Traditional Face Research Articles

Optimization of municipal solid waste incineration for low-NOx emissions through numerical simulation

With urbanization, municipal solid waste (MSW) generation is increasing. Traditional landfill methods face land shortages and environmental pollution. Waste incineration, which reduces waste and recovers resources, has become a key management method. However, nitrogen oxides (NOx) produced during incineration severely impact the environment, requiring improved control technologies. This study optimized three denitrification technologies—air staging, flue gas recirculation (FGR), and selective non-catalytic reduction (SNCR)—using numerical simulations. The research provides support for improving waste incinerator efficiency and stability while reducing NOx emissions, aiding the sustainable development of waste incineration technology. By optimizing the primary and secondary air distribution ratios, the initial NOx generation was reduced by 8.39%. When 20% of the recirculated flue gas was introduced as secondary air, NOx generation was reduced by 23.54%, and boiler efficiency increased to 83.78%. The study examined the impact of different sludge mixing ratios on the temperature and NOx emissions within the context of municipal solid waste (MSW) incineration. Initially, the study aimed to address the environmental concerns of NOx emissions during the incineration process by exploring how the introduction of sludge at various mixing ratios would affect combustion parameters. The results showed that a sludge mixing ratio between 3 and 13% optimized the combustion process with 7% being the most effective in balancing temperature control and NOx emissions. Specifically, the best value of the sludge mixing ratio refers to achieving an optimal reduction in NOx emissions while maintaining stable incinerator operation. The chemical compositions of the sludge included key elements such as carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O), with approximate proportions of C: 31.2%, H: 4.7%, N: 2.5%, S: 0.6%, and O: 31.8%.

A label-free battery state of health estimation method based on adversarial multi-domain adaptation network and relaxation voltage

The state of health (SOH) estimation of lithium-ion batteries is crucial for the operational reliability and safety of electric vehicles. However, traditional data-driven methods face problems of label shortage and domain discrepancy caused by diverse battery types and operating conditions. This paper proposes a label-free SOH estimation method based on adversarial multi-domain adaptation technique and relaxation voltage (RV). Firstly, the raw RV and integral voltage are proposed to construct the two-dimensional input sequence to ensure high adaptability across various target domains. Secondly, a two-dimensional convolutional neural network integrated with fully connected layers is developed to establish the feature extractor and SOH estimator, which paves the way for effective knowledge transfer. Furthermore, an adversarial multi-domain adaptation method with a domain discriminator is developed to optimize the extraction of domain-invariant features and enable accurate SOH estimation without SOH labels. Datasets of 50 batteries with different temperatures and materials are used for the validation. The proposed method shows outstanding performance, achieving the average RMSE and MAE of 0.0274 and 0.0240 across various working temperatures, and 0.0177 and 0.0148 across different battery types. It suggests that the proposed method can achieve robust adaptive battery SOH estimation across multiple target domains without using SOH labels.

Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.

Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes. Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care. The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model's proficiency through zero-shot as well as few-shot prompting strategies. Results show GPT's varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision. Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.

A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment of geographic zones within smart cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, the proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, and energy consumption. The CNN component constructs hierarchical feature maps through successive convolution and pooling operations, while the LSTM captures sequence-based patterns. Fully connected layers integrate these features to generate final predictions. Implemented in Python using TensorFlow and Keras on an Intel Core i7 system with 32 GB RAM and an NVIDIA GTX 1080 Ti GPU, the model demonstrated a superior performance. It achieved a mean absolute error of 0.042, root mean square error of 0.067, and an R-squared value of 0.935, outperforming existing methodologies in real-time adaptability and resource efficiency.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

Nanoparticle-Based Therapies for Neurotropic Viral Infections: Mechanisms, Challenges, and Future Prospects.

