Search Results (231)

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1 + 1)-ES Sandpile model can effectively reconstruct source parameters, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications. Full article

The deployment of airborne hyperspectral sensors has expanded rapidly, driven by their ability to capture spectral information beyond the visual range and to reveal objects that remain obscured in conventional imaging. In scenarios where prior target signatures are unavailable, anomaly detection provides an effective alternative by identifying deviations from the spectral background. However, real-world reconnaissance and monitoring missions frequently take place in complex and dynamic environments, requiring anomaly detectors to demonstrate robustness and adaptability. These requirements have rarely been met in current research, as evaluations are still predominantly based on small, context-restricted datasets, offering only limited insights into detector performance under varying conditions. To address this gap, we propose a context-adaptive hyperspectral sensor and perception management (hSPM) architecture that integrates sensor context extraction, band selection, and detector management into a single adaptive processing pipeline. The architecture is systematically evaluated on a new, large-scale airborne hyperspectral dataset comprising more than 1100 annotated samples from two diverse test environments, which we publicly release to support future research. Comparative experiments against state-of-the-art anomaly detectors demonstrate that conventional methods often lack robustness and efficiency, while hSPM consistently achieves superior detection accuracy and faster processing. Depending on evaluation conditions, hSPM improves anomaly detection performance by 28–204% while reducing computation time by 70–99%. These results highlight the advantages of adaptive sensor processing architectures and underscore the importance of large, openly available datasets for advancing robust airborne hyperspectral anomaly detection. Full article

This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions. Unlike traditional analytical methods, which are computationally intensive and often impractical in real time, the proposed system leverages the flexibility of multilayer perceptrons to model both forward and inverse relationships between drop conditions and flight outcomes. In the forward stage, a trained network predicts range, flight time, and impact velocity from predefined release parameters. In the inverse stage, a deeper neural model reconstructs the required release velocity, angle, and altitude directly from the desired operational outcomes. By employing a hybrid workflow—combining physics-based simulation with neural approximation—our approach generates large, high-quality datasets at low computational cost. Results demonstrate that the inverse network achieves high accuracy across deterministic and stochastic tests, with minimal error when operating within the training domain. The study confirms the suitability of neural architectures for tackling complex, nonlinear identification tasks in precision airdrop operations. Beyond their technical efficiency, such models enable agile, GPS-independent mission planning, offering a reliable and low-cost decision support tool for humanitarian aid, scientific research, and defense logistics. This work highlights how artificial intelligence can transform conventional trajectory design into a fast, adaptive, and autonomous capability. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

The utilization of sugarcane molasses (SCM), a byproduct of sugar refining, offers a promising bio-based alternative to conventional chemical admixtures in cementitious systems. This study investigates the effects of SCM at five dosage levels, 0.25%, 0.50%, 0.75%, 1.00%, and 1.25% by weight of cement, on cement mortar performance across fresh, mechanical, thermal, durability, and density criteria. A comprehensive experimental methodology was employed, including flow table testing, compressive strength (7, 14, and 28 days) and flexural strength measurements, embedded thermal sensors for real-time hydration monitoring, water absorption and chloride ion penetration tests, as well as 28-day density determination. Results revealed clear dose-dependent behavior, with SCM enhancing mortar flowability proportional to dosage, raising the spread diameter from 11.5 cm (control) to 20 cm at 1.25%. At 0.25% SCM, compressive strength (47.5 MPa at 28 days) and flexural strength (~2.9 MPa) were higher than those of the remaining SCM dosages, supported by sustained heat release and positive temperature differentials. However, dosages ≥ 0.5% drastically suppressed hydration kinetics and mechanical performance, with compressive strength falling below 10 MPa. Furthermore, high SCM content led to increased water absorption (up to 10.6%) and chloride permeability (CIP above 5100 C), while bulk density declined from 2250 kg/m³ to 2080 kg/m³ at 1.25% SCM. Statistical validation using one-way ANOVA confirmed that these differences across dosage levels were significant (p < 0.05), underscoring the importance of dosage optimization. This investigation confirms that low-dosage SCM (≤0.25%) can be an effective bio-additive, providing improved workability with negligible compromise in strength and durability. In contrast, higher dosages undermine matrix integrity and performance. Future work is recommended to assess long-term microstructural evolution, field exposure durability, and adaptability across diverse cementitious systems. Full article

