Search Results (346)

The article presents a comprehensive review of key issues and challenges related to enhancing the durability of hot forging tools. It discusses modern strategies aimed at increasing tool life, including modifications to tool materials, heat treatment, surface engineering, tool and die design, die geometry, tribological conditions, and lubrication. The review is based on extensive literature data, including recent publications and the authors’ own research, which has been implemented under industrial conditions at the modern forging facility in Forge Plant “Glinik” (Poland). The study introduces original design and technological solutions, such as an innovative concept for manufacturing forging dies from alloy structural steels with welded impressions, replacing traditional hot-work tool steel dies. It also proposes a zonal hardfacing approach, which involves applying welds with different chemical compositions to specific surface zones of the die impressions, selected according to the dominant wear mechanisms in each zone. General guidelines for selecting hardfacing material compositions are also provided. Additionally, the article presents technological processes for die production and regeneration. The importance and application of computer simulations of forging processes are emphasized, particularly in predicting wear mechanisms and intensity, as well as in optimizing tool and forging geometry. Full article

Accurate organ-level segmentation is essential for achieving high-throughput, non-destructive, and automated plant phenotyping. To address the challenge of intelligent acquisition of phenotypic parameters in tomato plants, we propose TSINet, an end-to-end dual-task segmentation network designed for effective and precise semantic labeling and instance recognition of tomato point clouds, based on the Pheno4D dataset. TSINet adopts an encoder–decoder architecture, where a shared encoder incorporates four Geometry-Aware Adaptive Feature Extraction Blocks (GAFEBs) to effectively capture local structures and geometric relationships in raw point clouds. Two parallel decoder branches are employed to independently decode shared high-level features for the respective segmentation tasks. Additionally, a Dual Attention-Based Feature Enhancement Module (DAFEM) is introduced to further enrich feature representations. The experimental results demonstrate that TSINet achieves superior performance in both semantic and instance segmentation, particularly excelling in challenging categories such as stems and large-scale instances. Specifically, TSINet achieves 97.00% mean precision, 96.17% recall, 96.57% F1-score, and 93.43% IoU in semantic segmentation and 81.54% mPrec, 81.69% mRec, 81.60% mCov, and 86.40% mWCov in instance segmentation. Compared with state-of-the-art methods, TSINet achieves balanced improvements across all metrics, significantly reducing false positives and false negatives while enhancing spatial completeness and segmentation accuracy. Furthermore, we conducted ablation studies and generalization tests to systematically validate the effectiveness of each TSINet component and the overall robustness of the model. This study provides an effective technological approach for high-throughput automated phenotyping of tomato plants, contributing to the advancement of intelligent agricultural management. Full article

Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m³, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m³, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m³, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r² values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. Full article

This study investigated the integration of advanced Atmospheric Water Generators (AWGs) within the design process of building energy systems, focusing on the water–energy nexus in the context of a real-life hospital building. It is based on a simulation approach, recognised as a viable means to analyse and enhance AWG potentialities. However, the current state of research does not address the issue of AWG integration within building plant systems. This study contributes to fill such a research gap by building upon an authors’ previous work and proposing an enhanced methodology. The methodology describes how to incorporate a multipurpose AWG system into the energy simulation environment of DesignBuilder (DB), version 7.0.0116, through its coupling with AWGSim, version 1.20d, a simulation tool specifically developed for atmospheric water generators. The chosen case study is a wing of the Mondino Hospital in Pavia, Italy, selected for its complex geometry and HVAC requirements. By integrating AWG outputs—covering water production, heating, and cooling—into DB, this study compared two configurations: the existing HVAC system and an enhanced version that includes the AWG as plant support. The simulation results demonstrated a 16.3% reduction in primary energy consumption (from 231.3 MWh to 193.6 MWh), with the elimination of methane consumption and additional benefits in water production (257 m³). This water can be employed for photovoltaic panel cleaning, further reducing the primary energy consumption to 101.9 MWh (55.9% less than the existing plant), and for human consumption or other technical needs. Moreover, this study highlights the potential of using AWG technology to supply purified water, which can be a pivotal solution for hospitals located in areas affected by water crises. This research contributes to the atmospheric water field by addressing the important issue of simulating AWG systems within building energy design tools, enabling informed decisions regarding water–energy integration at the project stage and supporting a more resilient and sustainable approach to building infrastructure. Full article

