Search Results (614)

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

Flexible magnetic sensors have become a hot research topic due to their non-contact human–machine interaction capabilities in areas such as motion recognition and posture detection of intelligent robots, virtual reality (VR) space reconstruction, and the Internet of Things. This study proposes a flexible, low-power three-dimensional (3D) magneto-impedance (MI) sensor based on a planar FeSiB/PI/graphene microcoil/PI/FeSiB heterostructure. Through the magneto-impedance effect of soft magnetic materials and the magnetoresistance effect of graphene under the synergistic modulation of weak current excitation, this sensor can decouple the magnetic field components in the X, Y, and Z directions with a single measurement, thus guaranteeing the real-time detection capability of a 3D magnetic field. Experimental results show that the proposed 3D magnetic sensor possesses the obvious advantages, such as the low power consumption of 76 μW, high resolutions of 31, 36, and 6992 nT/Hz^1/2 in the X, Y, and Z directions, respectively. Additionally, the magnetic sensor exhibits excellent anti-bending performance and can adapt to complex mechanical deformation environments. These characteristics endow it with great application potential in the field of intelligent wearable devices and provide new ideas for the future flexible electronics technology. Full article

This study presents a low-cost wireless sensor network (WSN) integrated with an Internet of Things (IoT) platform for continuous monitoring of agro-environmental parameters relevant to rice harvest decision support. Solar-powered sensor nodes equipped with temperature-humidity (DHT22) and light intensity (BH1750) sensors were deployed in a Pathum Thani 1 rice field in Si Prachan, Suphan Buri province, Thailand. Environmental data were recorded hourly from June to September 2025 and transmitted wirelessly to a cloud-based dashboard for real-time visualization. Growing Degree Days (GDD) were calculated from measured air temperature using a literature-based base temperature, and cumulative GDD (CGDD) was used to track rice growth progression across vegetative, reproductive, and grain-filling stages. The system demonstrated stable long-term operation and continuous data acquisition under field conditions. Observed CGDD trends were consistent with reported growth-stage thresholds for the studied rice variety, while measured light intensities ranged from 36,900 to 37,810 lx, relative humidity remained consistently high throughout the season, and air temperatures varied between daily minima of 23.5–25.2 °C and maxima near 35.4 °C, which are suitable for rice photosynthesis and development. The seasonal CGDD increased linearly to 580.3, 1189.9, 1593.7, and 2385.7 °C by the end of June, July, August, and September, respectively, exhibiting a strong linear relationship with days after 1 June 2025 (R² = 0.9999), which confirms stable thermal accumulation throughout the growing season. Full article

Although tungsten oxide (WO₃)-based NO₂ sensors have been extensively studied, achieving high sensitivity at low operating temperatures remains a significant challenge. To address this limitation, we designed a WO₃/SiNWs heterojunction-based sensor, fabricated through metal-assisted chemical etching followed by hydrothermal synthesis. Structural and morphological analyses confirm the uniform integration of WO₃ nanorods onto SiNWs and the establishment of an effective p–n junction. The optimized sensor exhibits a response of 238 toward 1 ppm NO₂ at 127 °C with a response/recovery times of 14.8 s/99.2 s. The improved performance stems from the heterojunction-driven enhancement of charge carrier separation and surface adsorption sites, offering a viable route for developing low-power, high-performance gas sensors. Full article

The carved lacquer horse-hoof-shaped box excavated from Yulin, Shaanxi Province, represents a typical example of lacquerware preservation in the arid environment of northern China, exhibiting multiple deterioration phenomena, including substrate deformation, lacquer film peeling, and pigment fading. To systematically analyze its structural composition and craftsmanship features, this study employed multiple analytical techniques, including ultra-depth microscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), confocal laser micro-Raman spectroscopy (Raman), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Based on these analyses, a targeted conservation protocol was developed. Results revealed that the carved lacquer horse-hoof-shaped box has a wooden substrate structure, with the lacquer ash layer composed of mixed materials, including calcium carbonate (CaCO₃), quartz (SiO₂), and hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂). The lacquer film layer contains Chinese lacquer and plant oils, with cinnabar applied as surface decoration. Based on these findings, a stratified reinforcement conservation strategy was proposed: under dynamic monitoring with optical fiber sensors and three-dimensional scanning, the wooden substrate was reinforced with moisture-curable polyurethane (MCPU), the lacquer ash layer was strengthened with acrylic emulsion (Primal AC33), aged areas were restored with nano calcium hydroxide (Ca(OH)₂) aqueous dispersion, and polyethylene glycol (PEG 400) poultice application was implemented to restore the flexibility of the lacquer film. This research significantly enhanced the integrity and stability of the carved lacquer horse-hoof-shaped box, providing practical evidence and technical references for the scientific conservation of lacquerware excavated from arid regions of northern China. Full article

