Search Results (51)

The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. Full article

When cultivating a selected field crop (alfalfa), we aimed to examine its positive effects on the variability of soil infiltration capacity. A total of 21 monitoring points were proposed for investigating soil hydraulic conductivity on the targeted plot with a total area of 47.64 ha, divided between the irrigated and non-irrigated areas. The plot is located outside the village of Oponice (Slovak Republic) and is managed by VPP Kolíňany. The study of hydraulic conductivity has been ongoing on the selected plot for several years. The presented results come from a two-year experiment, during which work operations related to the cultivation of alfalfa were carried out on the plot. The unsaturated hydraulic conductivity of the soil was assessed several times a year using a Mini Disk Infiltrometer, while soil moisture at monitoring points and the dependence of the measurement date (work operations, weather conditions) were also monitored. The average soil moisture content in the pilot measurements reached 18.77% vol. (CV = 1.44%), in the secondary measurements 17.21% vol. (CV 20.49%), in tertiary measurements 15.27% vol. (CV = 10.38%), and in the last measurements 15.26% vol. (CV = 10%), which ultimately represents a positive result of soil moisture balance. To test the significance of the differences between measurements taken across the entire surveyed plot, a one-factor ANOVA analysis was used to compare the measurement dates. The results showed a statistically significant difference when examining the effect of the time period of soil infiltration capacity monitoring between all measurements (p = 0.004). The mutual combinations of individual measurement dates were mostly significant (p = 0.03 for IDM1, IDM2; p = 0.003 for IDM2, IDM3), except for one case without a significant difference (IDM3, IDM4; p = 0.52). The second hypothesis was confirmed only at some monitoring points, and it can be stated that the irrigated area had a more significant effect on the soil infiltration capacity. The results obtained by the Shapiro–Wilk test and Welch’s test in irrigated and non-irrigated areas at individual dates showed statistically insignificant differences in three cases (IDM1, p = 0.123; IDM3, p = 0.382; IDM4, p = 0.445) and statistically significant in one case (IDM2, p = 0.0175). Based on the hypotheses and the results obtained, it can be said that the work tasks performed have a decisive influence on the infiltration capacity of the soil. The phenomenon of “water resistance” did not manifest itself in our research on soil infiltration capacity. The results were also evaluated using ArcGIS software 10.0 to display the spatial variability of soil hydraulic conductivity. The last application used to evaluate the results was Orange software 3.40.0, using clustering maps and hierarchical clustering. The results also pointed to variability depending on the dates of monitoring. Full article

To meet the development requirements of the path planning unit for unmanned underwater vehicles (UUVs), research is conducted on UUV 3D obstacle avoidance path planning algorithms and software design. Firstly, aiming at the problem of underwater 3D obstacle avoidance path planning for UUVs, a global path planning algorithm based on the improved D* Lite is designed, and a local path planning algorithm combining the 3D obstacle avoidance strategy and the improved artificial potential field (APF) algorithm is designed. Secondly, based on the above path planning algorithms, a UUV 3D obstacle avoidance path planning software is developed under the Robot Operating System 2 (ROS2) framework and deployed on an Orange Pi 5B. To test the algorithms and the developed software, a UUV autonomous navigation hardware-in-the-loop (HIL) simulation system is constructed. Finally, based on this system, three types of HIL simulation experiments are conducted, including global path planning, local path planning, and comprehensive obstacle avoidance path planning. The simulation experiments show that the improved D* Lite-APF algorithm has better comprehensive performance than the traditional D* Lite-APF algorithm; the path planning software can guide the UUV to reach the goal point safely and runs stably and reliably. The designed UUV 3D obstacle avoidance path planning algorithm and software exhibit good obstacle avoidance performance and can be applied to the rapid development of actual UUV path planning units. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

