Search Results (823)

This paper proposes a multiphysics-based optimal design process for a 750 W axial-flux ferrite consequent-pole (AFCP) pump motor aimed at reducing permanent magnet usage. To mitigate the high computational cost associated with repetitive numerical analyses, a metamodel (surrogate model)-based optimization framework is adopted. A consequent-pole (CP) structure is applied to an initial ferrite axial-flux permanent magnet (AFPM) motor, and ten key design variables are selected for optimization. The electromagnetic performance corresponding to variations in these variables is evaluated using three-dimensional finite element analysis (3D FEA), and the resulting dataset is used to construct metamodels. In AFPM motors incorporating ferrite permanent magnets and a CP structure, electromagnetic performance, thermal saturation, and structural stability collectively limit reliable operation. Therefore, a multiphysics-based evaluation is essential. The optimal design is assessed through electromagnetic, thermal, and structural finite element analyses. According to the 3D FEA results, the optimal model achieves a 46.85% reduction in permanent magnet volume while improving efficiency by 0.75%, reaching 95.53%, compared to the initial model. The torque ripple and peak-to-peak cogging torque are reduced by 28.81% and 31.37%, reaching 0.08 Nm and 0.06 Nm, respectively. In addition, the total harmonic distortion (THD) of the back-electromotive force waveform decreases from 12.4% to 2.53%. Stable operating characteristics are confirmed through demagnetization, thermal, and structural analyses, demonstrating that the proposed optimal design process successfully achieves both permanent magnet reduction and overall performance improvement in ferrite-based AFCP motors. Full article

With the rapid growth of vessel traffic and the widespread adoption of the Automatic Identification System (AIS) in recent years, analyzing maritime traffic flow characteristics has become an essential component of modern maritime supervision. Clustering analysis is one of the primary data-mining approaches used to extract traffic patterns from AIS data. Addressing the challenge of assigning appropriate weights to the multidimensional features in AIS trajectories, namely latitude and longitude, speed over ground (SOG), and course over ground (COG). This study introduces an adaptive parameter optimization mechanism based on evolutionary algorithms. Specifically, Particle Swarm Optimization (PSO), a representative swarm intelligence algorithm, is employed to automatically search for the optimal feature-distance weights and the core parameters of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling dynamic adjustment of clustering thresholds and global optimization of model performance. By designing a comprehensive clustering evaluation index as the objective function, the proposed method achieves optimal parameter allocation in a multidimensional similarity space, thereby uncovering maritime traffic clusters that may be overlooked when relying on single-dimensional features. The method is validated using AIS trajectory data from the Xiamen Port area, where 15 traffic clusters were successfully identified. Comparative experiments with two other clustering algorithms demonstrate the superior performance of the proposed approach in trajectory pattern analysis, providing valuable reference for maritime regulatory and traffic management applications. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

Vagococcus species have been isolated from diverse environments, including aquatic, terrestrial, food-associated, and clinical sources; however, plant- and flower-associated representatives remain poorly characterized at the genomic level. In this study, we report the complete genomic sequence and analysis of Vagococcus sp. JNUCC 83, isolated from flowers of Camellia japonica collected on Jeju Island, Republic of Korea. The genome comprises a single circular chromosome of 2,472,896 bp with a GC content of 33.5 mol% and was assembled at high depth (555.43×), resulting in a high-quality complete genome. Genome-based phylogenomic analysis using the Type (Strain) Genome Server (TYGS) showed that strain JNUCC 83 forms a distinct lineage within the genus Vagococcus. Digital DNA–DNA hybridization (dDDH) values were far below the 70% species threshold, and 16S rRNA gene-based phylogeny consistently supported its independent placement, suggesting that JNUCC 83 represents a previously undescribed genomic species. Functional annotation based on EggNOG/COG analysis indicated the enrichment of genes involved in core metabolism and genome maintenance, while antiSMASH analysis identified a terpene-precursor-type biosynthetic locus encoding a polyprenyl synthase. Overall, this study expands the genomic understanding of flower-associated Vagococcus lineages and provides a foundation for future investigations into their ecological roles and potential applications as plant-derived microbial resources. Full article