As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform configuration, and vECU (Virtual Electronic Control Unit) execution into a single, repeatable development workflow. Control logic validated in Simulink is translated into AUTOSAR-compliant software, built into a QEMU (Quick EMUlator)-based vECU, and exercised in DRIM-SimHub using both virtual stimuli and a real sensor–actuator signal delivered through a dedicated I/O interface board. Using a seat–slide virtual limit controller as a representative case, the proposed workflow enables consistent reuse of the test scenarios across model-in-the-loop (MiL), software-in-the-loop (SiL), and virtual ECU stages, while preserving production-level timing behavior and the semantics of the AUTOSAR runtime. The experimental results show that the vECU accurately reproduces the PWM outputs, Hall sensor pulse timing, and limit–stop decisions of physical ECU, and that integration issues previously discovered only in HiL tests can be exposed much earlier. Overall, the workflow shortens verification cycles, improves the observability of timing-dependent behavior, and provides a practical basis for early validation in software-defined vehicle development. Full article

The integration of complementary metal–oxide–semiconductor (CMOS) and micro-electromechanical systems (MEMSs) technologies for miniaturized biosensor fabrication enables unprecedented spatiotemporal resolution in monitoring the bioelectrical activity of the nervous system. Wafer-level CMOS technology incurs high costs, but multi-project wafer (MPW) runs mitigate this by allowing multiple users to share a single wafer. Still, monolithic CMOS biosensors require specialized surface materials or device geometries incompatible with standard CMOS processes. Performing MEMS post-processing on the few square millimeters available in MPW dies remains a significant challenge. In this paper, we present a MEMS post-processing workflow tailored for CMOS dies that supports both surface material modification and layout shaping for intracortical biosensing applications. To address lithographic limitations on small substrates, we optimized spray-coating photolithography methods that suppress edge effects and enable reliable patterning and lift-off of diverse materials. We fabricated a needle-like, 512-channel simultaneous neural recording active pixel sensor (SiNAPS) technology based neural probe designed for integration with optical fibers for optogenetic studies. To mitigate photoelectric effects induced by light stimulation, we incorporated a photoelectric shield through simple modifications to the photolithography mask. Optical bench testing demonstrated >96% light-shielding effectiveness at 3 mW of light power applied directly to the probe electrodes. In vivo experiments confirmed the probe’s capability for high-resolution electrophysiological measurements. Full article

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across three ecologically distinct regions in British Columbia (Aleza Lake, Deception, and Eagle Hills). Random Forest regression models were calibrated using field-measured SI and a multistep variable-selection procedure that included Variance Inflation Factor (VIF) screening followed by model-based variable importance assessment. Model performance was evaluated using repeated 10-fold cross-validation. The combined ALS–Sentinel-2 models substantially outperformed single-source models, yielding cross-validated R² values of 0.63, 0.44, and 0.56 for Aleza Lake, Deception, and Eagle Hills, respectively, compared with R² values of 0.40, 0.40, and 0.46 for ALS-only models. Key predictors consistently included terrain metrics, such as the Topographic Position Index (TPI) and the Topographic Wetness Index (TWI), along with satellite-derived chlorophyll-sensitive indices including S2REP (Sentinel-2 red-edge position), MTCI (MERIS terrestrial chlorophyll), and GNDVI (Greenness Normalized Difference Vegetation Index). A general model using predictors common to all regions performed comparably (R² = 0.63, 0.41, 0.52), demonstrating the transferability and operational potential of the approach. These findings demonstrate that integrating ALS-derived terrain metrics with Sentinel-2 spectral indices provides a robust, age-independent framework for capturing spatial variability in forest productivity across landscapes. This multi-sensor fusion approach enhances traditional SI methods and single-sensor models, providing a scalable and operational tool for forest management and long-term planning in changing environmental conditions. Full article

