Search Results (11,823)

This paper presents a low-power high-precision Discrete-Time Delta–Sigma (DT-DS) analog-to-digital converter (ADC) for a Battery Management System (BMS), which is critical for monitoring key battery parameters such as voltage, current, and temperature. This design employs a second-order Cascade of Integrators FeedForward (CIFF) architecture using a hybrid chopping technique to effectively suppress 1/f noise and offset. Fabricated in a 180 nm Bipolar-CMOS-DMOS (BCD) process, the ADC achieves a peak signal-to-noise ratio (SNR) of 91.2 dB and a peak signal-to-noise-and-distortion ratio (SNDR) of 90.6 dB within a 600 Hz bandwidth, while consuming only 35 µA from a 1.8 V supply. This corresponds to a figure-of-merit (FoM) of 160.4 dB, calculated based on the SNDR, bandwidth, and power dissipation. Full article

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

In the current context of global warming, quantifying carbon fluxes between biosphere and atmosphere and identifying ecosystems as carbon sources or sinks is essential. The goal of this study is to quantify net carbon fluxes for the main forest types in peninsular Spain and characterize them as carbon sources or sinks. A hybrid methodology is proposed. First, net primary production (NPP) is obtained through machine learning using site properties, time metrics of meteorological series, and forest inventory data as inputs. The most accurate NPP estimates (R² ≥ 0.8 and relative RMSE ≤ 30%) were obtained by kernel ridge regression and gaussian process regression using latitude, elevation, time metrics of air temperature, precipitation and incoming solar radiation, and growing stock volume as inputs. Secondly, net ecosystem production (NEP) is obtained by subtracting heterotrophic respiration simulated by Biome-BGC from the previous NPP. All considered forest types presented small and mostly positive NPP and NEP values (greater for deciduous than for evergreen forests), thus generally acting as carbon sinks during the 2004–2018 period. Full article

Mixing processes in stirred tanks are widely applied across various industries, but still offer significant potential for optimization. A promising strategy is the use of double-stage impeller setups instead of conventional single impellers. While multi-impeller configurations are common in tall vessels, their benefits for standard tanks with a height-to-diameter ratio of 1 are largely unexplored. This study systematically investigates the flow fields of single, identical, and mixed double-stage configurations of a Rushton turbine, a pitched-blade turbine, and a retreat curve impeller. To ensure balanced power input in mixed configurations, a refined method for harmonizing specific power via impeller diameter adjustment is proposed. Stereo particle image velocimetry is applied to visualize flow fields, supported by refractive-index matching to enable measurements in a dished-bottom tank. The results reveal substantial flow deficiencies in single-impeller setups. In contrast, double-impeller setups generate novel and significantly improved velocity fields that offer clear advantages and demonstrate strong potential to enhance process efficiency across various mixing applications. These findings provide new experimental insights into the characteristics of dual impellers and form a valuable basis for the design and scale-up of stirred tanks, contributing to more efficient, reliable, and sustainable mixing processes. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Geopolymer mortars were produced from construction and demolition waste using a binary binder of recycled brick powder/recycled concrete powder (RBP/RCP = 70/30 wt%), activated with a hybrid alkaline solution (NaOH/Na₂SiO₃/KOH) and reinforced with sisal fibres at 0–2 wt%. Mechanical performance (compression and three-point bending) and microstructure–phase evolution (XRD, FTIR, SEM-EDS) were assessed after low-temperature curing. Sisal addition delivered a strength–toughness trade-off with a reproducible optimum at ~1.0–1.5 wt%; at 2.0 wt%, fibre clustering and connected porosity reduced the effective load-bearing section, penalising flexure more than compression. Microstructural evidence indicates coexistence and co-crosslinking of N-A-S-H and C-(A)-S-H gels—enabled by Ca from RCP—leading to matrix densification and improved fibre–matrix anchorage. Fractographic features (tortuous crack paths, bridging, and extensive pull-out at ~1.5 wt%) are consistent with an extended post-peak response and higher fracture work without compromising early-age strength. This study achieves the following: (i) it identifies a practical reinforcement window for sisal in RBP/RCP geopolymers, (ii) it links gel chemistry and interfacial phenomena to macroscopic behaviour, and (iii) it distils processing guidelines (gradual addition, workability control, gentle deaeration, and constant A/S) that support reproducibility. These outcomes provide a replicable, low-embodied-CO₂ route to fibre-reinforced geopolymer mortars derived from CDW for non-structural and semi-structural applications where flexural performance and post-peak behaviour are critical. Full article