Alkaline carbonatite complexes are formed by magmatic, hydrothermal, and weathering geological events, which modify the minerals present in the rocks, resulting in ores with varied metallurgical behavior. To better spatially distinguish ores with distinct plant responses, creating a 3D geometallurgical block model was necessary. To establish the clusters, four different algorithms were tested: K-Means, Hierarchical Agglomerative Clustering, dual-space clustering (DSC), and clustering by autocorrelation statistics. The chosen method was DSC, which can consider the multivariate and spatial aspects of data simultaneously. To better understand each cluster’s mineralogy, an XRD analysis was conducted, shedding light on why each cluster performs differently in the plant: cluster 0 contains high magnetite content, explaining its strong magnetic yield; cluster 3 has low pyrochlore, resulting in reduced flotation yield; cluster 2 shows high pyrochlore and low gangue minerals, leading to the best overall performance; cluster 1 contains significant quartz and monazite, indicating relevance for rare earth elements. A hierarchical indicator kriging workflow incorporating a stochastic partial differential equation (SPDE) trend model was applied to spatially map these domains. This improved the deposit’s circular geometry reproduction and better represented the lithological distribution. The elaborated model allowed the identification of four geometallurgical zones with distinct mineralogical profiles and processing behaviors, leading to a more robust model for operational decision-making. Full article

Topping is an essential step in cotton cultivation in Xinjiang, China, which can effectively increase the number of bolls per plant and optimize the yield and quality. Paclobutrazol, as a common chemical topping agent for cotton, faces challenges such as unstable topping effect and limited leaf surface absorption during application. In this study, paclobutrazol was used as the ligand and a zinc complex was synthesized by the thermosolvent method to replace paclobutrazol and improve the topping effect on cotton. The structure of the complex was characterized using FTIR, UV-vis, TG, and XRD analyses. The results confirmed that each zinc ion coordinated with four nitrogen atoms from the triazole rings of paclobutrazol and two oxygen atoms from nitrate ions, forming an octahedral geometry. Surface tension measurement and analysis revealed that the complex had a surface tension reduction of 12.75 mN/m compared to paclobutrazol, thereby enhancing the surface activity of the complex in water systems and improving its absorption efficiency on plant leaves. Two-year field trials indicated that the foliar application of the complex at a dosage of 120 g·hm⁻² in inhibiting cotton plant height was more stable to that of paclobutrazol or mepiquat chloride. It also shortened the length of fruiting branches, making the shape of cotton plants compact, thereby indirectly improving the ventilation and light penetration of the cotton field and the convenience of mechanical harvesting. Yield data showed that, compared with artificial topping, the complex at a dosage of 120 g·hm⁻² treatment increased cotton yield by approximately 4.6%. Therefore, the paclobutrazol–zinc complex is a promising alternative to manual topping and have great application potential in future mechanized cotton production. Full article

The focus of this paper is placed on Oscillating Water Column (OWC) systems. The primary aim is to analyze, through both mathematical modeling and numerical simulations, a single module (chamber) of an OWC plant which, in addition to energy production, offers the dual advantage of large-scale integration into port infrastructures or coastal defense structures such as breakwaters, etc. The core challenge lies in optimizing the geometry of the OWC chamber and its associated ducts. A trapezoidal cross-section is adopted, with various front wall inclinations ranging from $90°$ to $45°$. This geometric parameter significantly affects both the internal compression ratio and the hydrodynamic behavior of incoming and outgoing waves. Certain inclinations revealed increased turbulence and notable interference with waves reflected from the chamber bottom which determined an unexpected drop in efficiency. The optimal performance occurred at an inclination of approximately $55°$, yielding an efficiency of around 12.8%, because it represents the most advantageous and balanced compromise between counter-trend phenomena. A detailed analysis is carried out on several key parameters for the different configurations (e.g., internal and external wave elevations, crest phase shifts, pressures, hydraulic loads, efficiency, etc.) to reach the most in-depth analysis possible of the complex phenomena that come into play. Lastly, the study also discusses the additional structural and functional benefits of inclined walls over traditional parallelepiped-shaped chambers, both from a structural and construction point of view, and for the possible use for coastal defense. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article

