135 real negative results, null findings, and replication failures in Engineering. Search the index →
WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record compiled from open scholarly databases; the abstract is shown in full only where the paper is openly licensed, otherwise a short excerpt under fair use. Classifications are automated and approximate.
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…
View details →Angela Stefania Bergantino, Giulio Fusco, Mario Intini et al. · 2025 · The Annals of Regional Science
Abstract The digital economy can function either as a catalyst to stimulate economic growth or else as a driver of socioeconomic inequality when its benefits are unevenly distributed. This study investigates the effect of rural digital connectivity on income inequality in Italy. Utilizing NUTS 3 panel data spanning 2014–2022, we conduct a counterfactual Difference-in-Differences approach with continuous treatment intensity to estimate the impact of introducing rural broadband coverage at speeds of 30 and 100 Mbps on multiple measures of income distribution, including the Gini, Theil, and Atkin
View details →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
View details →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.
View details →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,
View details →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…
View details →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
View details →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
View details →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
View details →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
View details →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…
View details →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…
View details →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…
View details →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…
View details →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…
View details →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
View details →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
View details →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
View details →Di Wu, Yifei Jia, Siyuan Li et al. · 2025 · arXiv
Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, existing decoding approaches largely remain constrained to single-task scenarios and individual subjects, limiting their broader applicability and generalizability. Efforts towards creating large-scale neurophysiological foundation models have shown promise, but continue to struggle with significant challenges due to pervasive data heterogeneity across subjects and decoding tasks. Simply increasing model parameters and da
View details →Arjun D. Desai, Francesco Caliva, Claudia Iriondo et al. · 2020 · arXiv
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Simil
View details →George Sterpu, Christian Saam, Naomi Harte · 2020 · arXiv
Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and
View details →Nikhil Raghav, Subhajit Saha, Md Sahidullah et al. · 2024 · arXiv
In this report, we describe the speaker diarization (SD) and language diarization (LD) systems developed by our team for the Second DISPLACE Challenge, 2024. Our contributions were dedicated to Track 1 for SD and Track 2 for LD in multilingual and multi-speaker scenarios. We investigated different speech enhancement techniques, voice activity detection (VAD) techniques, unsupervised domain categorization, and neural embedding extraction architectures. We also exploited the fusion of various embedding extraction models. We implemented our system with the open-source SpeechBrain toolkit. Our fin
View details →Ramin Babaee, Shahab Oveis Gharan, Martin Bouchard · 2025 · arXiv
We propose a novel digital-to-analog converter (DAC) weighting architecture that statistically minimizes the distortion caused by random timing mismatches among current sources. To decode the DAC input codewords into corresponding DAC switches, we present three algorithms with varying computational complexities. We perform high-level Matlab simulations to illustrate the dynamic performance improvement over the segmented structure.
View details →Daniele Gerosa, Thomas Eriksson · 2026 · arXiv
Motivated by recent developments in stochastic modeling of clock jitter in Analog-to-Digital Converters (ADCs) as autoregressive processes of order one (AR(1)), we study the density and stability properties of AR(1)-jittered sampling sets for Paley-Wiener signals. We show that, despite having the correct asymptotic density both on average and almost surely, such sets almost surely fail to be stable sampling sets. We complement this negative result with a finite-dimensional analysis, showing that the corresponding jittered sinc matrices are nonetheless well-conditioned with high probability.
View details →Ezra Tampubolon, Haris Ceribasic, Holger Boche · 2020 · arXiv
Congestion game is a widely used model for modern networked applications. A central issue in such applications is that the selfish behavior of the participants may result in resource overloading and negative externalities for the system participants. In this work, we propose a pricing mechanism that guarantees the sub-linear increase of the time-cumulative violation of the resource load constraints. The feature of our method is that it is resource-centric in the sense that it depends on the congestion state of the resources and not on specific characteristics of the system participants. This f
View details →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
View details →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
View details →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
View details →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
View details →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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