Interior space quality in certified office buildings is key in supporting the health and well-being of occupants. Daylight, which regulates the human circadian rhythm and affects physiological processes and productivity, is crucial. This study’s objective was to determine how a building’s architecture and selected elements of its interior such as partitions and finishing material parameters affect sunlight distribution in workspaces and its biological effectiveness, as measured using Equivalent Melanopic Lux (EML). The environment’s impact on the non-visual potential of a space was also assessed (in relation to the M/P ratio). To achieve these objectives, we used a 3D model of an office building floor to simulate natural lighting in various configurations, for a cool temperate climate using Solemma’s ALFA 2025 software. This research was conducted using simulations only, with no in situ measurements. The study assessed melanopic light intensity for specific zones and workstation groups. The impact of ceiling colors and the five colors given to partitions of different heights located between desks was also determined. The study evaluated the relationship between photopic and melanopic intensity and found that, as the height of the partitions increased, especially with cloudy skies, the importance of these planes’ colors increased. Blues had a positive effect on the space’s non-visual potential, while oranges showed significant decreases in EML relative to lux, by up to 25%. This research underscores the importance of light’s non-visual impact and the consideration of these aspects at every design stage, especially interior design, to provide a comfortable work environment and its long-term benefits. We also proposed natural light exposition optimization strategies that can support proper circadian rhythm. Full article

Real-time vessel tracking and environmental assessment in developing regions face significant challenges due to the high cost and proprietary constraints of commercial Automatic Identification System (AIS) services. We introduce MAGI, an open-source, low-cost, IoT-distributed architecture that integrates Orange Pi 5 edge nodes with software-defined radio (SDR) AIS receivers and containerized microservices to capture, preprocess, and stream AIS messages. During a ten-day field campaign in Panama, our decentralized deployment processed over 500,000 AIS transmissions, achieving 99% uptime and delivering vessel position and speed updates with sub-second latency. Based on the collected data, we also evaluated system scalability, energy consumption, and per node cost, demonstrating that a complete coastal network can be deployed for under USD 1200 per site. These results confirm that MAGI is a scalable, secure, and affordable IoT solution for AIS-based vessel tracking and environmental monitoring in resource-constrained settings. Full article

The dynamic loads affecting earth-retaining structures may increase in seismically active regions. Therefore, studying the soil–structure interaction among the soil, shoring systems, and adjacent structures is crucial. However, there is limited research on this important topic. This study investigates the seismic performance of a deep braced excavation and a nearby 10-story building in sandy soil formation. The main focus of this study is the consideration of the influence of varying foundation depths of adjacent structures on the seismic response of the shoring system and the performance of the shoring system and adjacent structure under different earthquake records. PLAXIS 2D software (Version 22.02) was used to carry out the numerical analysis. Sandy soil was modeled using the Hardening Soil with small-strain stiffness model (HS-small). Back analysis of observation data extracted from a real case study of a deep braced excavation in the central district of Kaohsiung City, adjacent to the O₇ Station on the Orange Line of the Kaohsiung MRT system in Taiwan, was used to validate the numerical analysis. Beyond model validation, a parametric study was conducted to address the effect of the foundation level of the building adjacent to the excavation on both the seismic behavior of the shoring system and the structure itself, using the Loma-Prieta (1989) earthquake record. The parametric study was further extended to assess the responses of the shoring system and the adjacent structure under the influence of the earthquake records of Loma-Prieta (1989), Northridge (1994), and El-Centro (1940). The results show that the maximum lateral displacement of the diaphragm wall occurred at the top of the wall in all studied cases. The maximum dynamic bending moment in the retaining structure was more than three times the static one on average. In contrast, the dynamic shear force was more than 2.85 times the static one on average. In addition, the dynamic axial force of the first and second struts was 1.38 and 3.17 times the static forces, respectively. The results also reveal large differences in the behavior of the shoring system and the adjacent structure between the different earthquake records. Full article