The V/G criterion is a critical indicator for monitoring dynamic changes during Czochralski silicon single crystal (Cz-SSC) growth. However, the inability to measure it in real time forces reliance on offline feedback for process regulation, leading to imprecise control and compromised crystal quality. To overcome this limitation, this paper proposes a novel soft sensor modeling framework that integrates both mechanism-based knowledge and data-driven learning for the real-time prediction of the crystal quality parameter, specifically the V/G value (the ratio of growth rate to axial temperature gradient). The proposed approach constructs a hybrid prediction model by combining a data-driven sub-model with a physics-informed mechanism sub-model. The data-driven component is developed using an attention-based dynamic stacked enhanced autoencoder (AD-SEAE) network, where the SEAE structure introduces layer-wise reconstruction operations to mitigate information loss during hierarchical feature extraction. Furthermore, an attention mechanism is incorporated to dynamically weigh historical and current samples, thereby enhancing the temporal representation of process dynamics. In addition, a robust ensemble approach is achieved by fusing the outputs of two subsidiary models using an adaptive weighting strategy based on prediction accuracy, thereby enabling more reliable V/G predictions under varying operational conditions. Experimental validation using actual industrial Cz-SSC production data demonstrates that the proposed method achieves high-prediction accuracy and effectively supports real-time process optimization and quality monitoring. Full article

Magnolia crassipes is a valuable species in Magnolia sect. Michelia known for its unique purple flowers, but interspecific reproductive barriers limit its use in breeding. Using M. crassipes as the maternal parent, we performed 13 pollination combinations (one selfed control and crosses with 12 taxa spanning five sections). We assessed reproductive processes from pollen–stigma interaction to seed and seedling performance, and verified hybrids using SSR markers. Reproductive barriers are strongly associated with phylogenetic distance, shifting from pollen-adhesion failure in crosses with donors from distant-section, to abnormal pollen-tube guidance in cross with M. denudata, and to fruit initiation in crosses with pollen donors from sect. Michelia. Among these Michelia-donor crosses, prezygotic barrier strength varied among combinations, as reflected by differences in stigma germination and ovule entry rates, which strongly influenced the potential for fruit set success. Postzygotic barriers further reduced reproductive success via seed abortion (peaking at 83.8%). However, all germinated hybrids exhibited normal early growth. Notably, backcrossing with the F1 hybrid M. ‘Danxia’ significantly improved reproductive compatibility (seed abortion rate 6.3% and germination rate 100%). This study clarifies the key barriers in M. crassipes hybridization and provides a basis and practical strategies for its genetic utilization. Full article

Studies on two-dimensional materials (such as topological insulators or transition metal dichalcogenides) have shown that they exhibit unique properties, including high charge carrier mobility and tunable bandgaps, making them attractive for next-generation electronics. Some of these materials (e.g., HfSe${}_2$) also offer thickness-dependent bandgap engineering. However, the standard device fabrication techniques often introduce processing contamination, which reduces device efficiency. In this paper, we present a modified mechanical exfoliation technique, the Reversed Structuring Procedure, which enables the fabrication of hybrid systems based on 2D microflakes with improved interface cleanness and contact quality. Hall effect measurements on Bi${}_2$Se${}_3$ and HfSe${}_2$ devices confirm enhanced electrical performance, including the decrease in the measured total resistance. We also introduce a novel Star-Shaped Electrode Structure, which allows for accurate Hall measurements and the exploration of geometric magnetoresistance effects within the same device. This dual-purpose geometry enhances the flexibility and demonstrates broader functionality of the proposed fabrication method. The presented results validate the Reversed Structuring Procedure method as a robust and versatile approach for laboratory test-platforms for electronic applications of new types of layered materials whose fabrication technology is not yet compatible with CMOS. Full article

Stroke continues to impose an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet, is an emerging resource in neurorehabilitation for stroke survivors. Technologies such as asynchronous transmission to handle intermittent connectivity, edge computing to conserve bandwidth and lengthen device life, functional interoperability across platforms, security mechanisms scalable to resource constraints, and hybrid architectures that combine local processing with cloud synchronization help bridge the digital divide and infrastructure limitations in low-resource environments. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain–computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients. Full article

In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn–Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios. Full article

