Negative / Null Result ReportOpen accessEngineering
Fengxiang Liu, Shipeng Ning, Xia Wang et al. · 2025 · Science Advances
Cancer stem cells (CSCs) and myeloid-derived suppressor cells (MDSCs) contribute to chemoresistance and immunosuppression, constraining chemoimmunotherapy outcomes. Differentiation therapy, aiming to mature CSCs and MDSCs, shows great promise. However, its efficacy is hindered by limited accessibility in hypoxic deep tumor regions. Inspired by the apoptotic body (ApoBD)-mediated deep tumor penetration, we design a pulsatile sequential drug release system with a core-shell structure. The reversible acid-responsive shell protonates and swells in lysosomes to release doxorubicin, inducing lysosom
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J. Jithish, Nagarajan Mahalingam, Bo Wang et al. · 2025 · Cybersecurity
Abstract The forthcoming 6G technology offers significant potential for the advancement of the smart grid domain. 6G promises ultra-low latency, higher data transfer rates, native Artificial Intelligence (AI) support, enhanced connectivity, and improved security for smart grids. Smart grids are vulnerable to cyberattacks, such as Distributed Denial-of-Service (DDoS) attacks, posing a significant threat to grid functionality. To address security concerns, smart grids implement intrusion detection systems (IDS), but detecting novel attacks such as subtle multi-domain DDoS attacks through traditi
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Takahiro Ogawa, Rune Shibata, Keiji Komatsu et al. · 2025 · International Journal of Implant Dentistry
PURPOSE: Despite decades of clinical success with microrough implant surfaces, persistent challenges-particularly the biological trade-off between osteoblast proliferation and differentiation-highlight the need for novel surface design strategies. This review investigates the potential of meso-scale topography (10-500 μm) as a promising and underexplored dimension in implant surface engineering, situated between macro-level implant geometry and conventional microroughness. METHODS: A systematic review, supplemented by a targeted literature search, was conducted to evaluate the biological and m
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Lihua Liang, Xiaodong Li, Shutong Liu et al. · 2026 · Buildings
This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwellings. Focusing on Liangshuaixiu dwellings in Wu’an, southern Hebei, we trained an HRNetV2 semantic segmentation model on high-resolution satellite imagery to identify and extract contours for 134,280 courtyard spaces. Core morphological parameters (area, orientation) were calculated and analyzed using GIS spatial statistics and the geographic detector mode
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Rafael Cabral, Ricardo Santos, José A.F.O. Correia et al. · 2025 · Sensors
The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evalua
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H. Fu, Xiong Zhou, Pengfei Xu et al. · 2025 · Materials
Predicting the compressive strength of concrete is essential for engineering design and quality assurance. Traditional empirical formulas often fall short in capturing complex multi-factor interactions and nonlinear relationships. This study employs an interpretable machine learning framework using Gradient Boosting Trees, Random Forest, and Backpropagation Neural Networks to predict concrete compressive strength. Bayesian optimization was employed for hyperparameter tuning, and SHAP analysis was used to quantify feature contributions. Based on 223 sets of compression test data, this study sys
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Lina Maria Ellegård, Gustav Kjellsson, Linn Mattisson · 2026 · The Economic Journal
Abstract The emergence of markets for on-demand online physician consultations —direct-to-consumer telemedicine (DCT) — is currently transforming many healthcare settings. DCT may be a cost-effective substitute for in-person consultations, but the convenience of seeking DCT may increase the demand for the service and consequently also the costs for health insurers. To causally assess the degree to which DCT consultations substitute for in-person primary care consultations, we exploit exogenous changes in patient fees in a fuzzy difference-in-discontinuities analysis of young adults in Sweden.