Background: Zebrafish-based drug discovery systems provide significant advantages over mammalian models for high-throughput in vivo screening. Among these, the NF-κB eGFP reporter system significantly enhances drug discovery in zebrafish by enabling real-time, high-resolution monitoring of pathway activity in live organisms, thereby streamlining mechanistic studies and high-throughput screening. Methods: We developed a novel AI (Quantifish and Orange software)-based zebrafish precision individualized 96-well ZF plates (0–7 dpf) and individualized MT tanks (8 dpf–4 mpf) protocol for the transposon-mediated transgenesis of the NFκB eGFP reporter system. Results: One-cell stage embryos were administered NFκB reporter construct and Tol2 transposase mRNA via microinjection and transferred to separate wells of a 96-well ZF plate. Bright-field and fluorescence images of each well were captured at 5 dpf in the F0, F1, and F2 generations using the automated confocal high-content imager CQ1. The Quantifish software was used for the automated detection and segmentation of zebrafish larval fluorescence intensity in specific regions of interest. Quantitative data on the fluorescence intensity and distribution patterns were measured in Quantifish, and advanced statistical and machine learning methods were applied using Orange. Imaging data with eGFP expression results were assessed to evaluate the efficiency of the transgenic protocol. Discussion: This AI-enhanced precision protocol allows for high-throughput screening and quantitative analysis of NFκB reporter transgenesis in zebrafish, enabling the efficient identification and characterization of stable transgenic lines that exhibit tissue-specific expression of the NF-κB reporter, such as lines with induced expression restricted to the retina following LPS stimulation. This approach streamlines the evaluation of regulatory elements, enhances data consistency, and reduces animal use, making it a valuable tool for zebrafish drug discovery. Full article

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments. Full article

This work presents a Fricke radiochromic gel dosimeter with xylenol orange (XO) and a gelatin matrix modified with sorbitol. The dosimeter, combined with 2D scanning using a flatbed scanner and data processing using dedicated software packages, creates a radiotherapy dosimetry measurement system. The dosimeter reacts to ionizing radiation by changing color as a result of the formation of complexes of Fe³⁺ and XO molecules. It was characterized in terms of thermal and chemical stability and mechanical properties. The presence of sorbitol improved the mechanical and thermal properties of the dosimeter. The dosimeter maintains chemical stability, enabling its use in dosimetric applications, for at least six weeks. The dose–response characteristics of the dosimeter are discussed and indicate a dynamic dose–response of the dosimeter (up to saturation) of about 20 Gy and a linear dose–response of about 12.5 Gy. The following applications of the dosimeter are discussed: (i) as a 2D dosimeter in a plastic container for performing a coincidence test of radiation and mechanical isocenters of a medical accelerator, and (ii) for in vivo dosimetry as a 2D dosimeter alone and simultaneously as a bolus and a 2D dosimeter. Research has shown that the dosimeter has promise in many applications. Full article

This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predictive model. The methodology encompasses four key stages: data collection, processing, prediction, and evaluation. After rigorous analysis and refinement, the dataset was reduced from 16,046 to 555 high-quality records. Eight ML models were implemented and evaluated using Orange Data Mining software, version 3.38.0, including advanced algorithms such as Random Forest, Gradient Boosting, and neural networks. Model performance was assessed through cross-validation and comprehensive metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination (R²). The AdaBoost algorithm emerged as the most accurate predictor, achieving impressive results with an MAE of 0.042, MSE of 0.003, RMSE of 0.057, and R² of 0.331. This research demonstrates the significant potential of ML techniques in enhancing raw material yield forecasting within the tanning industry. The findings contribute to more efficient forecasting processes, aligning with Industry 4.0 principles and paving the way for data-driven decision-making in manufacturing. Full article