To address the significant vibration and noise problems caused by the zero-order radial electromagnetic force (REF) in integer-slot permanent magnet synchronous motors (PMSMs), while simultaneously improving the motor’s overall electromagnetic performance, this paper proposes a hierarchical iterative optimization strategy integrating Taguchi methods and genetic algorithms. The optimization objectives include minimizing the zero-order REF amplitude, cogging torque, and torque ripple, while maximizing the average torque, with efficiency and back electromotive force total harmonic distortion (back-EMF THD) treated as constraints. First, an 8-pole 48-slot double-layer embedded PMSM model is constructed. An innovative parameter selection strategy, combining theoretical analysis with finite-element analysis, is employed to investigate the spatial order and frequency characteristics of the electromagnetic force. Subsequently, a sensitivity analysis is performed to stratify parameters: highly sensitive parameters undergo first-round optimization via the Taguchi method, followed by second-round optimization using a multi-objective genetic algorithm. The results demonstrate significant reductions in both the zero-order REF amplitude and cogging torque. Specifically, the motor’s peak vibration acceleration is reduced by 32.96%, and the peak sound pressure level (SPL) drops by 9.036 dB. Vibration acceleration and sound pressure across all frequency bands are significantly reduced to varying extents, validating the effectiveness of the proposed optimization approach. Full article

In this study, a layered multi-objective optimization design method is proposed for a segmented skewed pole permanent magnet synchronous linear motor (PMSLM), considering thrust ripple suppression. Based on a 2-D analytical model, the effects of end force, cogging force, and winding asymmetry force on thrust ripple in PMSLM are analyzed, and the correctness is verified using finite element analysis and experiments. On this basis, a layered multi-objective optimization method is proposed. The whole optimization is divided into three layers. Metamodels of optimal prognosis are established to optimize the structural parameters in a layered manner, achieving a compromise between reducing thrust ripple and increasing average thrust. The effectiveness of the layered multi-objective optimization method is verified through simulation and prototype experiments. The layered structure aims to improve efficiency while ensuring computational accuracy. Full article

This paper presents a practical and cost-effective method for in-motion estimation of a vehicle’s CoG position in all three directions by measuring accelerations during two types of maneuvers: braking (longitudinal and vertical CoG estimation) and cornering (lateral and vertical CoG estimation). The proposed method’s main advantage is that it does not require knowledge of vehicle characteristics, such as mass distribution, suspension geometry, or inertia parameters. It relies solely on the known distances between the sensors and their positions relative to a defined reference point on the vehicle. To validate the developed method, experimental tests were conducted on a prototype vehicle, varying the load conditions for the proposed driving scenarios. The CoG position obtained from dynamic maneuvers was compared with reference values derived from static measurements. The results showed that the proposed method could estimate the CoG position with an average error of 3% in the longitudinal direction, a maximum error of 12% in the lateral direction, and a maximum error of 14% in the vertical direction. Full article

As a highly integrated and compact power unit, the motor-pump finds critical applications in emerging electric vehicle (EV) domains such as electro-hydraulic braking and steering systems, where its vibration and noise performance directly impacts cabin comfort. A key factor limiting its NVH (Noise, Vibration, and Harshness) performance is the electromagnetic vibration and noise induced by the cogging torque of the built-in brushless DC motor (BLDCM). Traditional suppression methods that rely on stator auxiliary slots exhibit certain limitations. To address this issue, this paper proposes a collaborative optimization method integrating multi-parameter scanning and response surface methodology (RSM) for the design of auxiliary slots on the motor-pump’s stator teeth. The approach begins with a multi-parameter scanning phase to identify a promising region for global optimization. Subsequently, an accurate RSM-based prediction model is established to enable refined parameter tuning. Results demonstrate that the optimized stator structure achieves a 91.2% reduction in cogging torque amplitude for the motor-pump. Furthermore, this structure effectively suppresses radial electromagnetic force, leading to a 5.1% decrease in the overall sound pressure level. This work provides a valuable theoretical foundation and a systematic design methodology for cogging torque mitigation and low-noise design in motor-pumps. Full article

