid CPU-multiGPU tensor network state algorithms utilizing non-Abelian symmetries building on AI-motivated state-of-the-art hardware and software technologies. The presented numerical simulations on the FeMo cofactor, which plays a crucial role in converting atmospheric nitrogen to ammonia, are far beyond the scope of traditional approaches. Our large-scale SU(2) spin adapted density matrix renormalization group calculations up to bond dimension D = 216 on complete active space (CAS) size of 18 electrons in 18 orbitals [CAS(18, 18)] demonstrate that the current limit of exact solution, i.e. full-CI limit, can be achieved in fraction of time. Furthermore, benchmarks up to CAS(113, 76) demonstrate the utilization of NVIDIA's highly specialized AI accelerators via NVIDIA Tensor Cores, leading to performance around 115 TFLOPS on a single node supplied with eight NVIDIA A100 devices. As a consequence of reaching 71% of the full capacity of the hardware, the cubic scaling of computational time with bond dimension can be reduced to a linear form for a broad range of D values; thus, breaking the current computational limits of small CAS spaces in ab initio quantum chemistry and material science is becoming a reality. In comparison to strict U(1) implementations with matching accuracy, our solution has an estimated effective performance of 300-500 TFLOPS, which emphasizes the mutual need for both algorithmic and technological developments to push current frontiers on classical computation.

A comprehensive analysis of supercapacitors with current limitations and emerging trends in research

A comprehensive analysis of supercapacitors with current limitations and emerging trends in research