Nowadays, Internet of Things (IoT) application systems are broadly applied to various sectors of society for efficient management by monitoring environments using sensors, analyzing sampled data, and giving proper feedback. For their fast deployment, we have developed Smart Environmental Monitoring and Analysis in Real Time (SEMAR) as an integrated IoT application server platform and implemented the input setup assistance service using prompt engineering and a generative AI model to assist connecting sensors to SEMAR with step-by-step guidance. However, the current service cannot assist in connections of the sensors not learned by the AI model, such as newly released ones. To address this issue, in this paper, we propose an extension to the service for handling unlearned sensors by utilizing datasheets with four steps: (1) users input a PDF datasheet containing information about the sensor, (2) key specifications are extracted from the datasheet and structured into markdown format using a generative AI, (3) this data is saved to a vector database using chunking and embedding methods, and (4) the data is used in Retrieval-Augmented Generation (RAG) to provide additional context when guiding users through sensor setup. Our evaluation with five generative AI models shows that OpenAI’s GPT-4o achieves the highest accuracy in extracting specifications from PDF datasheets and the best answer relevancy (0.987), while Gemini 2.0 Flash delivers the most balanced results, with the highest overall RAGAs score (0.76). Other models produced competitive but mixed outcomes, averaging 0.74 across metrics. The step-by-step guidance function achieved a task success rate above 80%. In a course evaluation by 48 students, the system improved the student test scores, further confirming the effectiveness of our proposed extension. Full article

The instability of many volatile organic compounds (VOCs) limits their usage in different fragrance carriers and products. In scratch-and-sniff applications, VOCs are bound so strongly that release cannot happen without an external trigger. On the other hand, other fixatives like cyclodextrins release unstable volatile molecules too rapidly. We engineered biodegradable gelatin films whose release profile can be tuned by glycerol plasticization and alkaline protease degradation. Digitalized VOC release profiles acquired with the described near-real-time analysis toolkit are digital twins that replicate the behavior of the evaluated films in silico. Seven formulations were cast from 10% gelatin containing D-limonene, glycerol (5%, 20%), protease-C 30 kU mL⁻¹, and samples with additional water to establish a higher hydromodule for protease catalytic activity. Release profiles were monitored for nine days at 23 ± 2 °C in parallel by metal-oxide semiconductor (MOS) e-noses, gravimetric weight loss, and near-infrared measurements (NIR). These continuous measurements were cross-checked with gel electrophoresis, FTIR spectroscopy, hardness tests, and sensory intensity ratings. Results showed acceleration of VOC release by enzymatic treatment during the first days, as well as overall impact on the release profile. Differences in low and high glycerol films were observed, and principal component analysis of NIR spectra separated low and high glycerol groups, mirroring the MOS and FTIR data. Usability of MOS data was explored in comparison to more biased and subjective intensity results from sensory panel evaluation. Overall, the created toolkit showed good cross-checked results and enabled the possibility for close to real-time analysis for bio-based VOC carriers. Full article

Accurately characterizing the energy evolution during rock failure is crucial in understanding instability mechanisms and enabling the real-time monitoring and early warning of geological hazards in mining and geotechnical engineering. However, the energy evolution characteristics and correlations of multi-physics signals like acoustic emission (AE) and infrared radiation (IR) require further investigation. This study specifically investigated the energy evolution of AE and IR and their correlation during the uniaxial compression failure process of phosphate rock. Tests were performed on specimens under different loading rates to analyze energy dissipation and damage progression. Based on damage mechanics theory, damage evolution models were developed to describe the relationship between the cumulative AE energy, IR radiation variations (specifically the change in the average infrared radiation temperature, ΔAIRT), and strain under varying loading conditions. The results indicate that the loading rate significantly influences the energy release mechanism, with higher rates intensifying rock damage. The peak AE energy rate coincides with the inflection point of the cumulative energy curve, marking substantial internal energy release at failure. Additionally, as the loading rate increases, high-temperature regions in IR thermograms appear earlier, while the variation in ΔAIRT follows a decreasing trend. From an energy perspective, the correlation between AE ringing counts and the average IR temperature was analyzed at both the precursor and failure stages, revealing a strong relationship between AE activity and thermal energy dissipation. Furthermore, mathematical expressions for rock damage variables and coupled relationship equations were derived and validated using experimental data, yielding correlation coefficients (R²) exceeding 0.92. These findings provide a theoretical and methodological foundation for the development of enhanced real-time rock monitoring and early warning systems, contributing to improved safety in geological and mining engineering. Full article

