Search Results (370)

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds. Full article

Collaboration between autonomous vehicles and drones can enhance the efficiency and connectivity of three-dimensional transportation systems. When satellite signals are unavailable, vehicles can achieve accurate localization by matching rich ground environmental data to digital maps, simultaneously providing the auxiliary localization information for drones. However, conventional digital maps suffer from high construction costs, easy misalignment, and low localization accuracy. Thus, this paper proposes a visual point cloud map (VPCM) construction and matching localization for autonomous vehicles. We fuse multi-source information from vehicle-mounted sensors and the regional road network to establish the geographically high-precision VPCM. In the absence of satellite signals, we segment the prior VPCM on the road network based on real-time localization results, which accelerates matching speed and reduces mismatch probability. Simultaneously, by continuously introducing matching constraints of real-time point cloud and prior VPCM through improved iterative closest point matching method, the proposed solution can effectively suppress the drift error of the odometry and output accurate fusion localization results based on pose graph optimization theory. The experiments carried out on the KITTI datasets demonstrate the effectiveness of the proposed method, which can autonomously construct the high-precision prior VPCM. The localization strategy achieves sub-meter accuracy and reduces the average error per frame by 25.84% compared to similar methods. Subsequently, this method’s reusability and localization robustness under light condition changes and environment changes are verified using the campus dataset. Compared to the similar camera-based method, the matching success rate increased by 21.15%, and the average localization error decreased by 62.39%. Full article

This study presents an innovative methodology to optimize the operation of distribution transformers through the estimation of hourly load curves, aimed at minimizing technical losses due to oversizing, particularly in systems lacking advanced metering infrastructure. The proposed approach combines clustering techniques, K-Means with DTW, to identify representative daily consumption patterns and a supervised model based on LightGBM to estimate hourly load curves for unmetered transformers, using customer characteristics as input. These estimated curves are integrated into a process that calculates technical losses, both no-load and load losses, for different transformer sizes, selecting the optimal rating that minimizes losses without compromising demand. Empirical validation showed accuracy levels of 95.6%, 95.29%, and 98.14% at an individual transformer, feeder, and a complete electrical system with 16,864 transformers, respectively. The application of the methodology to a real distribution system revealed a potential annual energy savings of 3004 MWh, equivalent to an estimated economic reduction of 150,238 USD. Full article

Background: Reliable and non-invasive methods for assessing welfare in poultry are essential for improving evidence-based welfare monitoring and advancing management practices in commercial production systems. The iris-to-pupil (IP) ratio, previously validated by our group in primates and cattle, reflects autonomic nervous system balance and may serve as a physiological indicator of stress in laying hens. This study evaluated the utility of the IP ratio under field conditions across diverse commercial layer housing systems. Materials and Methods: In total, 296 laying hens (Lohmann Brown, n = 269; White Leghorn, n = 27) were studied across four locations in Canada housed under different systems: Guelph (indoor; pen), Spring Island (outdoor and scratch; organic), Ottawa (outdoor, indoor and scratch; free-range), and Toronto (outdoor and hobby; free-range). High-resolution photographs of the eye were taken under ambient lighting. Light intensity was measured using the light meter app. The IP ratio was calculated using NIH ImageJ software (Version 1.54p). Statistical analysis included one-way ANOVA and linear regression using GraphPad Prism (Version 5). Results: Birds housed outdoors had the highest IP ratios, followed by those in scratch systems, while indoor and pen-housed birds had the lowest IP ratios (p < 0.001). Subgroup analyses of birds in Ottawa and Spring Island farms confirmed significantly higher IP ratios in outdoor environments compared to indoor and scratch systems (p < 0.001). The IP ratio correlated weakly with ambient light intensity (r² = 0.25) and age (r² = 0.05), indicating minimal influence of these variables. Although White Leghorn hens showed lower IP ratios than Lohmann Browns, this difference was confounded by housing type; all White Leghorns were housed in pens. Thus, housing system but not breed was the primary driver of IP variation. Conclusions: The IP ratio is a robust, non-invasive physiological marker of welfare assessment in laying hens, sensitive to housing environment but minimally influenced by light or age. Its potential for integration with digital imaging technologies supports its use in scalable welfare assessment protocols. Full article

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species. Full article

To collect the energy generated by rotational motion in the natural environment, a piezoelectric electromagnetic hybrid energy harvester (HEH) based on a planetary gear system is proposed. The harvester combines piezoelectric and electromagnetic effects and is mainly used for collecting low-frequency rotational energy. The HEH has a compact structure and contains four sets of piezoelectric energy harvesters (PEHs) and electromagnetic energy harvesters (EMHs) inside. The working principle of the energy harvester is analyzed, its theoretical model is established, and a simulation analysis is conducted. To verify the effectiveness of the design, an experimental device is constructed. The results indicate that the HEH can generate an average output power of 250 mW under eight magnets and an external excitation frequency of 7 Hz. In actual power supply testing, the HEH can light up 60 LEDs and provide stable power supply for the temperature–humidity meter. Full article