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Fhysmélia Firmino de Albuquerque, Rodrigo M. Iost, Frank N. Crespilho · 2025 · ACS Measurement Science Au
The development of enzyme-based bioelectronic devices, including biosensors and biomimetic systems, has significantly advanced with the introduction of innovative materials such as hydrogels, deep eutectic solvents (DES), and ionic liquids (ILs). These materials offer unique advantages in enhancing biodevice performance, particularly in enzyme stabilization, biocompatibility, and electrochemical sensitivity. Hydrogels, known for their high water content and flexibility, provide an ideal matrix for enzyme immobilization in biological applications but are limited by low ionic conductivity. DES,
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Cao Zhengzheng, Guo Fangxu, Rong Tao et al. · 2026 · Scientific Reports
Floor aquifers are responsible for approximately 55% of water inrush incidents in coal mines. As mining depths increase, the risk posed by confined floor aquifers becomes more severe. Grouting reinforcement and the sealing of water…
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Bin Liu, Changfeng Yan, Ming Lv et al. · 2026 · Computers in Industry
Domain adaptation-based methods are extensively applied to predict the Remaining Useful Life (RUL) of rolling bearings under complex operating conditions. However, the nonlinear degradation process of bearings gives rise to markedly…
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M. A. Huque, Leon M. Tolbert, Benjamin J. Blalock et al. · 2026 · IMAPSource Proceedings
High-temperature power conversion modules (DC-DC converters, inverters, etc.) have enormous potential in extreme environment applications, including automotive, aerospace, geothermal, nuclear, and well logging. Power-to-volume and…
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Bianca Menzani, Priscille De Gea, Xavier Gidrol et al. · 2026 · Lab on a Chip
models for studying human physiology, development and disease. Their potential is very important and they have broad applications, but their impact is currently limited by persistent challenges such as incomplete maturation, batch…
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Joao Gabriel Ostrowski, József Menyhárt · 2020 · Acta Polytechnica Hungarica
Based particularly on data technologies, information is rapidly evolving in engineering. In mechanical engineering, maintenance is benefiting the most from data innovations, the reduction of maintenance costs, and the improvement of system…
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Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg et al. · 2020 · arXiv
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme
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Huy Kinh Phan, Viet Lam Phung, Tuan Anh Dinh et al. · 2020 · arXiv
In recent years, statistical parametric speech synthesis (SPSS) systems have been widely utilized in many interactive speech-based systems (e.g.~Amazon's Alexa, Bose's headphones). To select a suitable SPSS system, both speech quality and performance efficiency (e.g.~decoding time) must be taken into account. In the paper, we compared four popular Vietnamese SPSS techniques using: 1) hidden Markov models (HMM), 2) deep neural networks (DNN), 3) generative adversarial networks (GAN), and 4) end-to-end (E2E) architectures, which consists of Tacontron~2 and WaveGlow vocoder in terms of speech qua
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Siqi Ye, Yong Long, Il Yong Chun · 2020 · arXiv
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At each layer of the proposed Momentum-Net, the model-based image reconstruction module solves the majorized penalized weighted least-square problem, and the image refining module uses a four-layer convolutional neural network (CNN). Experimental results with the NIH AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset show that the proposed Momentum-Net architecture significantly improves image reconstruction accuracy,
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Karl Strecker, Sabit Ekin, John OHara · 2021 · arXiv
A theoretical framework and numerical simulations quantifying the impact of atmospheric group velocity dispersion on wireless terahertz communication link error rate were developed based upon experimental work. We present, for the first time, predictions of symbol error rate as a function of link distance, signal bandwidth, signal-to-noise ratio, and atmospheric conditions, revealing that long-distance, broadband terahertz communication systems may be limited by inter-symbol interference stemming from group velocity dispersion, rather than attenuation. In such dispersion limited links, increas
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Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi et al. · 2024 · arXiv
Autonomous agents should coordinate effectively without prior knowledge of others' intents. While prior work has focused on intent inference, we address the inverse problem: how agents can strategically demonstrate their intents within general-sum dynamic games. We model this problem and propose an algorithm that balances intent demonstration with task performance. To handle nonlinear dynamic games with continuous state-action spaces, our method leverages iterative linear-quadratic game approximations and provides efficient intent-teaching guarantees: the uncertain agent's belief can be driven
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Max Langtry, Vijja Wichitwechkarn, Rebecca Ward et al. · 2024 · arXiv
Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve m
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Yanyu Cheng, Chongjun Ouyang, Yuanwei Liu et al. · 2025 · arXiv
This paper conducts a comprehensive performance analysis for pinching-antenna systems (PASS) under both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission. Given the cost of waveguides, we consider a scenario where the waveguide is not deployed in all rooms, i.e., some users are beyond the line-of-sight (LoS) link service area of the PASS. Specifically, we consider a PASS where a pinching antenna in one room serves two users located in separate rooms. The wireless transmissions between the pinching antenna and the users are performed via LoS and non-line-of
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Haoyang Li, Yuchen Hu, Chen Chen et al. · 2024 · arXiv
Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures needed for high-fidelity SE. Group-Rational KAN (GR-KAN), a variant of Kolmogorov-Arnold Networks (KAN), retains KAN's expressiveness while improving scalability on complex tasks. We adapt GR-KAN to existing DNN-based SE by replacing dense layers with GR-KAN layers in the time-frequency (T-F) domain MP-SENet and adapting GR-KAN's activations into the 1D CNN layers in the time-domain Demucs. Results on Voicebank-DEMAND sho
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Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit et al. · 2024 · arXiv
Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in out-of-domain (OOD) scenarios. This phenomenon amplifies with named entities (NEs), where, in addition to insufficient contextual information or knowledge about the NEs, novel NEs keep emerging. To address these issues, we propose DARAG (D
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Mengqi Wang, Zhan Liu, Zengrui Jin et al. · 2025 · arXiv
Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on using the diffusion-based large language model LLaDA for automatic speech recognition (ASR). We first investigate its use as an external deliberation-based processing module for Whisper-LLaMA transcripts. By leveraging the bidirectional attention and denoising capabilities of LLaDA, we explore random masking, low-confidence masking, and semi-autoregressive strategies, showing that Whisper-LLaDA substantially reduces
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Jiaqi Wu, Jingyi Yuan, Yang Weng et al. · 2025 · arXiv
Power system voltage regulation is crucial to maintain power quality while integrating intermittent renewable resources in distribution grids. However, the system model on the grid edge is often unknown, making it difficult to model physical equations for optimal control. Therefore, previous work proposes structured data-driven methods like input convex neural networks (ICNN) for "optimal" control without relying on a physical model. While ICNNs offer theoretical guarantees based on restrictive assumptions of non-negative neural network parameters, can one improve the approximation power with
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Kushal Mehta, Arshita Jain, Jayalakshmi Mangalagiri et al. · 2020 · arXiv
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in orde
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Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong et al. · 2023 · arXiv
Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still retain complex characteristics even after being magnified, which may di
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Deepak Singla, Soham Chatterjee, Lavanya Ramapantulu et al. · 2020 · arXiv
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages over traditional imagers in this domain, the existing NVS systems either do not meet the power constraints or have not demonstrated end-to-end system performance. To address this, we improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal and address the low-power requirement for object detection and classification by exploring various convolut
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Luca Didaci, Sara Maria Pani, Claudio Frongia et al. · 2023 · arXiv
EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results
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Neil Irwin Bernardo, Jingge Zhu, Jamie Evans · 2021 · arXiv
We analyze the symbol error probability (SEP) of $M$-ary pulse amplitude modulation ($M$-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized $M$-PAM receivers is obtained when Nakagami-$m$ fading channel is considered. The derived expression enables the optimization of the quantizer and/or constellation under the minimum SEP criterion. Our analysis of optimal quantization for equidistant $M$-PAM receiver reveals the existence of error fl
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Amritanshu Pandey, Marko Jereminov, Gabriela Hug et al. · 2017 · arXiv
Recent advances in power system simulation have included the use of complex rectangular current and voltage (I-V) variables for solving the power flow and three-phase power flow problems. This formulation has demonstrated superior convergence properties over conventional polar coordinate based formulations for three-phase power flow, but has failed to replicate the same advantages for power flow in general due to convergence issues with systems containing PV buses. In this paper, we demonstrate how circuit simulation techniques can provide robust convergence for any complex I-V formulation tha
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