Endotoxins, lipopolysaccharides released from the outer membrane of Gram-negative bacteria, pose a significant risk in healthcare environments, particularly in Central Sterile Supply Departments (CSSDs), where the delivery of sterile pyrogen-free medical devices is critical for patient safety. Traditional methods for controlling endotoxins in water systems, such as ultraviolet (UV) disinfection, have proven ineffective at reducing endotoxin concentrations to comply with regulatory standards (<0.25 EU/mL). This limitation presents a significant challenge, especially in the context of reverse osmosis (RO) permeate used in CSSDs, where water typically has very low conductivity. Despite the established importance of endotoxin removal, a gap in the literature exists regarding effective chemical-free methods that can meet the stringent endotoxin limits in such low-conductivity environments. This study addresses this gap by evaluating the effectiveness of the eBooster™ electrochemical technology—featuring proprietary electrode materials and a reactor design optimized for potable water—for endotoxin removal from water, specifically under the low-conductivity conditions typical of RO permeate. Laboratory experiments using the B250 reactor achieved >90% endotoxin reduction (from 1.2 EU/mL to <0.1 EU/mL) at flow rates ≤5 L/min and current densities of 0.45–2.7 mA/cm². Additional real-world testing at three hospitals showed that the eBooster™ unit, when installed in the RO tank recirculation loop, consistently reduced endotoxin levels from 0.76 EU/mL (with UV) to <0.05 EU/mL over 24 months of operation, while heterotrophic plate counts dropped from 190 to <1 CFU/100 mL. Statistical analysis confirmed the reproducibility and flow-rate dependence of the removal efficiency. Limitations observed included reduced efficacy at higher flow rates, the need for sufficient residence time, and a temporary performance decline after two years due to a power fault, which was promptly corrected. Compared to earlier approaches, eBooster™ demonstrated superior performance in low-conductivity environments without added chemicals or significant maintenance. These findings highlight the strength and novelty of eBooster™ as a reliable, chemical-free, and maintenance-friendly alternative to traditional UV disinfection systems, offering a promising solution for critical water treatment applications in healthcare environments. Full article

Three-dimensional (3D) concrete printing (3DCP) has gained significant attention over the last decade due to its many claimed benefits. The absence of effective real-time quality control mechanisms, however, can lead to inconsistencies in extrusion, compromising the integrity of 3D-printed structures. Although the importance of quality control in 3DCP is broadly acknowledged, research lacks systematic methods. This research investigates the feasibility of using ultrasonic pulse velocity (UPV) as a practical, in situ, real-time monitoring tool for 3DCP. Two different groups of binders were investigated: limestone calcined clay (LC³) and zeolite-based mixes in binary and ternary blends. Filaments of 200 mm were extruded every 5 min, and UPV, pocket hand vane, flow table, and viscometer tests were performed to measure pulse velocity, shear strength, relative deformation, yield stress, and plastic viscosity, respectively, in the fresh state. Once the filaments presented printing defects (e.g., filament tearing, filament width reduction), the tests were concluded, and the open time was recorded. Isothermal calorimetry tests were conducted to obtain the initial heat release and reactivity of the supplementary cementitious materials (SCMs). Results showed a strong correlation (R² = 0.93) between UPV and initial heat release, indicating that early hydration (ettringite formation) influenced UPV and determined printability across different mixes. No correlation was observed between the other tests and hydration kinetics. UPV demonstrated potential as a real-time monitoring tool, provided the mix-specific pulse velocity is established beforehand. Further research is needed to evaluate UPV performance during active printing when there is an active flow through the printer. Full article

This study investigates the cracking behavior of high-performance calcium oxide-activated concrete incorporating basalt and synthetic macro fibers under compressive and flexural loading. Acoustic emission (AE) monitoring was employed to capture real-time crack initiation and propagation, offering insights into damage evolution mechanisms. A comprehensive series of uniaxial compression and four-point bending tests were conducted on fiber-reinforced and plain specimens. AE parameters, including count, duration, risetime, amplitude, and signal energy, were analyzed to quantify crack intensity and classify fracture modes. The results showed that tensile cracking dominated even under compressive loading due to lateral stresses, while fiber inclusion significantly enhanced toughness by promoting distributed microcracking and reducing abrupt energy release. Basalt fibers were particularly effective under flexural loading, increasing the post-peak load-bearing capacity, whereas synthetic macro fibers excelled in minimizing tensile crack occurrence under compression. Full article

