Negative / Null Result ReportOpen accessEngineering
Rui Zhao, Qiushi Feng, Yangyang Xia et al. · 2025 · Pharmaceuticals
The inherent complexity and heterogeneity of tumors pose substantial challenges for the development of effective oncology therapeutics. Organoids, three-dimensional (3D) in vitro models, have become essential tools for predicting therapeutic responses and advancing precision oncology, with established correlations to clinical outcomes in patient-derived models. These systems have transformed preclinical drug screening by bridging the gap between conventional two-dimensional (2D) cultures and in vivo models, preserving tumor histopathology, cellular heterogeneity, and patient-specific molecular
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Yawen Zhu, Wanqi Yang, Zhong‐Xia Wang et al. · 2025 · Journal of Nanobiotechnology
Liver regeneration is a sophisticated biological process influenced by a complex microenvironment that becomes profoundly altered in various pathological conditions. Current therapeutic approaches, including liver transplantation and…
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Cao Zhengzheng, Xie Mengqi, Rong Tao et al. · 2026 · Scientific Reports
With the development of underground engineering and oil and gas exploitation, the problem of fluid-solid interaction in fractured rock mass has become increasingly prominent, and its complex coupling mechanism has a profound impact on the…
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Yudan Wang, Litian Liu, Xinning Li et al. · 2022 · Journal of Interventional Cardiology
Background: Mortality after percutaneous coronary intervention (PCI) in ST-elevation myocardial infarction (STEMI) patients with cardiogenic shock (CS) remains high. However, the real-world risk factors for mortality in these patients are poorly defined. Objective: The aim of this study is to establish a clinical prognostic nomogram for predicting in-hospital mortality after primary PCI in STEMI patients with CS. Methods: This retrospective, multicenter, observational study included STEMI patients with CS who underwent PCI at 39 hospitals in Hebei Province from January 2018 to December 2019. A
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Wan Azani Mustafa, Haniza Yazid, Hiam Alquran et al. · 2024 · PLoS ONE
Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more difficult and lead to incorrect identification during weld defect detection. Weld defect inspection uses X-radiography testing, which is now mostly outdated. Recently, numerous researchers have utilized X-radiography digital images to inspect the defect. As a result, for error-free inspection, an autonomous weld detection and classification system are required. One of the most difficult issues in the field of image processing, particu
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Junyi Yin, Jie Zhu, Shaolei Wang et al. · 2026 · Nature Communications
One-dimensional (1D) multifunctional fibers have garnered significant attention due to their advantageous geometry properties, which allows conformal interfacing with soft biological tissues and efficient charge transport. Here, we…
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Yongxiang Liu, Weijie Li, Li Liu et al. · 2026 · IEEE Transactions on Pattern Analysis and Machine Intelligence
The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR AT
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Jun Tao, Zhihan Wang, Jianqiu Wu et al. · 2026 · Remote Sensing
Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often contain strong background interference such as specular reflections. Model development is further constrained by the scarcity of data from the early ignition stage. To address these challenges, we propose a joint data and model optimization framework. We first build a hybrid dataset through an ROI-guided synthesis pipeline, in which latent diffusion models are u
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Yuzhou Tang, Judith S. Ford, Tim T. Cockerill · 2026 · Biochar
Abstract The agricultural sector urgently requires scalable solutions to reduce greenhouse gas (GHG) emissions from residue management. Biochar offers a promising carbon removal pathway, but its adoption is limited by technical, regulatory, and economic barriers. A key constraint is the lack of system designs that can accommodate multiple feedstocks while complying with land application regulations. This study designs and evaluates an integrated biochar production system that enables the separate processing of straw and manure through parallel pyrolysis lines, while optimising internal energy
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Prabhu Gurunathappa Sheelavantar, Poornachandra Pandit, Shreelaxmi Prashant · 2026 · Scientific Reports
Abstract The need for sustainable, practical alternatives to Portland cement and two-part alkali-activated systems has led to the development of one-part alkali-activated concrete (OPAAC), which efficiently reuses industrial by-products like fly ash and ground granulated blast furnace slag (GGBS). This study evaluates the fresh (workability) and hardened properties (compressive, tensile, and flexural strengths) of FA-GGBS-based OPAAC, along with durability indicators including sorptivity and chloride ion permeability. A performance-based multi-response optimization using a Taguchi L 9 array an