Extensive research has focused on optimizing energy consumption in residential buildings based on indoor thermal conditions. However, modeling the energy and thermal behavior of non-residential buildings presents greater challenges due to their complex geometries and the high computational cost of detailed simulations. Simplifying input variables can enhance the applicability of artificial intelligence techniques in predicting energy and thermal performance. This study proposes a neural network-based approach to characterize the thermal–energy relationship in commercial buildings, aiming to provide an efficient and scalable solution for performance prediction. Consumptions trends for a building are generated using the EnergyPlus™ dynamic simulation software over a timespan of a year in different locations, and the data are then used to train neural network models. Uncertainty analyses are carried out to evaluate the behavior effectiveness of the artificial neural networks (ANNs) in different weather conditions, and the root mean square error (RMSE) is calculated in terms of mean air temperatures. The results show that this approach can reproduce the functional relationship between input and output data. Three different ANNs are trained for the northern, central, and southern climatic zones of Italy. The southern region’s models achieved the highest accuracy, with an RMSE below 0.5 °C; whereas the model for the northern cities was less accurate, since no specific trend in plant management was present, but it still achieved an acceptable accuracy of 1.0 °C. This approach is computationally lightweight; inference time is below 5 ms, and can be easily embedded in optimization algorithms for load dispatch or in microcontroller applications for building automation systems. Full article

This study investigated the synergistic effects of vegetation configurations and microclimate factors on seasonal thermal comfort in a semi-enclosed university courtyard in Wuhan, located in China’s Hot Summer and Cold Winter climate zone (Köppen: Cfa, humid subtropical). By adopting a field measurement–simulation–validation framework, spatial parameters and annual microclimate data were collected using laser distance meters and multifunctional environmental sensors. A validated ENVI-met model (grid resolution: 2 m × 2 m × 2 m, verified by field measurements for microclimate parameters) simulated 15 vegetation scenarios with varying planting patterns, evergreen–deciduous ratios (0–100%), and ground covers. The Physiological Equivalent Temperature (PET) index quantified thermal comfort improvements relative to the baseline. The optimal grid-based mixed planting configuration (40% evergreen trees + 60% deciduous trees) significantly improved winter thermal comfort by raising the PET from 9.24 °C to 15.42 °C (66.98% increase) through windbreak effects while maintaining summer thermal stability with only a 1.94% PET increase (34.60 °C to 35.27 °C) via enhanced transpiration and airflow regulation. This study provides actionable guidelines for climate-responsive courtyard design, emphasizing adaptive vegetation ratios and spatial geometry alignment. Full article

Leaf biochemical traits are crucial for understanding plant physiological status and ecological dynamics. Partial least squares regression (PLSR) models have been widely used to estimate leaf biochemical traits from spectral reflectance information. However, variations in sun–sensor geometry, the sensor field of view, and the random orientation of leaves can introduce multi-angular reflection properties that differ between leaf sides. In this context, the transferability of PLSR models across different leaf sides and viewing zenith angles (VZAs) remains unclear. This study investigated the potential of multi-angular spectral reflection from dorsiventral leaves to improve the transferability of PLSR models for estimating the leaf chlorophyll content (LCC) and equivalent water thickness (EWT). We compared models trained using multi-angular data from both leaf sides with models trained using nadir data (from the adaxial side, abaxial side, or their combination). The results show that the PLSR models trained with multi-angular data from both leaf sides outperformed the models trained with nadir data, achieving the highest accuracy in estimating biochemical traits (LCC: R² = 0.87, RMSE = 7.17 μg/cm², NRMSE = 10.71%; EWT: R² = 0.86, RMSE = 0.0015 g/cm², NRMSE = 10.00%). In contrast, the PLSR models trained using single-angle reflection from either the adaxial or abaxial side showed a lower estimation accuracy and greater variability across leaf sides and VZAs. The superior performance across datasets obtained under different measurement conditions (e.g., integrating spheres and leaf clips) further confirmed the improved generalizability of the PLSR model trained with multi-angular data from dorsiventral leaves. These findings highlight the potential of the multi-angular spectral reflection of dorsiventral leaves to enhance the estimation of biochemical traits across various leaf sides, viewing angles, and measurement conditions. They also underscore the importance of incorporating spectral diversity into model training for improved transferability. Full article