Neurotropic viral infections pose a significant challenge due to their ability to target the central nervous system and cause severe neurological complications. Traditional antiviral therapies face limitations in effectively treating these infections, primarily due to the blood-brain barrier, which restricts the delivery of therapeutic agents to the central nervous system. Nanoparticle-based therapies have emerged as a promising approach to overcome these challenges. Nanoparticles offer unique properties that facilitate drug delivery across biological barriers, such as the blood-brain barrier, and can be engineered to possess antiviral activities.

Identification of hanger damage based on deflection influence matrix

Tied-arch bridges, a vital component of modern infrastructure, are susceptible to various forms of damage, particularly hangers. The detection and identification of such damages are crucial for maintaining structural integrity and safety. However, traditional methods face challenges in terms of accuracy and efficiency. This study aims to develop a refined method for hanger damage identification in tied-arch bridges, to address the limitations of existing techniques. By focusing on deflection changes at the anchoring points between the hangers and tie beams, we sought to enhance the precision of damage detection. We propose an innovative approach based on the concept of influence lines, introducing the ‘generalized deflection difference influence line'and the ‘deflection difference influence matrix’. Then proposed a new identification index for identifying the damaged hanger after matrix. An actual tied-arch bridge was used to validate the proposed approach. A detailed three-dimensional finite element model of the bridge was developed and calibrated using dynamic and static response data. Thirty different hanger-damage conditions were simulated to evaluate the effectiveness of the proposed method. Our findings reveal that the deflection difference influence matrix offers more detailed and comprehensive information on bridge distribution points than traditional methods. Our method proved effective in identifying hanger damage, irrespective of its location on the bridge. In additionally, the identification efficiency of the method can be improved by adjusting the magnitude of the applied load, with larger loads amplifying the detectability of damage. This study highlights the potential of the deflection difference influence matrix to revolutionize hanger damage identification for tied-arch bridges. Its adaptability, accuracy, and efficiency are significant advancements over existing methods. This study successfully demonstrates an innovative and reliable method for hanger damage identification.

Ensembled methodology for the comtrade analysis regarding medium voltage side in wind park

This research proposes a methodology for automated COMTRADE analysis using ensembled algorithms for fault analysis in medium voltage circuits, specifically for wind park feeders. Traditional fault detection methods face significant limitations, such as CT saturation and DC offset, underscoring the need for improved approaches. The proposed methodology integrates Random Committee, XGBoost (XGB), and Light XGB algorithms, optimized through grid search, achieving an accuracy of 99.397 %. This ensemble approach significantly reduces classification errors, false positives, and false negatives compared to single algorithm methods. This research has the analysis of 3929 events for training and 829 fault events for test, it revealed a high correlation between current and fault spectra; the methodology demonstrated superior performance in fault location analysis, with terminal faults showing the most severe transient recovery voltage (TRV) peak values, while kilometer faults exhibited higher transient recovery restoration voltage growth rates. The fault location analysis was conducted by comparing simulation-derived overvoltage with standard TRV graphs, aligned with IEC 62271-100 standards. Circuit breakers exhibited higher TRV tolerance with lower fault currents. Terminal faults, particularly symmetrical three-phase faults, were identified as the most severe, typically occurring at peak TRV values. Its findings reduced classification errors, from 41 to 5 events, and decreased the false negative rate from 1.81 % to 0.24 % compared to Random Forest. Validation with virtual relays showed a reduction in RCE from 5.3 % to 0.63 %.

Prospect of large language models and natural language processing for lung cancer diagnosis: A systematic review

AbstractLung cancer, a leading cause of global mortality, demands a combat for its effective prevention, early diagnosis, and advanced treatment methods. Traditional diagnostic methods face limitations in accuracy and efficiency, necessitating innovative solutions. Large Language Models (LLMs) and Natural Language Processing (NLP) offer promising avenues for overcoming these challenges by providing comprehensive insights into medical data and personalizing treatment plans. This systematic review explores the transformative potential of LLMs and NLP in automating lung cancer diagnosis. It evaluates their applications, particularly in medical imaging and the interpretation of complex medical data, and assesses achievements and associated challenges. Emphasizing the critical role of Artificial Intelligence (AI) in medical imaging, the review highlights advancements in lung cancer screening and deep learning approaches. Furthermore, it underscores the importance of on‐going advancements in diagnostic methods and encourages further exploration in this field.