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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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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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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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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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Kaituo Li, Lin Zhu, Fei Xiong et al. · 2026 · Processes
Enhanced geothermal systems (EGS) are a key technology for developing deep geothermal resources, yet they face significant challenges in constructing efficient thermal reservoirs within high-stress, high-strength, and low-permeability crystalline rock formations. Traditional hydraulic fracturing (HF) techniques encounter deep challenges in these environments, including excessively high fracturing pressures, limited fracture network patterns, and the risk of induced seismicity. This paper reviews the multi-scale thermal-mechanical mechanisms, fracture evolution patterns, and control strategies
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Zhenlin Zhang, Lang Yan, Weiwei Li et al. · 2026 · Nature Communications
In the development of clinically translatable triplet photosensitizers for hypoxia regulated photodynamic therapy (PDT), there is an unmet need for engineering sensitizers as near-infrared (NIR)-responsive, type I/type Ⅱ dual…
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Yu‐Hui Zhang, Lu-Qiang Wei, Chen-Shuang Liu et al. · 2025 · ACS Omega
High Resolution Image Download MS PowerPoint Slide Pesticides are of great significance in ensuring food yield. However, the extensive use of pesticides has led to severe environmental pollution and significant economic losses.…
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E. Maartensson, N. Welch, M. Scarpari et al. · 2026 · Nuclear Fusion
Abstract ST-E1 is Tokamak Energy’s commercially competitive fusion power plant design featuring a lifetime high-temperature superconducting (HTS) magnet cage, which has completed its pre-concept design stage. A central challenge at the pre-concept stage is the need to iteratively and consistently integrate the development of the magnet cage, core plasma physics, and power-exhaust systems, while avoiding serial design loops and late discovery of infeasible operating scenarios. Accurate representation of inductive current drive is particularly critical, as it tightly couples magnetic equilibria,
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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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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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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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Justyna Kujawska, Wojciech Cel, B. Charmas et al. · 2026 · Energies
Biochar obtained from digestate is a promising material in the context of digestate management. However, it is important to note that the properties of the resulting material are largely dependent on the parameters of the pyrolysis process, with temperature being a particularly significant factor. The objective of this study was to evaluate the impacts of the digestate pyrolysis temperature on the chemical structure, thermal stability, and thermal decomposition characteristics of biochar produced at temperatures of 400, 500, 600, and 800 °C in an inert nitrogen atmosphere. Material characteriz
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Yasin Onuralp Özkılıç, Mohamed Abdikarin Mohamud, Fatih Yılmaz et al. · 2026 · Scientific Reports
This study investigates the mechanical performance of geopolymer concrete cured under various oven temperatures and ambient conditions, focusing on the interactions among curing regimes, alkaline activator molarities, and ground granulated…
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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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Hanxu Ma, Shunda Qiao, Ying He et al. · 2026 · Reports on Progress in Physics
Abstract This paper proposed an optical detection mechanism that utilized orthogonal phase modulation (OPM) and Lissajous mode decoupling within a light-induced thermoelastic spectroscopy (LITES) sensor for the first time, enabling…
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Mohamed Abdellatief, Mohamed Mortagi · 2026 · Scientific Reports
Accurate prediction of the unconfined compressive strength (UCS) of geopolymer-stabilized clayey soil is critical for geotechnical engineering. Conventional regression algorithms and even advanced machine learning approaches such as artificial neural networks often struggle to fully capture the highly non-linear interactions among soil properties and geopolymer mix parameters while maintaining computational efficiency and interpretability on limited datasets. To address these challenges, this investigation proposes a novel hybrid predictive framework based on a sector fruit fly optimization al
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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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