Search Results (1,395)

To address the problem that the maximum error of back-EMF observers increases with an increase in motor speed, based on an extended state observer, this paper designs an angle-compensation strategy based on a proportional–integral controller and an extended state observer, according to the principle that a proportional–integral controller uses an integral link to eliminate steady-state error. After obtaining the back EMF, the proportional–integral phase-locked loop is often used to extract the angle and speed from the observed back EMF. However, this method will produce steady-state errors when the motor is accelerated, and the integration link is prone to overshoot, so it exhibits some defects. Therefore, this paper uses an extended state observer instead of a proportional–integral regulator to build an improved phase-locked loop based on an extended state observer. Full article

SSRMS refers to a Space Station Remote Manipulator System. The robotic arm of the Wentian module can complete tasks such as supporting astronauts’ extravehicular activities, installing and maintaining payloads, and inspecting the space station. The seven-joint SSRMS manipulator is critical for space missions. This study aims to build its kinematic model via screw theory. It simplifies SSRMS to right-angle rods, defines joint screw axes, twist coordinates, and initial pose matrix. Using the PoE (Product of Exponentials) formula, the 7-DOF forward kinematics equation is derived. In addition, it derives fixed joint angle for inverse kinematics, including analytical solutions and numerical solutions. It elaborates analytical solutions for fixing joints 1/7 and 2/6 and numerical solutions for fixing joints 3/4/5, solves all joint angles via kinematic decoupling, and addresses special cases. Experiments with China’s space station small arm parameters show the probability of meeting the accuracy threshold 10^–4 is 99.79%, verifying model effectiveness, while noting singularity-related weak solving areas. This provides a reliable basis for subsequent inverse kinematics optimization. Full article

Escalating climate change and the increasing frequency of weather extremes pose a threat to the resilience of urban environments and human health, highlighting the urgent need for implementing energy-efficient interventions and reducing building cooling loads. This study investigates the passive building envelope retrofit technologies of external shading, electrochromic windows, and thermochromic windows through a multi-criteria evaluation analysis based on energy savings, economic performance, and indoor thermal comfort improvement. Thermochromic windows are discerned by a mean colour transition temperature of 34 °C and operate throughout the entire year, while electrochromic windows are activated only during cooling periods. Both technologies present total solar transmittance indices of 72.6% and 8.4% in the bleached and tinted state, respectively. External shading devices are either static or movable, applied with an inclination angle, and are either standalone interventions or combined with chromogenic glazing. Eight retrofit scenarios are investigated for a single-story, fully electrified residential building in Athens, Greece. The building features south- and east-oriented windows, which is an appropriate case to assess the effectiveness of these passive envelope cooling technologies in regulating solar heat gains. Thermal comfort is assessed using Fanger’s PMV (predicted mean vote) and PPD (Predicted Percentage of Dissatisfied) indices. The combination of electrochromic windows and movable external shading yields the highest annual electricity savings at 22.2% and reduces the PPD by 15.8%. Local static shading, on the other hand, ranks as the optimal retrofit solution in terms of economic performance, with a life-cycle cost of €6378, a 9.3% improvement in thermal comfort, and a corresponding reduction of 626 thermal discomfort hours. While the proposed multi-criteria framework can be applied to other buildings and climates, the quantitative results reported here are linked to the specific case examined: a residential building with south- and east-facing glazing in Athens, Greece, representing Mediterranean climatic conditions. Full article

