Search Results (142)

This pilot study investigated an automated pilot plant for removing microplastics (MPs) from industrial wastewater that are generated during packaging production. MP removal is based on organosilane-induced agglomeration-fixation (clump & skim technology) followed by separation. The wastewater had high MP loads (1725 ± 377 mg/L; 673 ± 183 million particles/L) and an average COD of 7570 ± 1339 mg/L. Over 25 continuous test runs, the system achieved consistent performance, removing an average of 97.4% of MPs by mass and 99.1% by particle count, while reducing the COD by 78.8%. Projected over a year, this equates to preventing 1.7 tons of MPs and 6 tons of COD from entering the sewage system. Turbidity and photometric TSS measurements proved useful for process control. The approach supports water reuse—with water savings up to 80%—and allows recovery of agglomerates for recycling and reuse. Targeting pollutant removal upstream at the source provides multiple financial and environmental benefits, including lower overall energy demands, higher removal efficiencies, and process water reuse. This provides financial and environmental incentives for industries to implement sustainable solutions for pollutants and microplastic removal. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

The accurate identification of wood patterns is critical for optimizing the use of sustainable wood building materials, promoting resource efficiency, and reducing waste in construction. This study presents a deep learning-based approach for enhanced wood material recognition, combining EfficientNet architecture with advanced data augmentation techniques to achieve robust classification. The augmentation strategy incorporates geometric transformations (flips, shifts, and rotations) and photometric adjustments (brightness and contrast) to improve dataset diversity while preserving discriminative wood grain features. Validation was performed using a controlled augmentation pipeline to ensure realistic performance assessment. Experimental results demonstrate the model’s effectiveness, achieving 88.9% accuracy (eight out of nine correct predictions), with further improvements from targeted image preprocessing. The approach provides valuable support for preliminary sustainable building material classification, and can be deployed through user-friendly interfaces without requiring specialized AI expertise. The system retains critical wood pattern characteristics while enhancing adaptability to real-world variability, supporting reliable material classification in sustainable construction. This study highlights the potential of integrating optimized neural networks with tailored preprocessing to advance AI-driven sustainability in building material recognition, contributing to circular economy practices and resource-efficient construction. Full article

Three-dimensional (3D) reconstruction has become a fundamental technology in applications ranging from cultural heritage preservation and robotics to forensics and virtual reality. As these applications grow in complexity and realism, the quality of the reconstructed models becomes increasingly critical. Among the many factors that influence reconstruction accuracy, the lighting conditions at capture time remain one of the most influential, yet widely neglected, variables. This review provides a comprehensive survey of classical and modern 3D reconstruction techniques, including Structure from Motion (SfM), Multi-View Stereo (MVS), Photometric Stereo, and recent neural rendering approaches such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), while critically evaluating their performance under varying illumination conditions. We describe how lighting-induced artifacts such as shadows, reflections, and exposure imbalances compromise the reconstruction quality and how different approaches attempt to mitigate these effects. Furthermore, we uncover fundamental gaps in current research, including the lack of standardized lighting-aware benchmarks and the limited robustness of state-of-the-art algorithms in uncontrolled environments. By synthesizing knowledge across fields, this review aims to gain a deeper understanding of the interplay between lighting and reconstruction and provides research directions for the future that emphasize the need for adaptive, lighting-robust solutions in 3D vision systems. Full article