Deep learning model for precise prediction and design of low-melting point phthalonitrile monomers

Phthalonitrile is a widely applied resin due to its outstanding high-temperature resistance and versatility. However, the high melting point of its monomer limits its range of applications. Due to the scarcity of data, traditional deep learning models face challenges in accurately predicting the properties of phthalonitrile monomers. In this work, we propose a novel deep learning model based on techniques such as molecular similarity weighting, pre-training parameter averaging, and hybrid neural networks to address the issue of data sparsity. Our model exhibits exceptionally high accuracy for predicting the melting point of phthalonitrile monomers, e.g., the mean absolute error on the experimental dataset is 15.3 °C. To explore novel low-melting monomers, we develop a high-throughput molecule generation software and use our model to filter the virtual molecules, and ultimately obtain candidates that meet low-melting and synthesizability requirements. Moreover, we have successfully synthesized one candidate molecule, and the high consistency between our predictions and experimental measurements again demonstrates our model’s accuracy. Meanwhile, molecule dynamic simulations are conducted to provide explanations for the reduction in melting point. This study not only provides guidance for designing phthalonitrile monomers, but also proposes an innovative approach for exploring of new materials under conditions of sparse data.

Robust face recognition model based sample mining and loss functions

Traditional face recognition algorithms rely on margin-based softmax loss functions merely. However, these algorithms tend to perform poorly with low-quality images owing to the varying hardness of the datasets. To address this issue, we introduce a face recognition algorithm based on sample mining (FRABSM), an innovative face recognition algorithm that improves performance by integrating sample mining with conventional margin-based methods. Sample mining selectively focuses on specific samples during model training. FRABSM prioritises information-dense samples with more distinctive features. In this study, we present a probability-driven mining strategy that enhances the ability of the model to handle hard samples, thereby significantly increasing its robustness and adaptability. Mathematical evaluations demonstrate the effectiveness of FRABSM. An accuracy of 94.70% is achieved on the CPLFW. Additionally, the experimental results show that our approach achieves improvements over state-of-the-art methods on three renowned datasets (CPLFW, IJB-B, and TinyFace), highlighting its potential and efficiency. The source code is available at https://github.com/Xkf0/FRABSM.

Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning.

Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.

Carbon contrast agents for terahertz spectroscopic NDT of impacted glass fibre reinforced plastics

Impact damage poses a significant challenge for composite materials since it is often subsurface and hard to detect, necessitating effective assessment approaches. Traditional NDT methods face challenges in identifying this specific type of damage. In this study, a departure from traditional NDT approaches was taken by utilizing THz spectroscopy to detect impact damage. Three different lay-ups of GFRP laminates were studied; GFRP with non-woven carbon fibre veil, GFRP coated with MWCNTs, and plain GFRP. Impact tests were conducted ranging from 5, 12 and 20 J in energy and 2D images were then acquired It was found that the inclusion of the MWCNTs and carbon veil gave superior image contrast compared to plain GFRP. Furthermore, the damaged area observed in the images increased with impact energy across all configurations. The outcomes derived from THz spectroscopy were benchmarked against those from ultrasound testing. THz spectroscopy demonstrates greater promise as a highly effective technique for detecting impact damage using carbon materials, offering advantages over traditional NDT methods due to its distinct wavelength, portability, non-contact capability, capacity to depict fibre alignment, lack of additional calibration specimens and coupling agents, and its ability to partially uncover the scope of impact damage.