This work presents an advanced electro-physical model for hydrogenated amorphous silicon (a-Si:H) Junction Field Effect Transistors (JFETs) to enable the design of devices with energy-efficient analog interface building blocks for Lab-on-Chip (LoC) systems. The presence of this device can support monolithic integration with thin-film sensors and circuit-level design through a validated compact formulation. The model accurately describes the behavior of a-Si:H JFETs addressing key physical phenomena, such as the channel thickness dependence on the gate-source voltage when the channel approaches full depletion. A comprehensive framework was developed, integrating experimental data and mathematical refinements to ensure robust predictions of JFET performance across operating regimes, including the transition toward full depletion and the associated current-limiting behavior. The model was validated through a broad set of fabricated devices, demonstrating excellent agreement with experimental data in both the linear and saturation regions. Specifically, the validation was carried out at 25 °C on 15 fabricated JFET configurations (12 nominally identical devices per configuration), using the mean characteristics of 9 devices with standard-deviation error bars. In the investigated bias range, the devices operate in a sub-µA regime (up to several hundred nA), which naturally supports µW-level dissipation for low-power interfaces. This work provides a compact, experimentally validated modeling basis for the design and optimization of a-Si:H JFET-based LoC front-end/readout circuits within technology-constrained and energy-efficient operating conditions. Full article

This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a calibrated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation results revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of “Software-in-the-Loop” (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable “green transport” goals and reducing emissions in congested urban nodes. Full article

We present a mixed-signal front-end ASIC designed for compact Silicon Photomultiplier (SiPM) sensor interfaces, implemented in the AMS 0.35 µm CMOS technology. The chip integrates two independent analog channels, each composed of five custom second-generation current conveyors (CCII⁺), a fast zero-crossing discriminator, and a peak-and-hold stage based on a tailored operational amplifier. The CCII⁺ and discriminator blocks were designed in-house, based on literature designs and adapted to the technology to ensure low input impedance and fast current-mode signal propagation. This architecture enables precise detection of small signals with reduced pile-up, important for time-resolved photon detection. Bias and threshold control are provided by programmable current mirrors and SPI-configurable DACs, including a 10-bit current-mode DAC based on a current-splitting structure with approximately 200 nA resolution. A custom SiPM behavioral model was developed in the Cadence environment to support design and simulation, reproducing realistic pulse shapes and recovery dynamics for timing applications. Circuit-level simulations confirm correct analog functionality and stable operation across the intended dynamic range, with a per-channel consumption of about 5.9 mA at 3.3 V (19.5 mW), reflecting a tradeoff between speed and robustness. The system is compatible with external timing architectures, while internal CCII⁺ stages ensure low-impedance current reception, fast discrimination, and accurate current-to-voltage conversion for peak detection. Full article

CubeSats are a fundamental tool of space exploration, allowing for the testing of novel ideas that can be upscaled to more efficient satellite systems. This work presents the development and characterisation of an additively manufactured aluminium mechanism designed to enable the self-functionalisation of CubeSat structures through material extrusion metal additive manufacturing, as a foundation for sensor integration. A space-grade AlSi7Mg alloy was selected and prepared as a filament to print a fully functional hinge geometry, aiming to evaluate the feasibility of producing movable metallic components using a low-cost and sustainable extrusion-based process. Produced parts were subjected to debinding and vacuum sintering, achieving a densification above 85% and an average hardness of 52.2 HV. Further characterisation, including micro-computed tomography, X-ray diffraction and dynamic mechanical analysis, was used to assess the microstructural integrity, present phase, and mechanical behaviour of the sintered components. The designed shrinkage-compensated hinge mechanism preserved its rotational mobility after sintering, validating the mechanical inter-locking strategy and the design for additive manufacturing methodology used. The results demonstrate that material extrusion enables the fabrication of lightweight, functional, and integrated aluminium mechanisms suitable for sensor incorporation and actuation in small satellite systems. This proof-of-concept highlights material extrusion as a sustainable and economically viable route for developing intelligent aero-space structures, paving the way for future adaptive and sensor-integrated CubeSat subsystems. Full article

