Search Results (343)

Modern distribution networks operate under increasingly demanding conditions, characterized by the integration of distributed energy resources, unbalanced three-phase operation, low measurement redundancy, variable topologies, and data uncertainty. In this context, distribution system state estimation (DSSE) is a key tool for operational monitoring; however, its practical deployment is often hindered by topological inconsistencies and gross measurement errors. This paper proposes an integrated and modular deep learning-based methodological framework that combines active topology identification (ATI), gross error detection (GED), error-type identification (ETI), error-location identification (ELI), measurement reconstruction and correction (MRC), and DSSE. The ATI module is formulated as a global multiclass classifier, whereas the subsequent modules are trained as topology-specific models. Compromised measurements are handled through an iterative GED–ETI–ELI–MRC loop that detects, identifies, locates, and corrects one anomalous measurement per iteration before re-evaluating the input vector. The proposed methodology was validated by using simulation-based scenarios generated in OpenDSS for a real unbalanced three-phase 240-node distribution feeder. The results show that no single architecture is dominant across all subproblems: WaveNet1D achieved the best relative performance in ATI, GED, and ETI; EncDec-CNN in ELI; NBEATS1D in MRC; and EncDec-GRU in DSSE. Additionally, WLS estimators based on both nodal voltages and branch currents failed to achieve numerical convergence on the 240-node test system under the evaluated conditions, a finding consistent with recent literature reporting analogous convergence failures in distribution networks of similar or smaller scale. Furthermore, the integrated evaluation shows that omitting ATI increases the voltage-magnitude MAE by a factor of 12.3 and the voltage-angle MAE by a factor of 8.1 with respect to the complete framework, whereas omitting only the compromised-measurement treatment increases these errors by factors of 1.8 and 1.9, respectively. The total offline computational cost was approximately 1587.9 h (66.2 GPU-days), while the online inference latency was approximately 0.45 ms per sample, making the framework compatible with AMI- and SCADA-based monitoring cycles. These findings confirm that topological consistency is the dominant factor in DSSE accuracy and that iterative measurement correction meaningfully improves estimator robustness under anomalous measurement conditions. Full article

This paper establishes a multi-scale nested sub-modeling finite element simulation framework for the deformation and damage analysis of large-scale structures under complex loading conditions. By sequentially transferring displacement solutions from the global model to local sub-models, the framework enables progressive high-resolution analysis from the macroscopic scale (>10 m) down to the microscopic scale (~1 μm), thereby significantly improving solution accuracy in critical regions while maintaining computational efficiency. The proposed approach is validated on a shell structure subjected to hydrostatic pressure and on a plate with a central crack. The results show that the relative errors of stress and strain along specified paths in the shell structure are within 5%, while the relative errors of the stress intensity factor along the crack front in the cracked plate are also below 5%. Furthermore, the framework is integrated with the crystal plasticity finite element method, and a fatigue indicator parameter model based on the accumulated equivalent plastic strain is established to predict the shear fatigue life of Ti-6Al-4V ELI titanium alloy. The predicted fatigue lives are in good agreement with experimental data, with all errors below 10%. This study demonstrates that the proposed sub-modeling method can accurately transfer multi-scale mechanical responses and achieve localized refinement analysis of large-scale structures and can be effectively used for crystal plasticity simulations and fatigue life assessment. Full article

The frequency and intensity of heatwaves are increasing annually worldwide due to climate change. Combined with the urban heat island effect, elevated heat stress episodes threaten the survival and performance of urban trees, in turn reducing their ecosystem benefits. For this reason, the foliar heat tolerance of 35 Malus genotypes (two species, 32 cultivars, one variety, one hybrid) was evaluated under controlled laboratory assays. Heat injury to foliar tissue was quantified using chlorophyll fluorescence (Fv/Fm) to assess photosystem II (PSII) damage and an electrolyte leakage index (ELI) to evaluate cellular membrane integrity. A preliminary dose–response experiment using six genotypes exposed to a temperature gradient (40–50 °C) was conducted to establish thermal response curves and derive LT₅₀ values (temperature at 50% decline in Fv/Fm). These analyses confirmed substantial genotypic variation in thermal tolerance and identified 45 °C as an optimal discriminatory temperature for large-scale screening. This temperature was subsequently applied to assess heat injury across all 35 genotypes. Measurements were conducted in May (spring foliage) and August (summer foliage) to evaluate ontogenetic influences. In some instances, only one genotype was available for experimental purposes. Consequently, conclusions regarding genotypic differences in heat tolerance are based on replicated datasets, whereas genotypes represented by single-tree sampling are presented for descriptive purposes only. Heat stress significantly affected Fv/Fm and ELI, with strong genotype and seasonal effects recorded. In most genotypes, foliar damage was greater in spring than in summer. Good correlations between Fv/Fm and ELI confirmed their value as complementary physiological measures of heat tolerance in plants. Of the 35 genotypes evaluated, Malus sargentii, M. ‘Prairifire’, M. baccata ‘Jackii’, M. ‘Royal Fountain Huber’ and M. Donald Wyman were the most heat tolerant. The substantial variation in foliar heat tolerance detected across the 35 genotypes tested demonstrates potential for selecting Malus genotypes with superior foliar heat tolerance and highlights opportunities for identifying heat resilient candidates among other under-utilized urban tree taxa. Full article

