Search Results (6,215)

This investigation evaluates the viscosity and flow performance of cationic polyacrylamide (CPAA) solutions by assessing the effect of CPAA concentrations, shear rate, temperature, and electrolyte salt types. The study aims to characterize the flow behavior of CPAA solutions for different industrial utilizations under some challenging conditions of high salinity of two different electrolytes and high-temperature environments. In addition, the study addresses the critical shear rate thresholds at which the transition from shear-thinning to shear-thickening occurs. An Anton Paar rotational rheometer was employed to evaluate the flow behavior of cationic polyacrylamide solutions over the range of 20–80 °C at 20 °C intervals. Polymer samples were prepared from CPAA powder in a concentration range of 500–5000 ppm. To determine the electrolyte effects, NaCl and CaCl₂ were incorporated into the polymer solutions with a concentration range of 0–10 Wt.%. This study revealed that shear stress is vastly sensitive to CPAA concentration at shear rates less than 200 s⁻¹, whereas this sensitivity reduces at higher shear rates where the resulting profiles converge. Moreover, a considerable decrease in shear stress was reported with temperature as a result of the thermal influence on the molecular interaction forces. Rheological analysis of the CPAA solutions shows they exhibit strong non-Newtonian shear-thinning behaviors with viscosity decreasing significantly as the shear rate approaches 200 s⁻¹. On the contrary, a transition to a shear-thickening profile is observed at a shear rate above this limit of 200 s⁻¹. The results show that the dynamic viscosity of the CPAA solutions rises significantly as the concentration increases from 500 to 5000 ppm. At a shear rate of 10 s⁻¹, the dynamic viscosity increased from 2.4 to 33.8 mPa·s as the CPAA concentration increased from 500 to 5000 ppm (exactly 2.4, 11.8, 16.6, and 33.8 mPa.s for 500, 1500, 2500, and 5000 ppm, respectively). Additionally, increasing the temperature from 20 to 80 °C exerts a strong negative influence on dynamic viscosity. Specifically, for the 5000 ppm concentration at a shear rate of 10 s⁻¹, the dynamic viscosity decreased from 33.8 to 18.3 mPa.s as the temperatures rose from 20 to 80 °C (recorded as 33.8, 27.9, and 18.3 mPa.s at 20, 40, and 80 °C, respectively). Furthermore, the introduction of different electrolytes, such as NaCl and CaCl₂, significantly reduces the viscosity flow profiles. Full article

As unmanned aerial vehicles (UAVs) are increasingly utilized in urban inspection and emergency rescue missions, path planning under strong wind conditions persists as a critical challenge. Traditional algorithms frequently exhibit deficiencies in environmental adaptability or encounter difficulties in balancing exploration and exploitation. This paper presents a dynamic-proportion Bat–Cuckoo Search (BA-CS) Hybrid Algorithm enhanced with wind field perception to tackle the challenges of UAV path planning in urban environments with strong winds, specifically addressing the issues of insufficient environmental adaptation and the exploration–exploitation imbalance. The algorithm integrates a dual-feedback mechanism that dynamically modifies the ratio of the BA/CS subpopulations in accordance with real-time iteration progress and population diversity. By incorporating wind field perception into population initialization, interpopulation information exchange, and wind resistance perturbation strategies, it attains efficient path optimization under multiple constraints. Experimental results under strong winds with speeds ranging from 10.8 to 13.8 m/s indicate that the proposed algorithm generates paths that are smooth, continuous, and entirely collision-free. It achieves a superior average wind resistance cost of 0.92, which is 9.8%, 17.1%, and 52.6% lower than those of the A*, RRT, and PSO algorithms, respectively. With a planning time of 3.95 s, it satisfies the path wind resistance stability requirements stipulated in the GB/T 38930-2020 standard, providing an effective solution for UAV inspection and emergency rescue operations in urban wind scenarios. Full article

