Search Results (2,318)

A new statistical control chart denoted as CHEWMA (Cultural Heritage EWMA) is proposed for microclimate monitoring in preventive conservation. This tool is a real-time detection method inspired by the EN 15757:2010 standard, serving as an alternative to its common adaptations. The proposed control chart is intended to detect short-term fluctuations (STFs) in temperature (T) and relative humidity (RH), which would enable timely interventions to mitigate the risk of mechanical damage to collections. The CHEWMA chart integrates the Exponentially Weighted Moving Average (EWMA) control chart with a weighting mechanism that prioritizes fluctuations occurring near extreme values. The methodology was validated using RH time series recorded by seven dataloggers installed at the Alava Fine Arts Museum, and, from these, seventy simulated time series were generated to enhance the robustness of the analyses. Sensitivity analyses demonstrated that, for the studied dataset, the CHEWMA chart exhibits stronger similarity to the application of EN 15757:2010 than other commonly used real-time STF detection methods in the literature. Furthermore, it provides a flexible option for real-time applications, enabling adaptation to specific conservation needs while remaining aligned with the general framework established by the standard. To the best of our knowledge, this is the first statistical process control chart designed for the field of preventive conservation of cultural heritage. Beyond assessing CHEWMA’s performance, this study reveals that, when adapting the procedures of the European norm by developing a new real-time approach based on a simple moving average (herein termed SMA-FT), a window of approximately 14 days is more appropriate for STF detection than the commonly assumed 30-day period in the literature. Full article

Piezometers located near watercourses experiencing periodic fluctuations provide a means to analyse soil properties and derive key hydrogeological parameters through pressure wave transmission analysis, which is affected in amplitude and time (lag). These techniques are invaluable for hydrogeological characterizations, such as assessing pollutant diffusion, conducting construction projects below the water table, and evaluating flood zones. While traditionally applied to study tidal influences in coastal areas, this research introduces their application to channels indirectly affected by tidal oscillations due to downstream confluences with tidal waterways. This innovative approach combines the analysis of tidal barriers with the effects of storms and droughts. This study synthesises findings from an experimental monitoring field equipped with advanced recording technologies, allowing for high-resolution, long-term analysis. The dataset, spanning dry periods, major storms, and channel overflows, offers unprecedented precision and insight into aquifer responses. This study analyses the application of wave transmission calculations using continuous level recording in a river and in observation piezometers. Two methods of analysis are applied to the series generated, one based on the variation in the amplitude and the other based on the phase shift produced by the transmission of the wave through the aquifer, both related to the hydrogeological characteristics of the medium. This study concludes that the determination of the fluctuation period is key in the calculation, being particularly more precise in the analysis of the amplitude than in the analysis of the phase difference, which has led to disparate results in previous studies. The results obtained make it possible to reconstruct and extrapolate real or calculated series of rivers and piezometers as a function of distance from the diffusivity obtained. Using the fluctuation period and diffusivity, it is possible to construct the wave associated with any event based on data from just one river or piezometer. Full article

In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail to predict the erosion rate of concrete (CER) structures accurately. This issue arises from oversimplified assumptions and the failure to account for environmental variations and complex nonlinear relationships between parameters. Consequently, a single traditional model is inadequate for predicting the CER under wind gravel flow conditions in this region. To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R² value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. Ensuring that the training set comprises at least 80% of the total data volume is crucial for the SVM model’s prediction accuracy. Moreover, erosion time is identified as the most significant factor affecting the CER. An enhanced theoretical erosion model, derived from the Bitter and Oka framework and integrating concrete strength and erosion parameters, was formulated. It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. The SVM model, however, recorded a minimal average relative error of just −0.5%, markedly surpassing these improved theoretical models in terms of prediction accuracy. Theoretical models often rely on simplifying assumptions, such as linear relationships and homogeneous material properties. In practice, however, factors like concrete materials, wind gravel flow, and climate change are nonlinear and non-homogeneous. This significantly limits the applicability of these models in real-world environments. Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. This model is crucial for safeguarding concrete structures in wind gravel flow environments. Full article