Neonatal diarrhea remains a significant threat to piglet health, resulting in substantial economic losses worldwide. Among the viral pathogens associated with this condition, rotavirus (RV) has been extensively reported in Brazil; however, lineage-level classification of circulating strains remains limited. This study aimed to characterize G and P genotypes of porcine RV field strains associated with diarrhea in piglets in Southern Brazil. A total of 10 fecal samples were collected by field veterinarian from diarrheic suckling piglets aged 1 to 14 days and analyzed by RT-PCR for the detection of RV species A, B, C, and H. RV species A (RVA) was detected in 90% (9/10) of the samples, while no other RV species were identified. Genotyping based on the VP7 and VP4 genes revealed a single G3P[6] genotype combination in all RVA-positive samples. Nucleotide (nt) and deduced amino acid (aa) sequence analysis revealed high genetic similarity among strains, with values of up to 99.3% for nt and 98.0% for aa of the VP7 gene and 100% for the VP4 gene (nt and aa). Phylogenetic analysis indicated that the VP7 sequences clustered with Brazilian G3 strains, forming a distinct group consistent with a novel lineage (putative G3-XII), whereas VP4 sequences supported a new sublineage (putative P[6]-Ig). These findings demonstrate low genetic variability of RVA field strains in this neonatal diarrhea outbreak, suggesting the circulation of a single viral population. They also emphasize the importance of continuous molecular surveillance to gain a deeper understanding of viral evolution and transmission dynamics in swine populations. Full article

The aim of the current study is to determine the nutritional value and the content of the biologically active components in kidney vetch (Anthyllis vulneraria L.). It is established that the dry biomass contains substantial amounts of proteins and carbohydrates, primarily dietary fiber, while the total oil content is relatively low (below 3.0%). The isolated glyceride oil represents the complete lipid fraction derived from all plant parts (leaves, stems, and flowers). The glyceride oil of A. vulneraria is notable for its high levels of biologically active constituents, particularly sterols, tocopherols, and phospholipids. Palmitic (30.3%) and oleic (11.5%) acids dominate the fatty acid profile; β-sitosterol, α-tocotrienol, and α-tocopherol are the major sterol and tocopherol components, respectively. On the other hand, phosphatidylinositol, together with phosphatidic acids, prevails within the phospholipid fraction. Based on the obtained fatty acid composition, several important ratios were calculated—unsaturated fatty acids (UFA)/saturated fatty acids (SFA), saturated fatty acids/monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA)/saturated fatty acids, and n-6/n-3, providing an integrated assessment of the lipid quality. The PUFA/SFA value (0.24) suggests relatively high oxidative stability. In contrast, the n-6/n-3 ratio (0.86) shows a balanced distribution of essential fatty acids, which is associated with favorable nutritional properties. Full article

The study objectives were to evaluate the in vitro and in vivo performance of additive manufacturing (AM) Ti6Al4V ELI alloy compared to that of a commercially available dental implant. Two AM shapes with the solid or lattice structures on the solid substrate were used: in vitro test: disk shapes (10.0 mm/dia. 2.0 mm/thick) and in vivo test: AM shapes matching the overall geometry of a commercial implant (3.0 mm/dia. 8.0 mm/length). Six disk specimens were placed in direct contact with Balb/c 3T3 fibroblasts for 72 h. Cytotoxicity was assessed with adenosine triphosphate activity. Four implant-shaped specimens were placed in the femurs of three rabbits and retrieved after 6 weeks. Osseointegration was evaluated by push-out testing and histological analysis. Data were analyzed using one-way ANOVA (α = 0.05). Surface roughness (µm) of AM-solid, AM-lattice, and a commercial implant were 8.02, 9.00, and 1.46, respectively. Cytotoxicity was not statistically different compared to surface configuration and Teflon^® controls (p > 0.05). Push-out test results were not significant between implants: the shear stiffness of commercial > AM-lattice > AM-solid (p > 0.05). Histological analysis demonstrated osseointegration without inflammatory responses in the surrounding bone tissue for all implants. While some processes and improvements are still required, AM remains a promising method for fabricating customized porous implants in the future. Full article