The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike existing approaches that apply adversarial training or gradient regularization independently, ShieldNet introduces a synergistic dual-layer defense framework featuring three key components: (1) an attack-aware adaptive weighting mechanism that dynamically balances defense priorities across multiple attack types, (2) a smoothness-regularized gradient penalty formulation that maintains differentiable gradients while encouraging locally smooth loss landscapes, and (3) a consistency loss component that enforces prediction stability between clean and adversarial inputs. Through extensive experimental validation across three diverse iris datasets, MMU1, CASIA-Iris-Africa, and UBIRIS.v2, and rigorous evaluation against strong adaptive attacks including AutoAttack, PGD-100 with random restarts, and transfer-based black-box attacks, ShieldNet demonstrated robust performance, achieving 87.3% adversarial accuracy under AutoAttack on MMU1, 85.1% on CASIA-Iris-Africa, and 82.4% on UBIRIS.v2, while maintaining competitive clean data accuracies of 94.7%, 93.9%, and 92.8%, respectively. The proposed framework outperforms existing state-of-the-art defense methods including TRADES, MART, and AWP, achieving an equal error rate (EER) as low as 2.8% and demonstrating consistent robustness across both gradient-based and gradient-free attack scenarios. Comprehensive ablation studies validate the complementary contributions of each defense component, while latent space analysis confirms that ShieldNet learns genuinely robust feature representations rather than relying on gradient obfuscation. These results establish ShieldNet as a practical and reliable solution for deployment in high-security biometric authentication environments. Full article

Peroxymonosulfate (PMS)-based advanced oxidation is often hindered by pH instability and the lack of post-reaction separation. Herein, commercial magnesium hydroxide (Mg(OH)₂) is introduced as a multifunctional catalyst to address these limitations. Mg(OH)₂ effectively catalyzed PMS decomposition via a nonradical pathway dominated by singlet oxygen (¹O₂) generation, achieving rapid and complete degradation of electron-rich pollutants like bisphenol A (BPA) within 40 min. The system exhibits exceptional pH self-regulation, stabilizing the solution at ~9.8 and maintaining high efficiency across an initial pH range of 3–11. Mechanistic studies confirm ¹O₂ as the primary reactive species with a steady-state concentration of 1.67 × 10⁻¹² M. The catalyst demonstrates strong resistance to common anions and humic acid, along with excellent stability over four cycles. Furthermore, Mg(OH)₂ enables in situ flocculation and removal of degradation products. This work highlights Mg(OH)₂ as an efficient, stable, and multifunctional activator, offering a integrated strategy for practical wastewater treatment. Full article

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. Full article

Sponge-based preservative packaging is an emerging approach to mitigate mechanical damage to fruits and vegetables during transportation, storage, and retail. However, conventional polyurethane sponges generally lack durable antibacterial activity and are neither biodegradable nor readily recyclable. Herein, to address these limitations, silver nanoparticles immobilized on TEMPO-oxidized cellulose nanofibers (TCNF@AgNPs) were incorporated into a quaternized chitin matrix to construct a synergistic antibacterial composite sponge (QCH/TCNF@AgNPs) for fruit preservation. The composite sponge exhibited strong antibacterial efficacy against Escherichia coli and Staphylococcus aureus, together with a low cumulative release of silver species of 2.49% after 336 h. In addition, the sponge showed >50% mass loss after 36 days in lysozyme solution, indicating good enzymatic degradability. Cytocompatibility assays further confirmed favorable biocompatibility and biosafety. Notably, the composite sponge provided satisfactory preservation performance for fresh strawberries. Overall, this work demonstrates the potential of QCH/TCNF@AgNPs as a biodegradable antibacterial packaging sponge for fruit preservation. Full article

Oxalic acid serves as an environmentally benign leaching agent, exhibiting strong reducing and complexing capabilities. In the oxalic acid leachate derived from vanadium-bearing shale, aluminum ions are present as major impurities. Achieving efficient and deep separation of vanadium from aluminum remains a key technical challenge. This study investigates the selective separation of vanadium and aluminum from oxalic acid leaching solutions using solvent extraction with Aliquat 336, supported by density functional theory (DFT) calculations. Experimental results demonstrate that, under optimized conditions, Aliquat 336 enables effective separation of vanadium from aluminum. DFT analysis elucidates the molecular-level interaction mechanism, revealing that the binding affinity of Aliquat 336 for [VO(C₂O₄)₂]²⁻ (ΔG = −287.96 kJ/mol) is significantly stronger than for [Al(C₂O₄)₂]⁻ (ΔG = −186.68 kJ/mol). These results provide a solid thermodynamic basis for the observed selectivity and establish a robust theoretical framework for developing high-efficiency separation processes. This work thus clarifies, for the first time, the mechanistic foundation of vanadium–aluminum separation in oxalic acid systems. Full article