Mechanoluminescent (ML) materials can exhibit visible-to-near-infrared mechanoluminescence when responding to the fracture or deformation of a solid under mechanical stimulation. Transforming mechanical energy into light demonstrates promising applications in terms of visual mechanical sensing. In this work, we synthesized the phosphor CaZnOS:Tb³⁺, Sm³⁺, which exhibited intense and tunable multicolor mechanoluminescence without pre-irradiation. Intense green ML materials were obtained by doping Tb³⁺ with different concentrations. Tunable multicolor mechanoluminescence (such as green, yellow-green, and orange-red) could be realized by combining green emission (about 542 nm), attributed to Tb³⁺, and red emission (about 600 nm) generated from the Sm³⁺ in the CaZnOS substrate. The tunable multicolor ML materials CaZnOS:Tb³⁺, Sm³⁺ exhibited intense luminance and recoverable mechanoluminescence when responding to mechanical stimulation. Benefiting from the excellent ML performance and multicolor tunability in CaZnOS:Tb³⁺, Sm³⁺, we mixed the phosphor with PDMS and a curing agent to explore its practical application. An application for visual mechanical sensing was designed for handwriting identification. By taking a time-lapsed shot while writing, we easily obtained images of the writer’s handwriting. The images of the ML intensity were acquired by using specific software to transform the shooting data. We could easily distinguish people’s handwriting through analyzing the different ML performances. Full article

Solubility is not only a crucial physicochemical property for laboratory practice but also provides valuable insight into the mechanism of saturated system organization, as a measure of the interplay between various intermolecular interactions. The importance of these data cannot be overstated, particularly when dealing with active pharmaceutical ingredients (APIs), such as dapsone. It is a commonly used anti-inflammatory and antimicrobial agent. However, its low solubility hampers its efficient applications. In this project, deep eutectic solvents (DESs) were used as solubilizing agents for dapsone as an alternative to traditional solvents. DESs were composed of choline chloride and one of six polyols. Additionally, water–DES mixtures were studied as a type of ternary solvents. The solubility of dapsone in these systems was determined spectrophotometrically. This study also analyzed the intermolecular interactions, not only in the studied eutectic systems, but also in a wide range of systems found in the literature, determined using the COSMO-RS framework. The intermolecular interactions were quantified as affinity values, which correspond to the Gibbs free energy of pair formation of dapsone molecules with constituents of regular solvents and choline chloride-based deep eutectic solvents. The patterns of solute–solute, solute–solvent, and solvent–solvent interactions that affect solubility were recognized using Orange data mining software (version 3.36.2). Finally, the computed affinity values were used to provide useful descriptors for machine learning purposes. The impact of intermolecular interactions on dapsone solubility in neat solvents, binary organic solvent mixtures, and deep eutectic solvents was analyzed and highlighted, underscoring the crucial role of dapsone self-association and providing valuable insights into complex solubility phenomena. Also the importance of solvent–solvent diversity was highlighted as a factor determining dapsone solubility. The Non-Linear Support Vector Regression (NuSVR) model, in conjunction with unique molecular descriptors, revealed exceptional predictive accuracy. Overall, this study underscores the potency of computed molecular characteristics and machine learning models in unraveling complex molecular interactions, thereby advancing our understanding of solubility phenomena within the scientific community. Full article

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects. Full article

Pancreatic ductal adenocarcinoma (PDAC) is the most frequent infiltrating type of pancreatic cancer. The poor prognosis associated with this cancer is due to the absence of specific biomarkers, aggressiveness, and treatment resistance. PDAC is a deadly malignancy bearing distinct genetic alterations, the most common being those that result in cancer-causing versions of the KRAS gene. Cannabigerol (CBG) is a non-psychomimetic cannabinoid with anti-inflammatory properties. Regarding the anticancer effect of CBG, up to now, there is only limited evidence in human cancers. To fill this gap, we investigated the effects of CBG on the PDAC cell lines, PANC-1 and MIAPaCa-2. The effect of CBG activity on cell viability, cell death, and EGFR-RAS-associated signaling was investigated. Moreover, the potential synergistic effect of CBG in combination with gemcitabine (GEM) and paclitaxel (PTX) was investigated. MTT was applied to investigate the effect of CBG on PDAC cell line viabilities. Annexin-V and Acridine orange staining, followed by cytofluorimetric analysis and Western blotting, were used to evaluate CBG’s effect on cell death. The modulation of EGFR-RAS-associated pathways was determined by Western blot analysis and a Milliplex multiplex assay. Moreover, by employing the MTT data and SynergyFinder Plus software analysis, the effect of the combination of CBG and chemotherapeutic drugs was determined. Full article