Accurate prediction of contaminant transport and self-purification processes in rivers remains challenging because pollutant dispersion, biochemical reactions, and hydrodynamic conditions interact across multiple spatial scales. This study aims to develop and compare mathematical models for soluble contaminant transport and biodegradable organic matter removal in channels and rivers. Unsteady advection–diffusion–reaction equations were formulated for one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) transport scenarios and solved through numerical techniques based on the transformation of partial differential equations into systems of ordinary differential or algebraic equations. In parallel, the classical Streeter–Phelps model and an extended formulation incorporating turbulent diffusion were implemented to evaluate organic load degradation and oxygen deficit dynamics. Simulations were performed using a Matlab R2019a-based computational framework under representative hydraulic and reaction conditions obtained from literature data and empirical correlations. The results showed that, under specific conditions, the 3D model reproduced trends comparable to those predicted by the 2D model, while the latter approached the behavior of the 1D formulation. The Streeter–Phelps model predicted an organic load removal efficiency of 97.74%, a purification index of 1.9564, a critical time of 18.43 h, and a critical distance of 6.93 km. These findings provide a useful framework for river water-quality assessment and support future applications involving complex hydrodynamic and pollutant-loading scenarios. Full article

The significant challenges of machining hard-to-cut materials pose an important problem for the manufacturing industries, as it can lead to increased tool wear, higher machining costs, and reduced productivity. Apart from experimental investigations, which are rather expensive and cannot always provide a comprehensive view of the process outcome due to limitations in measurement techniques, it is possible to use validated models to predict the temperature and stress state of the workpieces or test the effect of different process conditions. Although many Finite Element (FE) models have been developed for the turning process, usually accurate representation of the machining setup with a realistic 3D geometry for both cutting tool and workpiece is not taken into account. Thus, in this work, two different representations of the machining setup, including curved workpiece geometry, which is more rarely studied, are compared for the case of Ti-6Al-4V ELI turning under various conditions, and their effect on the accuracy of the prediction of the cutting force and chip morphology is investigated. It was found that the model with the straight workpiece overpredicts the cutting force to a higher extent compared to the model with the curved workpiece and also predicts a much higher workpiece temperature, whereas chip morphology was mainly affected by feed rate. No noticeable differences were observed between the two models. These results indicate that in most cases, the use of geometry with curved workpiece is more suitable for better prediction of the cutting forces. Full article

Despite the widespread use of phytase in swine diets, there is a need to update the current evidence on its effects on performance, carcass traits, and economic outcomes. This meta-analysis evaluated the impact of phytase supplementation on growth performance, carcass traits, and economic returns in growing–finishing pigs, combining meta-analysis with economic modeling. A systematic review (Web of Science, ScienceDirect, PubMed, and Scopus) revealed 1049 studies, 17 of which met the inclusion criteria (PRISMA). Three treatments were considered: a basal diet (BD), a reduced-P diet without phytase (DRP), and a reduced-P diet with phytase (DRP + P), with effect sizes expressed as weighted mean differences (WMDs). The meta-analysis included performance traits (daily feed intake, DFI; daily weight gain, DWG; feed conversion, FC; and final weight, FW) and carcass traits (carcass weight, CW; carcass yield, CY; backfat thickness, BT; loin eye area, LEA; lean meat percentage, LMP; and lean meat yield, LMY). With respect to BD vs. DRP, P reduction impaired performance and decreased ADG, DFI, and final weight (p < 0.001), especially at reductions ≥0.12%. In DRP + P vs. BD, phytase had no significant effects on performance (WMD = 0.008 kg/day) or carcass traits. In DRP + P vs. DRP, phytase improved performance (ADG: +0.068 kg/day; DFI: +0.106 kg/day; FW: +4.630 kg; FC: −0.115; p < 0.001), with stronger effects in males and at greater P reductions. In DRP + P vs. DB, carcass traits were not significantly affected, whereas DB vs. DRP reduced LEA (WMD = −1.820, p < 0.00), and DRP + P vs. DRP produced subgroup-dependent changes in CW, LEA, and LMY according to sex, phytase source, and diet composition. Economically, phytase increased profit by up to US$ 2.66 (vs. BD) and US$ 5.14 (vs. DRP), remaining advantageous even with enzyme price increases of up to 4×. Overall, phytase supplementation improved performance and economic returns, representing an effective strategy for the development of swine production systems. Full article