The galaxy NGC 7469 is a bright infrared source with an active galactic nucleus (AGN) and an intense star-forming region with a radius of approximately 500 parsecs, where the star formation rate is estimated to be 20–50 $M_{\odot }{\mathrm{yr}}^{-1}$. This study presents the results of spectral and photometric observations carried out during the period from 2020 to 2024 at the Fesenkov Astrophysical Institute (Almaty, Kazakhstan) and the Nasreddin Tusi Shamakhy Astrophysical Observatory (Shamakhy, Azerbaijan). Photometric data were obtained using B, V, and Rc filters, while spectroscopic observations covered the wavelength range of $\lambda$ 4000–7000 Å. Data reduction was performed using the IRAF and MaxIm DL Pro6 software packages. An analysis of the light curves revealed that after the 2019–2020 outburst, the luminosity level of NGC 7469 remained relatively stable until the end of 2024. In November–December 2024, an increase in brightness (∼0.3–0.5 magnitudes) was recorded. Spectral data show variations in the Ha fluxes and an enhancement of them at the end of 2024. On BPT diagrams, the emission line flux ratios [OIII]/H $\beta$ and [NII]/H $\alpha$ place NGC 7469 on the boundary between regions dominated by different ionization sources: AGN and star-forming regions. The electron density of the gas, estimated from the intensity ratios of the [SII] 6717, 6731 Ålines, is about $900–1000{\mathrm{cm}}^{-3}$. Continued observations will help to determine whether the trend of increasing brightness and emission line fluxes recorded at the end of 2024 will persist. Full article

Light curve analysis of W UMa-type contact binary systems using MCMC or MC methods can be time-consuming, primarily because the repeated generation of synthetic light curves tends to be relatively slow during the fitting process. Although various approaches have been proposed to address this issue, their implementation is often challenging due to complexity or uncertain performance. In this study, we introduce the BSN application, whose name is taken from the BSN project. The application is designed for analyzing contact binary system light curves, supporting photometric data, and employing an MCMC algorithm for efficient parameter estimation. The BSN application generates synthetic light curves more than 40 times faster than PHOEBE during the MCMC fitting process. The BSN application enhances light curve analysis with an expanded feature set and a more intuitive interface while maintaining compliance with established scientific standards. In addition, we present the first light curve analyses of four contact binary systems based on the TESS data, utilizing the BSN application version 1.0. We also conducted a light curve analysis using the PHOEBE Python code and compared the resulting outputs. Two of the target systems exhibited asymmetries in the maxima of their light curves, which were appropriately modeled by introducing a cold starspot on one of the components. The estimated mass ratios of these total-eclipse systems place them within the category of low mass ratio contact binary stars. The estimation of the absolute parameters for the selected systems was carried out using the $P-a$ empirical relationship. Based on the effective temperatures and masses of the components, three of the target systems were classified as A-subtype, while TIC 434222993 was identified as a W-subtype system. Full article

The increased use of smartphones in daily life challenges researchers regarding the quality of light emitted by screens. This study aims to analyze displays’ qualitative and quantitative light parameters from various smartphone models available on the market over the last decade. Advanced photometric and colorimetric measurements using complex instrumentation were performed. It covered the color gamut, channel linearity response, refresh rate, flickering, spatial radiation distribution, luminance, uniformity, and static contrast. The analysis showed that, despite advances in smartphone display technology, differences in visible radiation parameters between older and newer models are surprisingly marginal. However, improvements were observed in newer models in terms of viewing angles and compliance with the sRGB standard. Tested built-in blue light reduction filters were ineffective. It only slightly reduces light between 380 nm and 480 nm. In contrast, much higher decreases in this spectral range were achieved for dedicated applications. However, it lowered radiant power density across the visible spectrum, significantly decreasing the displays’ correlated color temperature. Enabling the power-saving mode caused the deterioration of parameters such as refresh rate, but the flicker depth remained constant. Static contrast for most tested devices was also at the same level. The findings confirm the need for further studies on display technology development that supports user well-being while minimizing its harmful effects. Full article