This study evaluates the potential of integrating visible and near-infrared (visNIR) spectroscopy, color analysis, and firmness testing for non-destructive tomato quality assessment and shelf-life prediction. Tomato fruit (cv. HM1823) harvested at four ripening stages were monitored over 12 days at 22 °C to investigate ripening stage-specific variations in key quality parameters, including color (hue angle), firmness (compression), and nutritional composition (pH, soluble solids content, and titratable acidity ratio). Significant changes in these parameters during storage highlighted the need for advanced tools to monitor and predict quality attributes. Spectral data (340–2500 nm) captured using advanced and cost-effective portable spectroradiometers, coupled with chemometric models such as partial least squares regression (PLSR), demonstrated reliable predictions of shelf-life and nutritional quality. The near-infrared spectrum (900–1700 nm) was particularly effective, with variable selection methods such as genetic algorithm (GA) and variable importance in projection (VIP) scores enhancing model accuracy. This study highlights the promising role of visNIR spectroscopy as a rapid, non-destructive tool for optimizing postharvest management in tomato. By enabling real-time quality assessments, these technologies support sustainable agricultural practices through improved decision-making, reduced postharvest losses, and enhanced consumer satisfaction. The findings also validate the utility of affordable spectroradiometers, offering practical solutions for stakeholders aiming to balance cost efficiency and reliability in postharvest quality monitoring. Full article

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas. Full article

Peak Ground Acceleration (PGA) is a measure of the maximum ground shaking intensity during an earthquake. The estimation of PGA in areas affected by earthquakes is a fundamental task in seismic hazard assessment and emergency response. This paper presents an automated service capable of rapidly calculating the PGA’s values in regions impacted by seismic events and publishing its results on an interactive website. The importance of such a service is discussed, focusing on its contribution to timely response efforts and infrastructure resilience. The necessity for automatic and real-time systems in earthquake-prone areas is emphasized, enabling decision-makers to assess damage potential and deploy resources efficiently. Thanks to a collaboration agreement with the Civil Protection Department, we are able to acquire accelerometric data from the Italian National Accelerometric Network (RAN) in real time at the monitoring center of the Osservatorio Vesuviano. These data, in addition to those normally acquired by the INGV network, enable us to utilize all available accelerometric data in the Campi Flegrei area, enhancing our capacity to provide timely and accurate PGA estimates during seismic events in this highly active volcanic region. Full article

The taxiing stage of an aircraft is characterized by its long duration, low operating thrust, and low combustion efficiency, resulting in substantial emissions of CO, CO₂, and VOCs, which adversely affect air quality near airports. This study has developed an open-path Fourier transform infrared spectroscopy (OP-FTIR) monitor with second-level time resolution to enable the optical remote monitoring of pollutants during taxiing. Measurements of CO, CO₂, and VOCs were conducted over one month at Hefei Xinqiao International Airport (HXIA). The generalized additive model (GAM) is used for data analysis to reveal complex nonlinear relationships between aircraft emission concentrations and meteorological factors, aircraft models, and their corresponding registration numbers. The GAM analysis shows that among meteorological factors, humidity, and atmospheric pressure have the most significant impact on aircraft exhaust monitoring, with a relative average contribution value as high as approximately six. The explanatory power of aircraft models for emissions is low (R² < 0.18), whereas that of registration numbers is high (R² > 0.6), suggesting that individual differences between aircrafts play a crucial role in emission concentration variations. Furthermore, a noticeable correlation was found between the CO/CO₂ ratio and volatile organic compound (VOC) concentrations (R² > 0.63), indicating that combustion efficiency significantly affects VOC emissions. This study not only advances the real-time remote sensing monitoring of pollutants during aircraft taxiing but also underscores the crucial role of the GAM in identifying the key drivers of emissions, providing a scientific basis for precise environmental protection management and policy-making. Full article