Previous research has demonstrated sex-specific differences in muscle cells regarding sex hormone release and steroidogenic enzyme expression after testosterone exposure. The present study aims to elucidate sex-related differences in intracellular processes involved in myogenesis and regeneration. Neonatal 46XX and 46XY human primary skeletal muscle cells were treated with increasing doses of testosterone (0.5, 2, 5, 10, 32, and 100 nM) for 24 h. The molecular pathways involved in muscle metabolism and growth, as well as the release of myokines involved in satellite cell activation, were analyzed using western blot, real-time PCR, and a Luminex assay. The unpaired Student’s t-test and one-way ANOVA for repeated measures were used to determine significant variations within and between groups. An increase in the expression and release of MYF6, IGF-I, IGF-II, and CXCL1, as well as a decrease in GM-CSF, IL-9, and IL-12, was observed in 46XX cells. Conversely, testosterone up-regulated GM-CSF and CXCL1 in 46XY cells but did not affect the release of the other myokines. Preferential activation of the MAPK pathway was observed in 46XX cells, while the PI3K/AKT pathway was preferentially activated in 46XY cells. In conclusion, our findings demonstrate differential responses to androgen exposure in 46XX and 46XY cells, resulting in the activation of muscle cell growth and energy metabolic pathways in a sex-specific manner. Full article

The Lunar Augmented Navigation Service (LANS) is the lunar equivalent of GNSS for future lunar explorations. It offers users accurate position, navigation, and timing (PNT) capabilities on and around the Moon. The Augmented Forward Signal (AFS) is a standardized signal structure for LANS, and its recommended standard was published online on 7 February 2025. This work presents software-defined radio (SDR) implementations of the LANS AFS simulator and receiver, which were rapidly developed within a month of the signal specification release. Based on open-source GNSS software, including GPS-SDR-SIM and Pocket SDR, our system provides a valuable platform for future algorithm research and hardware-in-the-loop testing. The receiver can operate on embedded platforms, such as the Raspberry Pi 5, in real-time. This feature makes it suitable for lunar surface applications, where conventional PC-based SDR systems are impractical due to their size, weight, and power requirements. Our approach demonstrates how open-source SDR frameworks can be rapidly applied to emerging satellite navigation signals, even for extraterrestrial PNT applications. Full article

Compared to high-cost hardware-based Global Navigation Satellite System (GNSS) positioning techniques, smartphone-based precise positioning technology plays an important role in applications such as the Internet of Things (IoT). Since Google released the Nougat version of Android in 2016, this has provided a new method for achieving high-accuracy positioning solutions with a smartphone. However, two factors are limiting smartphone-based high-accuracy applications, namely, real-time precise orbit/clock products without the internet and the quality-adaptive precise point positioning (PPP) model. To overcome these two factors, we introduce BDS PPP-B2b orbit/clock corrections and a hybrid weight function (based on C/N₀ and satellite elevation) into smartphone real-time PPP. To validate the performance of such a method, two sets of field tests were arranged to collect the smartphone’s GNSS measurements and PPP-B2b orbit/clock corrections. The results illustrated that the hybrid weight function led to 5.13%, 18.00%, and 15.15% positioning improvements compared to the results of the C/N₀-dependent model in the east, north, and vertical components, and it exhibited improvements of 71.10%, 72.53%, and 53.93% compared to the results of the satellite-elevation-angle-dependent model. Moreover, the mixed-frequency measurement PPP model could also provide positioning improvements of about 14.63%, 19.99%, and 9.21%. On average, the presented smartphone PPP model can bring about 76.64% and 59.84% positioning enhancements in the horizontal and vertical components. Full article

A technique has been proposed and experimentally tested for measuring the hydrogen concentration in an inert atmosphere within a closed system. This method utilizes a metal-oxide-semiconductor field-effect capacity-type (MOSFEC) sensor under harsh conditions such as exposure to inert gases, pressure fluctuations, and varying temperatures. The measurement is performed during the thermal decomposition of metal hydrides in a liquid sodium environment. The developed measurement technique for determining hydrogen concentration released from metal hydride samples in a system with a closed gas path is cost-effective compared to standardized, resource-intensive open-volume flow measurement methods. The use of the developed MOSFEC sensor technique allows for rapid and efficient investigation of the in situ real-time dynamics of gas release from various metal hydride materials differing in their hydrogen content within a small closed volume. Additionally, this approach enables precise determination of the specific gas release temperatures. Full article