Micromixing is a crucial process in microfluidic systems. In biochemical and chemical analysis, the sample is usually tested with reagents. These solutions must be well mixed for the reaction to be possible, generally using micromixers manufactured with sophisticated and expensive technology. The present work shows the design and evaluation of micromixers fabricated with LTCC (low-temperature co-fired ceramics) and FDM (fused deposition modeling) technologies for the development of functional and complex geometries. Two-dimensional planar serpentine and 3D chaotic convection serpentine micromixers were manufactured and implemented in an automated microanalytical system using photometric methods. To evaluate the performance of the micromixers, flow, mixing and absorbance measurements were carried out. Green tape and PP materials were used and showed good resistance to the acidic chemical solutions. The devices presented achieved mixing times in seconds, a reduced dispersion due to their aspect ratio, high sensitivity, and precision in photometric measurement. The optical sensing cells stored sample volumes in a range of 10 to 600 µL, which allowed the reduction of reagent consumption and waste generation. These are ideal characteristics for in situ measurement, portable, and low-cost applications focused on green chemistry and biochemistry. Full article

The mathematical model for photometric stereo makes several restricting assumptions, which are often not fulfilled in real-life applications. Specifically, an object surface does not always satisfies Lambert’s cosine law, leading to reflection issues. Moreover, the camera and the light source, in some situations, have to be placed at a close distance from the target, rather than at infinite distance from it. When studying algorithms for these complex situations, it is extremely useful to have at disposal synthetic datasets with known exact solutions, to assert the accuracy of a solution method. The aim of this paper is to present a Matlab package which constructs such datasets on the basis of a chosen exact solution, providing a tool for simulating various real camera/light configurations. This package, starting from the mathematical expression of a surface, or from a discrete sampling, allows the user to build a set of images matching a particular light configuration. Setting various parameters makes it possible to simulate different scenarios, which can be used to investigate the performance of reconstruction algorithms in several situations and test their response to lack of ideality in data. The ability to construct large datasets is particularly useful to train machine learning based algorithms. Full article

With the gradual application of 3D reconstruction technology in underwater scenes, the design of vision-based reconstruction models has become an important research direction for human ocean exploration and development. The underwater laser triangulation method is the most commonly used approach, yet it misses details during the reconstruction of sparse point clouds, which do not meet the requirements of practical applications. On the other hand, existing underwater photometric stereo methods can accurately reconstruct local details of target objects, but they require relative stillness to be maintained between the camera and the target, which is practically difficult to achieve in underwater imaging environments. In this paper, we propose an underwater target reconstruction algorithm that combines laser triangulation and multispectral photometric stereo (MPS) to address the aforementioned practical problems in underwater 3D reconstruction.This algorithm can obtain more comprehensive 3D surface data of underwater objects through mobile measurement. At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. The experimental results show that our method achieves higher-precision and higher-density 3D reconstruction than current state-of-the-art methods. Full article

The neural implicit SLAM system performs excellently in static environments, offering higher-quality rendering and scene reconstruction capabilities compared to traditional dense SLAM. However, in dynamic real-world scenes, these systems often experience tracking drift and mapping errors. To address these problems, we suggest DIN-SLAM, a dynamic scene neural implicit SLAM system based on optical flow and depth gradient verification. DIN-SLAM combines depth gradients, optical flow, and motion consistency to achieve robust filtering of dynamic pixels, while optimizing dynamic feature points through optical flow registration to enhance tracking accuracy. The system also introduces a dynamic scene optimization strategy that utilizes photometric consistency loss, depth gradient loss, motion consistency constraints, and edge matching constraints to improve geometric consistency and reconstruction performance in dynamic environments. To reduce the interference of dynamic objects on scene reconstruction and eliminate artifacts in scene updates, we propose a targeted rendering and ray sampling strategy based on local feature counts, effectively mitigating the impact of dynamic object occlusions on reconstruction. Our method supports multiple sensor inputs, including pure RGB and RGB-D. The experimental results demonstrate that our approach consistently outperforms state-of-the-art baseline methods, achieving an 83.4% improvement in Absolute Trajectory Error Root Mean Square Error (ATE RMSE), a 91.7% enhancement in Peak Signal-to-Noise Ratio (PSNR), and the elimination of artifacts caused by dynamic interference. These enhancements significantly boost the performance of tracking and mapping in dynamic scenes. Full article