The rapid advancement of edge computing and Tiny Machine Learning (TinyML) has created new opportunities for deploying intelligence in resource-constrained environments. With the growing demand for intelligent Internet of Things (IoT) devices that can efficiently process complex data in real-time, there is an urgent need for innovative optimisation techniques that overcome the limitations of IoT devices and enable accurate and efficient computations. This study investigates a novel approach to optimising Convolutional Neural Network (CNN) models for Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT), which requires complex signal processing, energy efficiency, and real-time processing, by simultaneously reducing input complexity and using advanced model compression techniques. By systematically reducing and halving the input complexity of a 1D CNN from 40 to 20 Boundary Voltages (BVs) and applying an innovative compression method, we achieved remarkable model size reductions of 91.75% and 97.49% for 40 and 20 BVs EIT inputs, respectively. Additionally, the Floating-Point operations (FLOPs) are significantly reduced, by more than 99% in both cases. These reductions have been achieved with a minimal loss of accuracy, maintaining the performance of 97.22% and 94.44% for 40 and 20 BVs inputs, respectively. The most significant result is the 20 BVs compressed model. In fact, at only 8.73 kB and a remarkable 94.44% accuracy, our model demonstrates the potential of intelligent design strategies in creating ultra-lightweight, high-performance CNN-based solutions for resource-constrained devices with near-full performance capabilities specifically for the case of HGR based on EIT inputs. Full article

Indocyanine green (ICG), a near-infrared (NIR) fluorescent dye with unique photoluminescent properties, is a helpful tool in many medical applications. ICG produces fluorescence when excited by NIR light, enabling accurate tissue visualization and real-time imaging. This study investigates the fundamental processes behind ICG’s photoluminescence as well as its present and possible applications in treatments and medical diagnostics. Fluorescence-guided surgery (FGS) has been transformed by ICG’s capacity to visualize tumors, highlight blood flow, and facilitate lymphatic mapping, all of which have improved surgical accuracy and patient outcomes. Furthermore, the fluorescence of the dye is being studied for new therapeutic approaches, like photothermal therapy, in which NIR light can activate ICG to target and destroy cancer cells. We go over the benefits and drawbacks of ICG’s photoluminescent qualities in therapeutic contexts, as well as current studies that focus on improving its effectiveness, security, and adaptability. More precise disease detection, real-time monitoring, and tailored therapy options across a variety of medical specialties are made possible by the ongoing advancement of ICG-based imaging methods and therapies. In the main part of our work, we strive to take into account the latest reports; therefore, we used clinical articles going back to 2020. However, for the sake of the theoretical part, the oldest article used by us is from 1995. Full article

The dynamic response of light bridges to moving loads presents significant challenges in controlling vibrations that can impact on the structural integrity and the user comfort. This study investigates the effectiveness of nonlinear semi-active absorbers in mitigating these vibrations on light bridges that are particularly susceptible to human-induced vibrations, due to their inherent low damping and flexibility, especially under near-resonance conditions. Traditional passive vibration control methods, such as dynamic vibration absorbers (DVAs), may not be entirely adequate for mitigating vibrations, as they require adjustments in damping and stiffness when operating conditions change over time. Therefore, suitable strategies are needed to dynamically adapt DVA parameters and ensure optimal performance. This paper explores the effectiveness of linear and nonlinear DVAs in reducing vertical vibrations of lightweight beams subjected to moving loads. Using the Bubnov-Galerkin method, the governing partial differential equations are reduced to a set of ordinary differential equations and a novel nonlinear DVA with a variable damping dashpot is investigated, showing better performances compared to traditional constant-parameter DVAs. The nonlinear viscous damping device enables real-time adjustments, making the DVA semi-active and more effective. A footbridge case study demonstrates significant vibration reductions using optimized nonlinear DVAs for lightweight bridges, showing broader frequency effectiveness than linear ones. The quadratic nonlinear DVA is the most efficient, achieving a 92% deflection reduction in the 1.5–2.5 Hz range, and under running and jumping reduces deflection by 42%. Full article