Mamba, based on the state space model (SSM), offers an efficient alternative to the quadratic complexity of attention, showing promise for long-sequence data processing and global modeling in 3D object detection. However, applying it to this domain presents specific challenges: traditional serialization methods can compromise the spatial structure of 3D data, and the standard single-layer SSM design may limit cross-layer feature extraction. To address these issues, this paper proposes MSHI-Mamba, a Mamba-based multi-stage hierarchical interaction architecture for 3D backbone networks. We introduce a cross-layer complementary cross-attention module (C3AM) to mitigate feature redundancy in cross-layer encoding, as well as a bi-shift scanning strategy (BSS) that uses hybrid space-filling curves with shift scanning to better preserve spatial continuity and expand the receptive field during serialization. We also develop a voxel densifying downsampling module (VD-DS) to enhance local spatial information and foreground feature density. Experimental results obtained on the KITTI and nuScenes datasets demonstrate that our approach achieves competitive performance, with a 4.2% improvement in the mAP on KITTI, validating the effectiveness of the proposed components. Full article

Today, machine learning (ML) is generally considered a potent and efficient tool for addressing studies in various diverse domains, including image processing and event prediction on a timescale. ML represents complex relations between features, and these mappings between such features may be applied in simulations of time-dependent events, such as the behavior of animals. Still, ML inherently strongly depends on extensive and consistent datasets, a fact that reveals both the benefits and drawbacks of ML. In the use of ML, insufficient or skewed data can limit the ability of algorithms to accurately predict or generalize possible states. To overcome this limitation, this work proposes an integrated hybrid approach that combines machine learning with methods from cognitive science, here especially inspired by the ACT-R model to approach cases of missing or unbalanced data. By incorporating cognitive processes such as memory, perception, and attention, the model accounts for the internal mechanisms of decision-making and environmental interaction where traditional ML methods fall short. This approach is particularly useful in representing states that are not directly observable or are underrepresented in the data, such as rare behavioral responses for animals, or adaptive strategies. Experimental results show that the combination of machine learning for data-driven analysis and cognitive ‘rule-based’ frameworks for filling in gaps provides a more comprehensive model of animal behavior. The findings suggest that this hybrid approach to simulation models can offer a more robust and consistent way to study complex, real-world phenomena, especially when data is inherently incomplete or unbalanced. Full article

With rapid urbanization, issues such as blind planning, disorder, and inefficiency in urban construction and land use have become increasingly prominent. To address these challenges, this study proposes a comprehensive suitability evaluation framework for urban construction land, using Zhengzhou City as a case study. The evaluation system incorporates five dimensions: topography, transportation, location, current land use status, and soil clay content. A hybrid weighting method, combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM), was employed to determine indicator weights. The research indicates that the suitability of the construction land can be classified into four categories: highly suitable, moderately suitable, critically suitable, and unsuitable. Among them, the highly suitable area accounted for 6.907% (502.71 km²), the moderately suitable area accounted for 81.668% (5943.54 km²), the critically suitable area accounted for 11.422% (830.98 km²), and the unsuitable area only accounted for 0.003% (0.18 km²). The results show that most areas in Zhengzhou City are highly suitable or moderately suitable for construction land, while Gongyi and Dengfeng, due to their complex terrain and long distances from the city center, are mostly in the critically suitable or unsuitable construction land. This evaluation result is in good agreement with the actual situation and can offer valuable insights for sustainable urban development. Full article

Elastomeric ablative coatings are essential for protecting solid rocket motor (SRM) combustion chambers from extreme thermal and erosive environments, and their performance is governed by both material composition and processing strategy. This review examines the main elastomer systems used for SRM insulation, including ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), hydroxyl-terminated polybutadiene (HTPB), polyurethane (PU), silicone-based compounds, and related hybrids, and discusses how their rheological behavior, cure kinetics, thermal stability, and ablation mechanisms affect manufacturability and in-service performance. A comprehensive assessment of coating technologies is presented, covering casting, molding, centrifugal forming, spraying, automated deposition, and emerging additive-manufacturing approaches for complex geometries. Emphasis is placed on processing parameters that control adhesion to metallic substrates, layer uniformity, defect formation, and thermomechanical integrity under high-heat-flux exposure. The review integrates current knowledge on how material choice, surface preparation, and application sequence collectively determine insulation efficiency under operational SRM conditions. Practical aspects such as scalability, compatibility with complex chamber architectures, and integration with quality-control tools are highlighted. By comparing the capabilities and limitations of different materials and technologies, the study identifies key development trends and outlines remaining challenges for improving the durability, structural robustness, and ablation resistance of next-generation elastomeric coatings for SRMs. Full article