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R² > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R² of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data. Full article

Electricity consumption is increasing as a result of increasing people’s needs, such as lighting, heating, and comfort. Different needs come into play day by day in the houses where people live and in places used as common areas, and this increases the need for electricity. Studies have observed that almost half of the world’s electricity consumption is made by buildings. Public buildings, shopping malls, hospitals, and hotels are typical examples of such structures. However, hospitals have an important place among all building types as they contain a wide range of devices and are of critical importance to many systems. Consumption in hospitals is a necessity rather than a desire for comfort in places such as hotels and shopping malls. Therefore, analysis of the energy consumed by hospitals is one of the important things to perform to reduce the damage caused by electricity consumption to the environment. In this study, the energy analysis of a green hospital with an installed area of 55,000 square meters in Istanbul was conducted, and machine learning techniques can be used in the analysis. Among many methods used for building energy analysis, long short-term memory (LSTM) has been chosen. The available data set was analyzed with the various LSTM methods and classification and prediction operations were carried out. Error rates for each method were compared. With the results obtained, it has been observed that the vanilla LSTM method provides acceptable results in building energy analysis. Full article

Accurate illuminance measurements are critical in assessing lighting quality during post-occupancy evaluations, and traditional methods are labor-intensive and time-consuming. This pilot study demonstrates an alternative that combines high dynamic range (HDR) imaging with a low-cost proxy-Lambertian surface to transform image luminance into spatial illuminance. Seven readily available materials were screened for luminance uniformity; the specimen with minimal deviation from Lambertian behavior (≈2%) was adopted as the pseudo-Lambertian surface. Calibrated HDR images of a fluorescent-lit university classroom were acquired with a digital single-lens reflex (DSLR) camera and processed in Photosphere, after which pixel luminance was converted to illuminance via Lambertian approximation. Predicted illuminance values were benchmarked against spectral illuminance meter readings at 42 locations on horizontal work planes, vertical presentation surfaces, and the circulation floor. The average errors were 5.20% for desks and 6.40% for the whiteboard—well below the 10% acceptance threshold for design validation—while the projector-screen and floor measurements exhibited slightly higher discrepancies of 9.90% and 14.40%, respectively. The proposed workflow significantly reduces the cost, complexity, and duration of lighting assessments, presenting a promising tool for streamlined, accurate post-occupancy evaluations. Future work may focus on refining this approach for diverse lighting conditions and complex material interactions. Full article

This study evaluated the effect of freezing for up to 120 days on the physicochemical and technological properties of hamburgers made from Botucatu rabbit does slaughtered at 3, 12, and 24 months of age. The parameters were evaluated as follows: surface color (L*, a*, b*), pH using an insertion pH meter, cooking loss using a grill, storage loss based on weight differences, shear force in cooked samples using a texture analyzer, shrinkage percentage, chemical composition (moisture, protein, lipids, and ash), and lipid oxidation, determined by measuring the concentration of malondialdehyde in the burgers at different storage intervals (0, 60, and 120 days) under freezing conditions (−18 °C). The results indicated that increased storage time and animal age reduced tenderness and increased lipid content (p < 0.05). Burgers made from younger does showed higher levels of lipid oxidation. Age also influenced color (greater redness and lower lightness in older animals) and chemical composition, with older does producing burgers with higher protein and lower moisture and mineral content. However, all samples remained within the limits established by Brazilian legislation. This study recommends using meat from does of different ages for hamburger production, as all variations met the required legal standards. Full article

Early detection and continuous monitoring of diseases are critical to improving patient outcomes, treatment adherence, and diagnostic accuracy. Traditional melanoma diagnosis relies primarily on visual assessment and biopsy, with reported accuracies ranging from 50% to 90% and significant inter-observer variability. Among emerging diagnostic technologies, Raman spectroscopy has demonstrated considerable promise for non-invasive disease detection, particularly in early-stage skin cancer identification. A portable, real-time Raman spectroscopy system could significantly enhance diagnostic precision, reduce biopsy reliance, and expedite diagnosis. However, miniaturization of Raman spectrometers for portable use faces significant challenges, including weak signal intensity, fluorescence interference, and inherent trade-offs between spectral resolution and the signal-to-noise ratio. Recent advances in silicon photonics present promising solutions by facilitating efficient light collection, enhancing optical fields via high-index-contrast waveguides, and allowing compact integration of photonic components. This work introduces a numerical analysis of an integrated digital Fourier transform spectrometer implemented on a silicon-nitride (SiN) platform, specifically designed for Raman spectroscopy. The proposed system employs a switch-based digital Fourier transform spectrometer architecture coupled with a single optical power meter for detection. Utilizing a regularized regression method, we successfully reconstructed Raman spectra in the 800 cm⁻¹ to 1800 cm⁻¹ range, covering spectra of both benign and malignant skin lesions. Our results demonstrate the capability of the proposed system to effectively differentiate various skin cancer types, highlighting its feasibility as a non-invasive diagnostic sensor. Full article

Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments. Full article

This study aims to evaluate the techno-economic feasibility of implementing the IEC 62034:2012 standard, which governs automatic test systems for battery-powered emergency escape lighting, on a 52.8-m multipurpose vessel. The work is based on a detailed case study of the vessel’s lighting systems, incorporating lighting simulations, system modifications using DALI-compatible components, and an economic analysis based on net present value, internal rate of return, and discounted payback period. The results demonstrate that the implementation reduces preventive maintenance costs significantly—from 24,750 EUR to 2250 EUR over ten years—while achieving a positive net present value of 5317 EUR, an internal rate of return of 27.81%, and a discounted payback period of under five years. The findings contribute to maritime safety literature by extending the application of IEC 62034:2012 to shipboard environments, where it is not yet standard practice. Practically, it provides a cost-effective and safety-enhancing solution for ship operators, suggesting that automated testing systems can replace outdated manual maintenance procedures and improve compliance with safety regulations. Full article

High energy consumption in residential buildings poses significant challenges, prompting governments to regulate this sector through comprehensive energy assessments and classification strategies. This study introduces a multi-layer perceptron artificial neural network (ANN) model to grade and predict energy consumption levels in residential buildings in Tabriz, Iran, based on their geometric and functional characteristics. This study uses the K-Nearest Neighbors (KNN) algorithm to classify energy consumption grades based on energy ratio (R-value). Six sample buildings were modeled using Rhinoceros 3D version 7 and Grasshopper version 1.0.0007 software to extract key energy-influencing factors. A parametric geometric model was developed for rapid data generation and validated against reference buildings to ensure reliability. Building classifications spanned areas of 40 to 300 square meters and heights of up to six stories, with energy evaluations conducted using EnergyPlus. The collected data informed the ANN model, enabling accurate predictions for existing and future constructions. The results demonstrate that the model achieves a remarkable prediction error of just 0.001, facilitating efficient energy assessments without requiring extensive modeling expertise. This research emphasizes the role of geometric features and natural lighting in energy consumption prediction, highlighting the model’s practicality for early design evaluations and architectural validations. Full article

This paper presents the results of the photometric and spectral monitoring of the galaxy NGC 3516, which is an active galactic nucleus (AGN) of type Sy $1.5$ with a changing look. Observations were carried out at the Fesenkov Astrophysical Institute (FAI, Almaty, Kazakhstan) and the Shamakhy Astrophysical Observatory (ShAO, Shamakhy, Azerbaijan). Spectral monitoring of this galaxy in the wavelength range 4000–7000 Å began in 2020, while photometric observations have been conducted since 2014. During the observation period, estimates of the galaxy’s brightness in the B, V and $Rc$ filters were obtained, as well as measurements of the emission line and continuum fluxes. The light curve shows increased brightness of NGC 3516 in 2016 and 2019. The increase of emission line fluxes of H$\beta$ and H$\alpha$ and continuum began in 2019 and continued until spring 2020, when these characteristics reached their maximal values. A powerful X-ray flare took place on 1 April 2020. A new phase of brightening began in 2021 and has continued until 2025. After reaching their maxima in 2020, the emission fluxes of H$\beta$ and H$\alpha$ decreased by a factor of 1.5–2 and remained at a low level until 2022–2023, when they began to increase again. Medium-resolution spectra obtained on 20 April 2020, with the 1-meter “West” telescope (TSHAO) were used to study the broad components of the H$\beta$ and H$\alpha$ emission line profiles. Model calculations showed that the broad profile of the H$\alpha$ line consists of a central unshifted component and two (blue and red) components shifted symmetrically relative to the central component by a velocity of $v=980\pm 20$ km ${\mathrm{s}}^{-1}$. The H$\beta$ emission line was relatively weak, so the radial velocity of its components was determined with a large uncertainty: $900\pm 600$ km ${\mathrm{s}}^{-1}$. Full article