This work investigates a new erase scheme in NAND flash memory to improve the lifetime and performance of modern solid-state drives (SSDs). In NAND flash memory, an erase operation applies a high voltage (e.g., >20 V) to flash cells for a long time (e.g., >3.5 ms), which degrades cell endurance and potentially delays user I/O requests. While a large body of prior work has proposed various techniques to mitigate the negative impact of erase operations, no work has yet investigated how erase latency and voltage should be set to fully exploit the potential of NAND flash memory; most existing techniques use a fixed latency and voltage for every erase operation, which is set to cover the worst-case operating conditions. To address this, we propose Revisiting Erase Operation, (REO) a new erase scheme that dynamically adjusts erase latency and voltage depending on the cells’ current erase characteristics. We design REO by two key apporaches. First, REO accurately predicts such near-optimal erase latency based on the number of fail bits during an erase operation. To maximize its benefits, REO aggressively yet safely reduces erase latency by leveraging a large reliability margin present in modern SSDs. Second, REO applies near-optimal erase voltage to each WL based on its unique erase characteristics. We demonstrate the feasibility and reliability of REO using 160 real 3D NAND flash chips, showing that it enhances SSD lifetime over the conventional erase scheme by 43% without change to existing NAND flash chips. Our system-level evaluation using eleven real-world workloads shows that an REO-enabled SSD reduces average I/O performance and read tail latency by 12% and 38%, respectivley, on average over a state-of-the-art technique. Full article

The 3D reconstruction of construction sites is of great importance for construction progress, quality, and safety management. Currently, most of the existing 3D reconstruction methods are unable to conduct continuous and uninterrupted perception, and it is difficult to achieve registration with real coordinates and dimensions. This study proposes a hierarchical registration framework for 3D reconstruction of construction sites based on surveillance cameras. This method can quickly perform on-site 3D reconstruction and restoration by taking surveillance camera images as inputs. It combines 2D and 3D features and does not need transfer learning or camera calibration. By experimenting on one construction site, we found that this framework can complete the 3D point cloud estimation and registration of construction sites within an average of 3.105 s through surveillance images. The average RMSE of the point cloud within the site is 0.358 m, which is better than most point cloud registration methods. Through this method, 3D data within the scope of surveillance cameras can be quickly obtained, and the connection between 2D and 3D can be effectively established. Combined with visual information, it is beneficial to the digital twin management of construction sites. Full article

Reference precipitation (RP) serves as a benchmark for evaluating the accuracy of precipitation products; thus, the selection of RP considerably affects the evaluation. In order to quantify this impact and provide guidance for RP selection, three interpolation methods, namely inverse distance weighting (IDW), ordinary kriging (OK), and geographical weighted regression (GWR), along with six groups of station densities, were adopted to generate different RPs, based on the super-high-density rainfall observations as true values, and we analyzed the errors of different RPs and the impacts of RP selection on the assessment of GPM precipitation products. Results indicate that the RPs from IDW and GWR both approached the true values as the station density increased (CC > 0.90); while the RP from OK showed some differences (CC < 0.80), it was similar to GWR when the station density was low, but the accuracy improved at first and then worsened as the station density continued to increase; the evaluation results based on different RPs showed remarkable differences even under the same conditions; when the average distance between rainfall gauges that were utilized to generate RPs was below the medium value (i.e., d < 20 km), the evaluation based on RP derived from IDW and GWR was close enough to that based on the true precipitation, which indicates its feasibility in evaluating satellite precipitation products. Full article

High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks. Full article

Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are limited, and real-time processing is essential. In this article, we present a novel FPGA-based hardware acceleration of a near-lossless compression technique for hyperspectral images by leveraging a division-free quadrature-based square rooting method. In this regard, the two division operations inherent in the original approach were replaced with pre-computed reciprocals, multiplications, and a geometric series expansion. Optimized for real-time applications, the synthesis results show that our approach achieves a high throughput of 1611.77 Mega Samples per second (MSps) and a low power requirement of 0.886 Watts on the economical Cyclone V FPGA. This results in an efficiency of 1819.15 MSps/Watt, which, to the best of our knowledge, surpasses recent state-of-the-art hardware implementations in the context of near-lossless compression of hyperspectral images. Full article