Titanium alloy welded joints are key parts of deep-sea pressure hulls, which are subjected to fatigue loadings in service. In this study, axial fatigue tests, mode I fatigue crack growth tests, and mixed-mode I–II fatigue crack growth tests were conducted on the Ti-6Al-4V ELI titanium alloy welded joint, and its fatigue failure mechanism and crack growth behavior is investigated and compared with the base material. The results show that the S–N curve of Ti-6Al-4V ELI titanium alloy welded joints has a very similar slope as the base material, but its fatigue performance is lower than the base material. However, the welded joints exhibit a higher resistance in the near-threshold region under mode I loading compared to the base material. Scanning electron microscope observation indicates that the fatigue crack mainly initiates from gas pores during welding for the Ti-6Al-4V ELI titanium alloy welded joints. Under mixed-mode I–II loading, the stress intensity factor range component $\mathsf{\Delta }{K}_{\mathrm{I}}$ of welded joints is higher than that of the base material, and an equivalent stress intensity factor range model is proposed to describe the crack growth rate under both mode I and mixed-mode I–II loadings. The new model incorporates a parameter dependent on the mode mixity ratio defined by $\mathsf{\Delta }{K}_{\mathrm{I}\mathrm{I}}/\mathsf{\Delta }{K}_{\mathrm{I}}$ in this paper, and it unifies the crack growth data well under mode I and mixed-mode I–II loadings. The paper indicates that the gas pores during welding are an important factor for the poor fatigue performance of Ti-6Al-4V ELI titanium alloy welded joints. Full article

The present work compares the corrosion performance of additively manufactured (AM) Ti-6Al-4V ELI (Extra-Low Interstitials) alloy manufactured by Laser-Powder Bed Fusion (L-PBF) using virgin powder (Cycle 1/C1 sample) and reused powder feedstock after up to 34 cycles (Cycle 34/C34 sample) of manufacturing. The effect of powder reuse is also evaluated for anodizing and Flash-PEO-coated specimens in Harrison’s (25 °C) and Hanks’ solutions (37 °C), representing simulated atmospheric precipitation and physiological conditions, respectively. Specimens were characterized using common metallographic techniques, X-ray diffraction, scanning electron microscopy and optical profilometry. Corrosion resistance was evaluated using cyclic potentiodynamic polarization (PDP) tests. The oxygen content in the Ti-6Al-4V reaches 0.14 wt.% after 34 cycles (C34) of powder reuse, enhancing its passivity in both Harrison’s and Hanks’ solutions. Both virgin and reused powder builds are susceptible to localized corrosion in Hanks’ solution at potentials above 1.75 V. Melt pool borders are thought to be the preferential sites for localized corrosion, as indicated by Volta potential measurements (ΔV = 100 mV). The number of cycles does not significantly affect the current–voltage responses for anodizing and flash-Plasma Electrolytic Oxidation (Flash-PEO) treatments, although anodizing is slightly more responsive to variations in surface roughness (i.e., real specimen area). Anodizing and Flash-PEO reduce the passive current density by nearly two orders of magnitude. Even after surface treatment, the alloy printed with reused powder revealed better passivity. Flash-PEO coatings yielded significant protection against localized corrosion. This unlocks Flash-PEO processing as a successful protection approach for AM biomedical components. Full article