In geotechnical foundation engineering, the bearing capacity and settlement behaviour of clay soils are key parameters governing foundation performance. Insufficient bearing capacity and excessive settlements limit economical foundation design and may lead to increased structural deformations. This study investigates the physical and mechanical properties of low-plasticity (CL) and high-plasticity (CH) clay soils obtained from boreholes drilled in Düzce Province, Türkiye, where bearing capacity, settlement, and relevant soil parameters were determined through field and laboratory testing and subsequently evaluated using statistical analyses. The calculated bearing capacity values ranged from 192 to 556 kPa, while settlement values varied between 0.88 cm and 5.83 cm. The corresponding maximum-to-minimum ratios were approximately 2.89 for bearing capacity and 6.62 for settlement. The effects of unit weight, water content, particle size distribution, groundwater level, internal friction angle, and cohesion on the bearing capacity and settlement behaviour of the examined clay soils were systematically assessed. The results indicate that unit weight is the most influential parameter for increasing bearing capacity and reducing settlement in CL-type soils, whereas the cohesion coefficient is the dominant parameter in CH-type soils. The results indicate that variations in shear strength and moisture-related parameters exert a significant influence on foundation performance. The findings provide quantitative insight into the relative impact of key soil parameters and offer practical implications for the design of building foundations in clayey soils under similar geological and geotechnical conditions. From a practical perspective, the findings support foundation design, especially in earthquake-prone areas, by accounting for soil bearing capacity and ensuring that settlements remain within permissible limits to maintain long-term structural performance. Full article

Additive manufacturing is now an integral part of digital prosthodontic workflows, and although stereolithography (SLA) is widely used for denture base fabrication, the dimensional accuracy of printed dentures remains highly dependent on manufacturing parameters, particularly build orientation. This study evaluated the influence of build orientation on the trueness and precision of SLA-printed maxillary and mandibular denture bases. Thirty complete denture bases were fabricated using SLA and divided into three groups according to build orientation: 0°, 45°, and 90° (n = 10). The intaglio surfaces of the printed dentures were scanned and compared with their corresponding digital reference models using three-dimensional inspection software. Trueness was quantified using root mean square error (RMSE) and directional deviations, while precision was assessed based on the variability of RMSE values within each group. Statistical analysis was performed using one-way ANOVA and Tukey’s post hoc test (p ≤ 0.05). Build orientation significantly affected the trueness of maxillary denture bases, with dentures printed at 90° demonstrating the lowest RMSE values. No statistically significant differences in trueness were observed among build orientations for mandibular denture bases. Precision was not influenced by build orientation for maxillary dentures, whereas mandibular dentures printed at 90° exhibited significantly greater variability compared with 0° and 45°. Build orientation is a critical factor influencing the dimensional accuracy of SLA-printed denture bases in an arch-dependent manner. Optimizing build orientation may enhance both accuracy and reproducibility, thereby improving the predictability and clinical reliability of additively manufactured denture bases. Full article

Nucleation remains one of the least understood steps during protein crystallization, although it strongly impacts product quality attributes, including total crystal numbers, final crystal size distributions, and thus downstream processing. In this work, the nucleation behavior of Lactobacillus brevis alcohol dehydrogenase (LbADH) wild type (WT) and five mutants (Q207D, Q126H, K32A, D54F, and T102E) is investigated in a stirred 7 mL crystallizer monitored by in situ multi-angle dynamic light scattering (MADLS). Nucleation was studied with highly pure homotetrameric LbADHs by establishing a crystallization, lyophilization, and re-solubilization protocol combined with size exclusion chromatography (SEC) and size exclusion high-performance liquid chromatography (SE-HPLC), yielding tetramer purities above 94% and removing low molecular weight impurities. During stirred batch crystallizations initiated by the addition of polyethyleneglycol 550 monomethyl ether (PEG 550 MME), SEC and SE-HPLC revealed decreasing tetramer peak areas but essentially constant peak apex positions, indicating that no long-lasting oligomeric intermediates accumulate at detectable levels. Time-resolved MADLS measurements using a custom-made flow-through cuvette in a bypass to the stirred crystallizer uncovered transient cluster populations. All protein variants exhibited an initial tetramer peak, followed by the formation of larger aggregates and a rapid rise in signal above a hydrodynamic diameter of 1000 nm, coinciding with the onset of macroscopic turbidity. A simple mesoscale nucleation model was formulated, yielding end-of-nucleation times, crystallized fractions, critical soluble concentrations, and apparent nucleation rate constants. The crystal contact mutations modulate both the timing and magnitude of the nucleation burst (rapid build-up of nuclei/cluster populations). The mutant Q207D showed strongly attenuated nucleation compared to the WT, whereas the other mutants (K32A, D54F, and particularly T102E) display markedly accelerated nucleation at nearly invariant critical concentrations. The combined workflow demonstrates how in situ MADLS, together with a tailored kinetic description, can provide mechanistic insight into protein nucleation in stirred batch crystallizers. Full article