This review summarizes the use of Auger Electron Spectroscopy (AES) for microchemical analysis of two different types of dielectric/(Al,Ga)N-based systems: (i) extrinsic dielectric PECVD SiO₂, ALD Al₂O₃, and ECR-CVD SiN_x films on Al_xGa_1−xN/GaN structures in the context of their application in microelectronic power devices and (ii) intrinsic Al₂O₃ films on AlN epitaxial layers grown by high-temperature oxidation for nanostructured technology of various gas/ion sensors. Particular attention is given to AES depth profiling across complete multilayer cross-sections, combining qualitative analysis of spectral line shape and intensity evolution as well as kinetic energy shifts with quantitative elemental depth distributions. This approach enables identification of chemical states and oxidation-related transformations at dielectric/semiconductor interfaces. Reported results demonstrate that AES provides micro- to nanometer-scale chemical information essential for distinguishing interfacial from the bulk properties. The capabilities and inherent limitations of AES depth profiling, including sputter-induced artifacts are also addressed, highlighting the role of optimized experimental conditions in reliable interface analysis. Full article

Population aging has intensified the prevalence of falls among older adults, making automatic Fall Detection Systems (FDS) a key component of telemonitoring and remote care. Among wearable-based approaches, inertial sensors, particularly accelerometers, offer an effective and low-cost alternative for continuous monitoring. However, the impact of the selection of the sampling frequency on model performance remains insufficiently explored. This work seeks to determine the sampling rate that best balances accuracy, stability, and computational efficiency in wearable FDS. Five representative algorithms (CNN-LSTM, CNN, LSTM-BN, k-NN, and SVM) were trained and evaluated using the SisFall dataset at 10, 20, 50, and 100 Hz, followed by a multi-stage validation including the real-fall repositories FARSEEING and Free From Falls, as well as a seven-day continuous monitoring test under real-life conditions. The results show that deep learning architectures consistently outperform traditional classifiers, with the CNN-LSTM model at 20 Hz achieving the best balance of accuracy (98.9%), sensitivity (96.7%), and specificity (99.6%), while maintaining stable performance across all validations. The observed consistency indicates that intermediate frequencies, around 20 Hz and down to 10 Hz, provide sufficient temporal resolution to capture fall dynamics while reducing data volume, which translates into more efficient energy usage compared to higher sampling rates. Overall, these findings establish a solid empirical foundation for designing next-generation wearable fall-detection systems that are more autonomous, robust, and sustainable in long-term IoT-based monitoring environments. Full article

The propagation of interfacial acoustic waves (IAWs) along a SiO₂/ZnO/SU-8/SiO₂ multilayer structure is theoretically predicted and experimentally validated. A two-dimensional finite-element analysis was performed using COMSOL Multiphysics, revealing that key IAW characteristics—such as the number of supported modes, propagation losses, and acoustic field distribution—are strongly influenced by the thickness of the intermediate SU-8 adhesive layer. In particular, the presence of the SU-8 layer enables the existence of IAW modes with opposite localization, namely upward- and downward-propagating IAWs. To validate the theoretical predictions, experimental measurements were carried out on delay lines fabricated on SiO₂/ZnO/SU-8/SiO₂ layered structures, revealing the propagation of three distinct IAW modes. The first two modes correspond to the downward and upward fundamental IAWs, while the third mode is a second-order mode identifiable as a downward leaky IAW (LIAW). The experimental results show excellent agreement with the theoretical predictions and establish a solid foundation for the future development of multifrequency IAW-based devices, including package-less acoustic components, microfluidic platforms, and gas and optical sensors designed for operation under harsh environmental conditions. Full article