Despite the growing relevance of Agriculture 4.0 technologies for enhancing productivity, decision-making, and sustainability in agri-food systems, their adoption remains uneven in developing-country contexts. This study aims to analyze the perceived severity and co-occurrence structure of barriers to Agriculture 4.0 adoption in the agri-food system of Rio Grande do Sul (RS), Brazil, using an exploratory quantitative design grounded in a barrier co-occurrence perspective rather than a causal or actor-centered network interpretation. An online survey conducted in 2024 with farmers in RS evaluated 25 literature-validated barriers spanning technological, economic, political, social, and environmental dimensions. The analysis combined a Barrier Severity Index (BSI), reliability testing, Principal Component Analysis (PCA), K-means clustering, ANOVA by farm size, and proximity-based co-occurrence networks constructed from highly rated barriers. The results show that economic barriers remain the most severe overall, particularly the lack of affordable solutions, high maintenance costs, and limited infrastructure. At the same time, farm-size-stratified networks reveal distinct association structures: small farms display a more segmented pattern linking affordability and technical access to institutional and capability constraints; medium farms show the most globally integrated co-occurrence structure; and large farms exhibit a dense but more differentiated configuration combining cost, interoperability, skills, and governance-related barriers. These findings are interpreted descriptively, as the networks capture patterns of co-reporting rather than causal interdependence. The study contributes a network-analytic representation of perceived barrier configurations and highlights the need for scale-sensitive policy mixes that address bundles of constraints rather than isolated obstacles. Full article

The fatigue performance of titanium alloys is a critical determinant of the service life and structural integrity for aerospace and marine engineering components. But within the framework of damage tolerance design, resistance to fatigue crack propagation is regarded as a key indicator governing the fatigue performance of these engineering structures. In previous work, while the general fatigue performance of Ti–6Al–4V-0.55Fe alloy has received systematic study, targeted research focusing on its resistance to fatigue crack propagation remains limited. Therefore, in this work, compared with Ti–6Al–4V ELI alloy, the fatigue crack propagation behavior and fracture mechanism of Ti–6Al–4V-0.55Fe alloy with a duplex microstructure were systematically investigated. The results show that when ∆K < 12.75 MPa⋅m^1/2, Ti-6Al-4V-0.55Fe alloy demonstrates superior resistance to fatigue crack propagation. Fractographic analysis indicates that the primary difference between the two alloys lies in the stage of crack initiation and early propagation. This behavior is attributed to the addition of trace Fe, which enhances α/β boundary resistance and thereby retards crack growth. Moreover, crack propagation of TC4-F alloy is also slowed by the increased path length from bypassing the α_p phase. Full article

Ti-6Al-4V ELI (Grade 23) fabricated by Laser Powder Bed Fusion (LPBF) exhibits well-known susceptibility to adhesive wear and tribo-oxidation under dry sliding, yet the tribological consequences of in-process laser remelting remain poorly characterized. This study investigates the influence of an in-layer laser scan strategy (single-scan and double-scan), normal forces in the 5–15 N range, and a sliding speed of 0.10–0.20 m·s⁻¹ on the dry sliding tribological response of additive manufactured Ti-6Al-4V ELI. A full factorial experimental design was carried out and the most significant factors and their contributions to the coefficient of friction, specific wear rate, and contact temperature were identified by a statistical method using a general linear model (GLM). The optimal parameters for both of the scan strategies were predicted using a response surface methodology (RSM). Furthermore, to assess the effect of the laser scan strategy and the in-layer remelting on the local mechanical properties, a microscale and nanoscale indentation was carried out. The results show that the normal load was the dominant factor with a contribution of 89.3% for the coefficient of friction, 54% for the specific wear rate, and 40.5% for the temperature. A significant load–scan strategy interaction that governed the wear behavior was detected. The double-scan strategy exhibited higher wear at 5 N but lower wear at 15 N than the single-scan, a counter-intuitive reversal attributed to the load-threshold tribolayer stabilization promoted by the remelting-induced near-surface microstructural modification. The novelty of this study was the setup of a robust GLM–RSM framework for predictive modeling and optimization of additively manufactured surfaces under tribological loading. Full article

Apple orchards are increasingly adopting anti-hail nets to mitigate climate risks; however, these structures alter canopy reflectance and pose challenges for remote sensing. This study presents an operational framework to map apple orchards under different netting conditions in Vacaria, Brazil. Multisensor surface reflectance data from Sentinel-2 and Harmonized Landsat and Sentinel-2 were used to generate dense spectral index time series combined with field observations. Five spectral indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Bare Soil Index (BSI), were evaluated individually and in combination within a hierarchical classification framework. Random Forest (RF) and one-dimensional convolutional neural networks (1DCNN) were applied as complementary machine learning approaches. RF showed more stable performance across hierarchical levels, while indices contributed differently depending on scale: BSI and NDVI were more effective at broader levels, whereas EVI and SAVI were critical for discriminating net colors. To our knowledge, this is the first study applying multisensor time series and machine learning to map anti-hail net systems in apple orchards. Full article