We analyze three nearby spiral galaxies—NGC 1097, NGC 1566, and NGC 3627—using images from the DustPedia database in seven infrared bands (3.6, 8, 24, 70, 100, 160, and 250 μm). For each image, we perform photometric decomposition and construct a multi-component model, including a detailed representation of the spiral arms. Our results show that the light distribution is well described by an exponential disk and a Sérsic bulge when non-axisymmetric components are properly taken into account. We test the predictions of the stationary density wave theory using the derived models in bands, tracing both old stars and recent star formation. Our findings suggest that the spiral arms in all three galaxies are unlikely to originate from stationary density waves. Additionally, we perform spectral energy distribution (SED) modeling using the hierarchical Bayesian code HerBIE, fitting individual components to derive dust properties. We find that spiral arms contain a significant (>10%) fraction of cold dust, with an average temperature of approximately 18–20 K. The estimated fraction of polycyclic aromatic hydrocarbons (PAHs) declines significantly toward the galactic center but remains similar between the arm and interarm regions. Full article

Non-collaborative (i.e., reflective, transparent, metallic, etc.) surfaces are common in industrial production processes, where 3D reconstruction methods are applied for quantitative quality control inspections. Although the use or combination of photogrammetry and photometric stereo performs well for well-textured or partially textured objects, it usually produces unsatisfactory 3D reconstruction results on non-collaborative surfaces. To improve 3D inspection performances, this paper investigates emerging learning-based surface reconstruction methods, such as Neural Radiance Fields (NeRF), Multi-View Stereo (MVS), Monocular Depth Estimation (MDE), Gaussian Splatting (GS) and image-to-3D generative AI as potential alternatives for industrial inspections. A comprehensive evaluation dataset with several common industrial objects was used to assess methods and gain deeper insights into the applicability of the examined approaches for inspections in industrial scenarios. In the experimental evaluation, geometric comparisons were carried out between the reference data and learning-based reconstructions. The results indicate that no method can outperform all the others across all evaluations. Full article

Background: The alignment of a person’s body segments depends on their innate anatomy and neuromuscular status. Sagittal posture assessments provide valuable information on correctable deficits, which can be used to prevent possible health issues or injuries. Methods: This article provides practical guidance on how to perform a basic photometric sagittal posture analysis in a reproducible manner, which reference points should be used, and which errors should be avoided. For this purpose, based on the current literature, four important evidence-based parameters for evaluation are defined, and literature-based reference values are given for the assessment of posture. Conclusions: When done correctly, the sagittal posture analysis is a valuable tool in the fields of medicine and sports. Full article

In recent years, the rapid advancement and pervasive deployment of unmanned aerial vehicle (UAV) technology have catalyzed transformative applications across the military, civilian, and scientific domains. While aerial imaging has emerged as a pivotal tool in modern remote sensing systems, persistent challenges remain in achieving robust small-target detection under complex all-weather conditions. This paper presents an innovative multimodal fusion framework incorporating photometric perception and cross-attention mechanisms to address the critical limitations of current single-modality detection systems, particularly their susceptibility to reduced accuracy and elevated false-negative rates in adverse environmental conditions. Our architecture introduces three novel components: (1) a bidirectional hierarchical feature extraction network that enables the synergistic processing of heterogeneous sensor data; (2) a cross-modality attention mechanism that dynamically establishes inter-modal feature correlations through learnable attention weights; (3) an adaptive photometric weighting fusion module that implements spectral characteristic-aware feature recalibration. The proposed system achieves multimodal complementarity through two-phase integration: first by establishing cross-modal feature correspondences through attention-guided feature alignment, then performing weighted fusion based on photometric reliability assessment. Comprehensive experiments demonstrate that our framework achieves an improvement of at least 3.6% in mAP compared to the other models on the challenging LLVIP dataset, and with particular improvements in detection reliability on the KAIST dataset. This research advances the state of the art in aerial target detection by providing a principled approach for multimodal sensor fusion, with significant implications for surveillance, disaster response, and precision agriculture applications. Full article