Background: The global dissemination of multidrug-resistant (MDR) Salmonella poses a persistent and serious threat to food safety systems. As a leading poultry-exporting country, Thailand requires a comprehensive understanding of how resistance plasmids spread among Salmonella populations within its chicken production chain. Methods: Between March 2023 and February 2024, 223 Salmonella isolates were collected from chicken slaughterhouses and markets in northeastern Thailand. From these, 19 representative MDR Salmonella enterica isolates, selected based on distinct spatiotemporal distributions, underwent whole-genome sequencing. Genomic analyses included sequence typing, core-genome phylogenetics, and screening for antimicrobial resistance genes. Plasmid replicons were identified, and functional annotation was performed using the COG database. Results: Phylogenetic analysis revealed 11 distinct sequence types within the population. Among these, ST1541 and ST50 showed clear evidence of clonal transmission across different production stages, with a notable clustering pattern observed during the winter season. All sequenced isolates exhibited an MDR phenotype. Plasmids were detected in 78.9% of isolates, with conjugative plasmids being the most frequent type (57.9%). The β-lactamase gene bla_TEM-60 was the most prevalent (78.9%) and showed a strong correlation (r ≥ 0.7) with resistance to both ampicillin and cefotaxime. Functional annotation further revealed an abundance of genes involved in carbohydrate and amino acid metabolism across all isolates. Conclusions: These findings indicate that MDR Salmonella dissemination is driven by two synergistic mechanisms: the clonal expansion of fit lineages and the horizontal transfer of conjugative plasmids harboring β-lactamase genes. We identified IncI-gamma-K1 and Col-related plasmids as key vectors in this process. This study advocates for targeted interventions, guided by a One Health approach, that specifically aim to disrupt plasmid transmission at critical control points, such as slaughterhouses, to curb the spread of antimicrobial resistance. Full article

Long-term continuous cropping is a prevalent agricultural practice aimed at maximizing land use efficiency and crop yields, yet it often leads to severe soil degradation, nutrient imbalance, and microbial community disruption. Effective soil remediation strategies are urgently needed to restore soil health and ensure sustainable agricultural production. In this study, we investigated the impact of forest soil amendment on microbial community structure, diversity, and functional potential in long-term continuous cropping soils. Using metagenomic sequencing, we analyzed soils from natural forest (BK), forest soil-amended soils (BCP), and fields under continuous cropping for 15 years (CP15) and 30 years (CP30). Forest soil amendment significantly mitigated microbial diversity loss and structural degradation caused by prolonged monoculture. Alpha diversity analysis revealed that BCP restored microbial diversity to levels comparable to BK, while beta diversity and NMDS analyses showed that microbial community composition in BCP closely resembled that of forest soil. Taxonomic profiling indicated that forest soil amendment enriched beneficial taxa such as Actinobacterota and Acidobacteriota, reversing shifts observed in CP15 and CP30. Functionally, COG and KEGG annotations revealed that BCP soils exhibited higher abundances of genes involved in carbohydrate metabolism, energy production, and nutrient cycling. Notably, the amendment reduced antibiotic resistance genes and virulence factors, potentially improving the microbial risk profile of soil communities. These findings demonstrate that forest soil amendment effectively restores microbial community structure and functionality in degraded soils, providing a nature-based solution for sustainable agriculture. Full article

To overcome the limitations in maneuverability and adaptability of traditional underwater vehicles, a novel hybrid-actuated, multimodal cephalopod-inspired robot is proposed. This robot innovatively integrates a hybrid drive system wherein sinusoidal undulating fins provide primary propulsion and steering, water-flapping tentacles offer auxiliary burst propulsion, and a gear-and-rack center-of-gravity (CoG) adjustment module modulates the pitch angle to enable depth control through hydrodynamic lift during forward motion. The effectiveness of the design was validated through a series of experiments. Thrust tests demonstrated that the undulating fin thrust scales quadratically with oscillation frequency, aligning with hydrodynamic theory. Mobility experiments confirmed the multi-degree-of-freedom control of the robot, demonstrating effective diving and surfacing via the CoG module and high maneuverability, achieving a turning radius of approximately 15 cm through differential fin control. Furthermore, field trials in an outdoor artificial lake with a depth of less than 1 m validated its environmental robustness. These results confirm the versatile maneuvering capabilities of the robot and its robust adaptability to confined and shallow-water environments, presenting a novel platform for complex underwater observation tasks. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