In the belt-heated incremental sheet forming process, the influence of process parameters on the forming limit angle significantly affects the forming accuracy and quality of components. Through macro and micro experiments, this study comprehensively analyzed the effect of key process parameters on the forming limit angle and identified forming temperature, tool head diameter, and step down as the primary factors that enhance the forming limit angle. Building on this, the dislocation density and grain size of the material under various forming temperatures, tool head diameters, and step-down values were investigated, clarifying the influence of these parameters on dislocation density and grain size in belt-heated incremental sheet forming. Furthermore, the dislocation density and grain size in the cross-section of the deformed region were calculated through micro-tests, revealing the variation patterns of dislocation density and grain size under different process conditions. These findings verified the macro–micro mechanism of the effect of process parameters on the forming limit angle and led to the establishment of a control method for the forming limit angle in belt-heated incremental sheet forming. Full article

Solar absorbers (SAs) are central to building-integrated solar-thermal systems; however, conventional black SAs, despite their high solar absorptance (α_s), offer limited aesthetic flexibility and are therefore poorly suited to modern architectural façades. Brightly colored SAs are widely assumed to suffer from intrinsically low α_s, creating a long-standing trade-off between color vibrancy and energy performance. Here this study reports a dielectric/absorber/dielectric/absorber (D/A/D/A) multilayer architecture, in which the absorber layer is composed of a TiO₂–TiON–C composite, that overcomes this limitation and enables colored solar absorbers (CSAs) with reflectance >20%, α_s > 0.90, wide viewing angles, strong self-cleaning capability, high corrosion resistance and exceptionally long projected service lifetimes. These results demonstrate that vivid coloration and high solar absorptance can be simultaneously achieved without compromising environmental durability. To highlight architectural applicability, we further implement a complementary-color contrast strategy for façade design, yielding visually striking, highly recognizable, and low-cost exterior surfaces. This approach enhances aesthetic integration while significantly strengthening the marketability of CSA-based building envelopes for next-generation sustainable architectural systems. Full article

Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy. Full article

With the accelerated low-carbon transition of the global energy mix, offshore wind power (OWP) is one of the fastest-growing renewable resources and is often integrated with conventional thermal units into a bundled export transmission system. Under sudden large disturbances, the lack of inertia support makes rotor-angle instability prone to occur, which undermines sustainable operation. Battery energy storage systems (BESS) provide fast emergency power support, and an effective control strategy can enhance transient rotor-angle stability while improving operational sustainability. Accordingly, equivalent-circuit models of the regional export system are established for the before-fault, during-fault, and after-fault stages. Building on the extended equal area criterion (EEAC) and the low-voltage ride-through (LVRT) capability of OWP, the stabilizing mechanism of BESS participation is examined from the perspectives of optimal power and timing, thereby yielding an optimal BESS control strategy for improving transient rotor-angle stability in regional renewable export systems. Finally, a regional renewable export system is implemented in MATLAB/Simulink R2022b, where severe contingencies are imposed to validate the effectiveness of the proposed BESS control strategy. Full article