In the field of optical inspection for unpatterned wafer surfaces, this paper presents a novel inspection system designed to meet the semiconductor industry’s growing demand for high efficiency and cost-effectiveness. The system is built around the principles of simplicity, stability, speed, and low cost. Its core is a low-speed stepping rotary line-scan architecture. This architecture is integrated with a two-step phase-shifting algorithm. The combination leverages line-scan differential interference contrast (DIC) technology. This aims to transform DIC technology—traditionally used for detailed observation—into an industrialized solution capable of rapid, accurate quantitative measurement. Experimental validation on an equivalent platform confirms strong performance. The system achieves an imaging uniformity exceeding 85% across dual channels. Its Modulation Transfer Function (MTF) value is greater than 0.55 at 71.8 lp/mm. The vertical detection clearly resolves 3 nm standard height steps. Additionally, the throughput exceeds 80 wafers per hour. The proposed line-scan DIC system achieves both high inspection accuracy and industrial-grade scanning speed, delivering robust performance and reliable operation. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Phytoremediation is an eco-friendly and in situ solution for remediating heavy metal-contaminated soils, yet practical application requires timely and accurate effectiveness evaluation. However, conventional chemical analysis of plant parts and soils is labor-intensive, time-consuming and limited for large-scale monitoring. This study proposed a rapid sensing framework integrating laser-induced breakdown spectroscopy (LIBS) with deep transfer learning and spectral indices to assess phytoremediation effectiveness of Sedum alfredii (a Cd/Zn co-hyperaccumulator). LIBS spectra were collected from plant tissues and diverse soil matrices. To overcome strong matrix effects, fine-tuned convolutional neural networks were developed for simultaneous multi-matrix quantification, achieving high-accuracy prediction for Cd and Zn (R²_test > 0.99). These predicted concentrations enabled calculating conventional phytoremediation indicators like bioconcentration factor (BCF), translocation factor (TF), plant effective number (PEN), and removal efficiency (RE), yielding recovery rates near 100% for TF and PEN. Additionally, novel spectral indices (SIs)—directly derived from characteristic wavelength combinations—were constructed to bypass intermediate quantification. SIs significantly improved the direct evaluation of Zn removal and translocation. Finally, a decision-level fusion strategy combining concentration predictions and SIs enhanced Cd removal assessment accuracy. This study validates LIBS combined with intelligent algorithms as a rapid sensor tool for monitoring phytoremediation performance, facilitating sustainable environmental management. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

To address the challenges of scarce labeled samples, strong instantaneity, and variable morphology in lightning optical classification—issues that traditional methods struggle to handle efficiently and often require extensive manual intervention—we propose a frame difference triplet network (FD-TripletNet) tailored for few-shot lightning recognition. The lightning optical dataset used in this study was collected from two observation stations over six months, comprising 459 video samples that include lightning events with diverse morphologies (e.g., branched, spherical) and non-lightning events prone to misclassification (e.g., strong light interference, moving objects). Considering the critical feature of lightning—abrupt single-frame changes—we introduce adjacent frame difference matrices as model input to explicitly capture transient brightness variations, reducing noise from static backgrounds. To enhance discriminative ability in few-shot scenarios, the model leverages Triplet Loss to compact intra-class features and separate inter-class features, combined with a dynamic sample matching strategy to focus on challenging cases. The experimental results show that FD-TripletNet achieves a classification accuracy of 94.8% on the dataset, outperforming traditional methods and baseline deep learning models. It effectively reduces the False Negative Rate (FNR) to 3.2% and False Positive Rate (FPR) to 7.4%, successfully distinguishing between lightning and non-lightning events, thus providing an efficient solution for real-time lightning monitoring in meteorological applications. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

As a natural refrigerant, CO₂ shows significant potential in sustainable thermal engineering due to its environmental safety and economic viability. While the transcritical CO₂ cycle demonstrates strong performance in heating, low-temperature applications, and integration with renewable energy sources, its widespread adoption is hindered by key challenges at the application level. These include: high sensitivity of system efficiency to operating conditions, which creates an “efficiency hump” and narrows the optimal operating window; increased component costs and technical challenges for key devices such as multi-channel valves due to high-pressure requirements; and complex system control with limited intelligent solutions currently integrated. Despite these challenges, the transcritical CO₂ cycle holds unique value in enabling synergistic energy conversion. Its ability to efficiently match and cascade different energy grades makes it particularly suitable for data center cooling, industrial combined cooling and heating, and solar–thermal hybrid systems, positioning it as an indispensable technology in future low-carbon energy systems. To fully realize its potential, development efforts must focus on high-value applications and key technological breakthroughs. Priority should be given to demonstrating its use in fields where it holds a distinct advantage, such as low-temperature refrigeration and high-temperature industrial heat pumps, to establish commercially viable models. Concurrently, core technologies—including adaptive intelligent control algorithms, high-efficiency expanders, and cost-effective pressure-resistant components—must be advanced. Supportive policies, encompassing energy efficiency standards, safety regulations, and fiscal incentives, will be essential to facilitate the transition from demonstration projects to widespread industrial adoption. Full article