Aperture photometry on asteroid trails. Detection of the fastest-rotating near-Earth object

Near-Earth objects (NEOs) on an impact course with Earth can move at high angular speeds. Understanding their properties, including their rotation state, is crucial for assessing impact risks and mitigation strategies. Traditional photometric methods face challenges in accurately collecting data on fast-moving NEOs. This study introduces an innovative approach to aperture photometry, tailored to analyzing trailed images of fast-moving NEOs. Our primary aim is to extract rotation state information for fast rotators. We applied our approach to the trailed images of three asteroids: 2023 CX1, 2024 BX1, and 2024 EF, which were either on a collision course or on a close fly-by with Earth, resulting in high angular velocities. By adjusting the aperture size, we controlled the effective instantaneous exposure time of the asteroid to increase the sampling rate of photometric variations. This enabled us to detect short rotation periods that would be challenging to derive with conventional methods. Our analysis shows that trailed photometry significantly reduces the overhead time associated with CCD readout, enhancing the sampling rate of the photometric variations. We demonstrate that this technique is particularly effective for fast-moving objects, providing reliable photometric data when the object is at its brightest and closest to Earth. For asteroid 2024 BX1, we detect a rotation period of $2.5888 0.0002$ seconds, the shortest ever recorded. We discuss under what circumstances it is most efficient to use trailed observations coupled with aperture photometry for studying the rotation characteristics of NEOs.

Enhanced Trajectory Forecasting for Hypersonic Glide Vehicle via Physics-Embedded Neural ODE

Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting.

Design of an Intelligent Radio Frequency Identification (RFID) Based Cashless Vending Machine for Sales of Drinks

This study examines the evolution of vending machines, emphasizing their integration with RFID technology for cashless transactions. Vending machines have transformed human-machine interaction, offering convenient access to products and services. Transitioning from coin-operated to RFID-enabled systems has revolutionized the industry, enhancing security, reducing costs, and improving user experience. Through exploration of technical specifications and design objectives, the research highlights RFID vending machines' potential to reshape consumer behavior and optimize operations. Traditional cash-based vending machines face challenges such as limited storage, recognition issues, and security concerns. To address these, the paper proposes an intelligent RFID-based cashless vending machine for drink sales. The system incorporates RFID technology for payment, allowing users to swipe cards and select drinks without cash involvement. Prototype development involved software design, utilizing C-language for multiproduct vending. Comparatively, the RFID-based system outperforms cash-based counterparts in efficiency, security, and sales tracking, presenting a superior solution for drink sales.

Effect of Microwave Curing on the Performance of High-volume Fly Ash Concrete

Concrete curing significantly impacts mechanical properties and durability, yet traditional methods face efficiency challenges. Microwave curing (MC) offers a promising alternative, providing rapid and uniform concrete curing. However, fully understanding the impact of MC on concrete performance, particularly in high-volume fly ash concrete (HVFC), remains a research gap. This study addresses this gap by investigating the effect of MC on key properties of HVFC, including compressive strength, drying shrinkage, chloride ion penetration, and resistance to sulfate attack. Concrete specimens underwent MC at varied energies (200–1000 W) and durations (10–30 min) to evaluate their mechanical and durability characteristics. Results show that MC significantly boosts early-stage concrete strength, notably at high applied energy. The specimen cured at 1000 W for 30 min (MC1000-30) achieved the highest 1-day strength of 23.68 MPa. However, the normal curing specimen exhibited the highest strength of 32.07 MPa at 28 days while the strength value of the MC1000-30 specimen declined to 20.25 MPa, indicating a 36.85% decrease. Although declining strength was observed after 28 days, the MC1000-30 specimen demonstrated improved durability with significantly reduced drying shrinkage (by 83.12%) and chloride ion penetration (by 31.6%). The study underscores the feasibility of utilizing MC for HVFC as it demonstrated significant enhancements in both early-stage concrete strength and durability at a later stage. Careful parameter optimization can ensure sustained effectiveness over time, offering a viable alternative to traditional curing methods.