This research determined the milk performance and milk and blood biochemical indicators of dairy goats fed with black oat supplements. The experiment was conducted on 20 French Alpine goats on the 48th day of lactation, divided into two groups of 10 goats each (initial body weights (BW) of 53.90 and 52.15 kg). The research lasted for 30 days, and the monitoring of production properties and blood sampling were carried out on the 1st, 15th, and 30th days of the research. Goats in the BOG group were fed a diet in which yellow oats were gradually replaced with black oats, whereas goats in the COG group received a diet containing yellow oats (CP: 143.64 vs. 150.40 g/kg DM; EE: 48.60 vs. 48.80 g/kg DM; NEL: 7.18 vs. 7.19 MJ/kg DM). These values were subjected to repeated-measures analysis using the PROC MIXED procedure and were further analyzed using Tukey’s post hoc test. Compared with the COG group, no significant differences were observed in the BOG group for the production performance of the goats, except for a slightly increased milk yield (1264.94 vs. 1542.10 g/day, p = 0.098) and reduced concentrations of urea and globulin in the milk of the BOG group (7.90 vs. 7.05 mmol/L, p = 0.081; 5.16 vs. 3.96 g/L, p = 0.091). In the blood of BOG goats, a significantly lower urea concentration was detected (8.75 vs. 7.05 mmol/L, p = 0.020). However, compared with the COG group, goats in the BOG group showed a slight increase (p > 0.05) in protein fractions and a decrease in lipid-related indicators in the blood. These findings confirm the moderate benefit of black oats as a dietary supplement in feed for lactating goats. Full article

Background/Objectives: Chemotherapy can negatively affect cognitive functioning through direct and indirect mechanisms, with 15–50% of patients experiencing a decline that impacts quality of life. While neurocognitive tests are the main assessment tool, self-reported measures provide valuable additional insights. The aim of this study was to evaluate the cognitive decline of patients during the early stages of chemotherapy treatment and its impact on their quality of life, as well as to outline future perspectives for an early intervention model. Materials and Methods: This prospective longitudinal study was conducted on 40 patients aged 18 to 64 years. Data were collected using the FACT-Cog v.3 questionnaire, administered at the beginning of the first chemotherapy cycle (T0), and then again after approximately 3–4 weeks (T1) and 6–8 weeks (T2). Results: The data show a progressive decline in perceived cognitive abilities (PCI: 72.10 → 64.43; PCA: 25.70 → 20.90) and overall quality of life (9.73 → 8.75) from T0 to T2. A significant positive correlation was found between perceived cognitive abilities and quality of life, supporting the link between cognitive decline and quality of life. Questionnaire variables reliably predicted changes in quality of life two months after chemotherapy [F(4, 35) = 3.91, p = 0.01]. Conclusions: The findings show a decline in quality of life as chemotherapy progresses. However, this decline can be predicted from the second month of treatment using the FACT-Cog v.3 questionnaire. The findings further highlight even more the value of integrating early empowerment, rehabilitative and psychoeducational programs to mitigate cognitive decline and improve quality of life. Full article

Background: Survival among children and adolescents and young adults (AYA) with cancer has improved substantially over recent decades; however, dominant survivorship models remain reactive—activated post-treatment and anchored to static exposure- and organ-based screening. This design underuses the anticipatory window at diagnosis and overlooks environmental and social determinants that modulate outcomes across the life course. Methods: We narratively reviewed international frameworks including the Children’s Oncology Group (COG), the International Late Effects of Childhood Cancer Guideline Harmonization Group (IGHG), the Pan-European Network for Care of Survivors after Childhood and Adolescent Cancer (PanCare) and the National Comprehensive Cancer Network (NCCN), and synthesized evidence on environmental determinants, exposomics, toxicogenomics, and implementation. Building on two decades of real-world practice, we describe the evolution from the Pediatric Environmental History (PEHis) to the Ambiomic Health Compass (AHC), integrating genomic, exposomic, geospatial, clinical, and biomonitoring layers into routine care. In this framework, survivorship is conceptualized as beginning at the time of cancer diagnosis (“day 0”). Results: PEHis operationalizes guideline-based care with structured environmental and social assessment, personalized plans, and community integration, contributing to improved survival, healthier behaviors, reduced treatment-related mortality and stronger oncology–primary-care coordination. AHC extends PEHis with dynamic risk recalibration, contextual alerts, targeted biomonitoring, and toxicogenomic interpretation, enabling anticipatory decisions from day 0. The manuscript summarizes the paradigm shift (current vs. Ambiomic models), the domain-specific expansion over existing guidelines, the core clinical/system tools, and time-bound metrics (12, 24, 60 months) to support implementation and evaluation. Conclusions: Survivorship should move upstream—from late surveillance to ambiomic, exposure-aware care beginning at diagnosis. Integrating advanced exposomics, mutational epidemiology, and explainable analytics can reduce preventable events and chronicity, enhance equity, and align pediatric oncology with planetary health. The PEHis–AHC continuum offers a scalable blueprint for next-generation survivorship programs in Europe and beyond. Ambiomic medicine does not replace precision medicine—it completes and extends it by integrating exposomics, social context, and anticipatory analytics from day 0. Full article