Traditional methods of synthesizing nanoparticles often rely on physical and chemical processes using synthetic hazardous chemicals. In contrast, the rise in green chemistry emphasizes using bioactive compounds from plants for the eco-friendly synthesis of nanostructures. These green synthesis techniques are increasingly recognized for their simplicity, cost-effectiveness, and ability to yield non-toxic by-products, an approach that aligns with sustainable practices. In this research, a straightforward, cheap, environmentally friendly, and sustainable procedure was developed to fabricate Zinc oxide nanoparticles (ZnO-NPs) employing three different pulp extracts: Agave (Agave americana), Chiku (Manilkara zapota), and Soursop (Annona muricata) to serve in the synthesis as capping, reduction, or stabilization agent. Analytical characterization techniques confirmed the successful phytosynthesis of ZnO-NPs, evidenced by significant absorbance peaks of UV-Vis spectra at 362 nm, and the chemical composition of ZnO without noticeable traces of phytochemical residues by carrying out ATR-FTIR analysis. SEM, STEM microscopies, and XRD analysis verified that the ZnO nanoparticles possess spherical geometries and hexagonal crystal structures. The average size of these nanoparticles was around 15.94, 18.08, and 23.32 nm for Agave, Chiku, and Soursop extract-based synthesis, respectively. Additionally, the in vitro antibacterial activity of phytosynthetized ZnO-NPs was evaluated against E. coli and S. aureus, confirming effective bacterial growth inhibition and demonstrating their significant antimicrobial potential. Full article

This study investigated the phytochemical composition and biological activities of Myrtus communis essential oil (EO) from Algeria, focusing on its antimicrobial, antifungal, and insecticidal properties using in vitro and in silico approaches. Gas chromatography–mass spectrometry (GC-MS) analysis identified myrtenyl acetate (57.58%), 1,8-cineole (17.82%), and α-terpineol (6.82%) as the major constituents. M. communis EO exhibited significant antibacterial activity, particularly against Staphylococcus aureus (13.00 ± 0.70 mm) and Salmonella typhimurium (13.00 ± 1.50 mm), with moderate inhibition of Bacillus subtilis (10 ± 1.00 mm) and Escherichia coli (9.00 ± 0.70 mm), while Pseudomonas aeruginosa showed resistance. The antifungal activity was notable against Fusarium oxysporum (16.50 ± 0.50 mm), Aspergillus fumigatus (11.00 ± 1.00 mm), and Penicillium sp. (9.00 ± 0.60 mm) but ineffective against Aspergillus niger. Insecticidal activity against Tribolium castaneum was evaluated using contact toxicity, fumigation toxicity, and repellent activity assays. The EO demonstrated potent insecticidal effects, with an LC₅₀ value of 0.029 µL/insect for contact toxicity and 162.85 µL/L air for fumigation after 96 h. Additionally, the EO exhibited strong repellent activity, achieving 99.44% repellency at a concentration of 0.23 mg/cm² after 24 h. Density functional theory (DFT) calculations provided insights into the molecular geometry and electronic properties of the key bioactive compounds. Molecular docking studies evaluated their binding affinities to bacterial enzymes (DNA gyrase, dihydrofolate reductase6, and Gyrase B) and insecticidal targets (acetylcholinesterase), revealing strong interactions, particularly for geranyl acetate and methyleugenol. These findings highlight M. communis EO as a promising natural antimicrobial and insecticidal agent, with potential applications in plant protection and biopesticide development. Full article

Urban wind, and particularly turbulence present in the roughness zone near structures, poses a critical challenge for next-generation drones. Complex flow patterns induced by large buildings produce significant disturbances that the vehicle must reject at low altitudes. Traditional turbulence models, such as the von Kármán model, underestimate these localized effects, compromising flight safety. To address this gap, we integrate high-resolution time and spatially varying urban wind fields from Large Eddy Simulations into a flight dynamics simulation framework using vehicle plant models based on configuration geometry and commonly deployed Ardupilot control laws, enabling a detailed analysis of drone responses in urban environments. Our results reveal that high-risk flight zones can be systematically identified by correlating drone response metrics with the spatial distribution of Turbulent Kinetic Energy (TKE). Notably, maximum g-loads coincide with abrupt TKE transitions, underscoring the critical impact of even short-lived wind fluctuations. By coupling advanced computational fluid dynamics with a real-time vehicle dynamics model, this work establishes a foundational methodology for designing safer and more reliable advanced air mobility platforms in complex urban airspaces. This work distinguishes itself from the existing literature by incorporating an efficient vortex lattice aerodynamic solver that supports arbitrary fixed-wing drone platforms through the simple specification of planform geometry and mass properties, and operating full-flights throughout a time and spatially varying urban wind field. This framework enables a robust assessment of stability and control for a wide range of fixed-wing drone platforms operating in urban environments, with delivery drones serving as a representative and practical use case. Full article

Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants. Full article