Theology and Modernity: Exploring Faith, Morality, and Human Condition Through Dostoevsky and De Lubac

In a world increasingly characterized by secularism and philosophical questioning, traditional theological beliefs face ongoing challenges. This article explores the convergence of religious thought and critical theory in the field of critical theology and demonstrates how theological concepts can critique and reshape social structures and the human existence. It synthesizes insights from the literary works of Fyodor Dostoevsky and the scholarly perspectives of Henri de Lubac, Joseph Frank, David Foster Wallace, and Frederick Copleston. The discussion emphasizes Dostoevsky’s relevance in examining faith, morality, and existential angst while exploring the fusion of theological and secular ideas to promote social justice and human well-being. This article accentuates the essential role of theology in contemporary society and argues for a balanced approach that considers both religious traditions and secular viewpoints. This interdisciplinary perspective provides a solid framework for understanding and improving the human condition.

Recovering 3D salt dome by gravity data inversion using ResU-Net++

In geophysical research, gravity-based inversion is essential for identifying geologic anomalies, mapping rock structures, and extracting resources such as oil and minerals. However, traditional gravity inversion methods face challenges, such as the volumetric effects of gravity fields and the management of large complex matrices. Unsupervised learning techniques often struggle with overfitting and interpreting gravity data. This study explores the application of various U-Net-based network architectures in gravity inversion, each offering distinct challenges and advantages. Nested U-Net, although effective, requires a high parameter count, leading to extended training periods. The recurrent residual U-Net’s implicit attention mechanism restricts its dynamic adaptability, whereas the attention U-Net’s lack of residual connections raises concerns about gradient issues. This research comprehensively analyzes the training processes, core functionalities, and module distribution of these networks, including the residual U-Net++. Our synthetic studies compare these networks with traditional focused regularized gravity inversion for reconstructing density anomalies. The results demonstrate that the nested U-Net closely approximates the actual model despite some redundancy. The recurrent residual U-Net indicates an improved alignment with minimal redundancies, and the attention U-Net is effective in density prediction but encounters difficulties in areas of low density. Notably, the residual U-Net++ excels in inversion modeling, achieving the lowest misfit percentage and accurately replicating density values. In practical applications, the residual U-Net++ impressively reconstructs the F2 salt diapir in the Nordkapp Basin with well-defined boundaries that closely match seismic data interpretations. These results underscore the capabilities of the residual U-Net++ in geophysical data analysis, structural reconstruction, and inversion, demonstrating its effectiveness in simulated settings and real-world scenarios.

Nanoclay-Composite Hydrogels for Bone Tissue Engineering.

Nanoclay-composite hydrogels represent a promising avenue for advancing bone tissue engineering. Traditional hydrogels face challenges in providing mechanical strength, biocompatibility, and bioactivity necessary for successful bone regeneration. The incorporation of nanoclay into hydrogel matrices offers a potential unique solution to these challenges. This review provides a comprehensive overview of the fabrication, physico-chemical/biological performance, and applications of nanoclay-composite hydrogels in bone tissue engineering. Various fabrication techniques, including in situ polymerization, physical blending, and 3D printing, are discussed. In vitro and in vivo studies evaluating biocompatibility and bioactivity have demonstrated the potential of these hydrogels for promoting cell adhesion, proliferation, and differentiation. Their applications in bone defect repair, osteochondral tissue engineering and drug delivery are also explored. Despite their potential in bone tissue engineering, nanoclay-composite hydrogels face challenges such as optimal dispersion, scalability, biocompatibility, long-term stability, regulatory approval, and integration with emerging technologies to achieve clinical application. Future research directions need to focus on refining fabrication techniques, enhancing understanding of biological interactions, and advancing towards clinical translation and commercialization. Overall, nanoclay-composite hydrogels offer exciting opportunities for improving bone regeneration strategies.