We developed a deep learning model for automated extraction and assessment of earthquake damage from dashcam and post-disaster images. By combining a custom-designed deep multi-layer perceptron model with an enhanced feature extraction methodology, we accurately classify image patches into “No Damage” (Class 0) and “Damage” (Class 1). The proposed model incorporates a rich set of image-based features, including color statistics, edge properties, and texture descriptors, along with strategies to mitigate class imbalance. Experimental results demonstrate the model’s high performance in identifying damaged areas, particularly its excellent recall for the “Damage” class, which is critical for rapid disaster response and damage mapping. Full article

This study proposes a sustainable urban planning strategy that enhances building energy self-sufficiency through photovoltaic-based renewable energy generation. This research focused on high-rise residential complexes and introduces an adaptive facade system designed to efficiently utilize the extensive solar-exposed surfaces of building facades. The system was programmed to automatically adjust according to the optimal tilt angle, which reflects monthly variations in solar altitude and azimuth, thereby maximizing energy generation. To further improve the effectiveness of the adaptive facade system, a “bridge-type apartment complex” layout was developed, in which building orientations varied around a south-facing axis to include southeast and southwest orientations. The proposed urban configuration was developed using a parametric design within the building information modeling software Revit 2023, and energy generation simulations were conducted for Seoul, South Korea, using Insight (a Revit plug-in) during 2024. Simulation results revealed that bridge-type apartment complexes achieved higher levels of renewable energy generation than conventional slab-type apartment complexes. These findings suggest that a three-dimensional urban design incorporating diverse facade orientations, combined with a dynamic adaptive facade system, not only enhances energy efficiency but also offers the potential to create creative and flexible urban landscapes that contrast with conventional, uniform cityscapes. Full article

Structural health monitoring of steel buildings requires accurate detection and localization of bolt loosening, a critical yet challenging task due to complex joint geometries and varying environmental conditions. We propose an intelligent framework that integrates an improved YOLOv9 model with multi-view image fusion to address this problem. The method constructs a comprehensive dataset with multi-angle images under diverse lighting, occlusion, and loosening conditions, annotated with multi-task labels for precise training. The YOLOv9 backbone is enhanced with attention mechanisms to focus on key bolt features, while an angle-aware detection head regresses both bounding boxes and rotation angles, enabling loosening state determination through a threshold-based criterion. Furthermore, the framework unifies camera coordinate systems and employs epipolar geometry to fuse 2D detections from multiple views, reconstructing 3D bolt positions and orientations for precise localization. The proposed method achieves robust performance in detecting loosening angles and spatially localizing bolts, offering a practical solution for real-world structural inspections. Its significance lies in the integration of advanced deep learning with multi-view geometry, providing a scalable and automated approach to enhance safety and maintenance efficiency in steel structures. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article

Multi-laser powder bed fusion is an emerging additive manufacturing technology that enables the production of high-performance components with intricate geometries and large aspect ratios. These tall, slender structures are highly susceptible to steep thermal gradients and residual stress, leading to deformation that compromises dimensional accuracy and structural integrity. This study investigates how geometric compensation, support structure design, and part scaling influence thermal deformation in Inconel 718 components fabricated via multi-laser powder bed fusion. Using pre-compensation, iterative support refinements, and scaled experimental builds, the deformation response across multiple geometries and print strategies is evaluated. Both compensated and original designs are printed on a commercial system equipped with three simultaneously operating lasers. Results show that printing high-angle surfaces without support structures is infeasible, as thermally induced warping and delamination lead to catastrophic failures. Conical support structures spanning critical regions reduce deformation by more than 50% compared to unsupported builds. Reduced-scale parts, however, do not reliably replicate full-scale deformation behavior due to altered boundary conditions and thermal pathways. These findings highlight the need for integrated design-for-AM workflows where compensation, support design, and scale effects are addressed jointly. The study demonstrates that deformation mechanisms do not scale linearly, emphasizing the limitations of small-scale proxies and the necessity of full-scale validation when developing reliable, deformation-aware design strategies for multi-laser powder bed fusion. Full article