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Several experimental and numerical studies have been conducted on the towing behavior of floating offshore wind turbines (FOWTs); however, these studies mainly focus on tension-leg platform (TLP) and semi-submersible designs with cylindrical features. The University of Maine’s VolturnUS+ concept is a cruciform-shaped barge-type FOWT with distinctive hydrodynamic properties that have not been characterized in previous research. This study presents basin-scale experiments that characterize the hydrodynamic drag properties of the VolturnUS+ platform, as well as observing the motion behavior of the platform and added resistance during towing in calm water and waves. The towing experiments are conducted in two towing configurations, with differing platform orientations and towline designs. The basin experiments are supplemented with a numerical study using computational fluid dynamic (CFD) simulations to explore flow-induced motion (FIM) on the platform during towing. In both the experiments and the CFD simulations, it was determined that the towing configuration significantly impacted the drag and motion characteristics of the platform, with the cruciform shape producing FIM phenomena. Observations from the towing tests confirmed the feasibility of towing the VolturnUS+ platform in the two orientations. The results and observations developed from the experimental and numerical towing studies will be used to inform numerical models for planning towing operations, as well as develop informed recommendations for towing similar cruciform-shaped structures in the future. Full article

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

Background: Early and accurate differentiation between melanomas and benign nevi is essential for making proper clinical decisions. This study aimed to identify clinical, morphological, and histopathological variables most strongly associated with melanoma, using both statistical and machine learning approaches. Methods: This study evaluated 184 melanocytic lesions using clinical, morphological, and histopathological parameters. Univariable analyses were performed in XLStat statistical software, version 2014.5.03, while multivariable machine learning models were developed in Jamovi (version 2.4). Five supervised algorithms (random forest, partial least squares, elastic net regression, conditional inference trees, and k-nearest neighbors) were compared using repeated cross-validation, with performance evaluated by accuracy, Kappa, sensitivity, specificity, F1 score, and calibration. Results: Univariable analysis identified significant differences between melanomas and nevi in age, horizontal diameter, gender, lesion location, and selected histopathological features (cytological and extracellular matrix changes, epidermal interactions). However, several associations weakened in multivariable analysis due to collinearity and overlapping effects. Using glmnet, the most influential independent predictors were cytological changes, horizontal diameter, epidermal interactions, and extracellular matrix features, alongside age, gender, and lesion location. The model achieved high discrimination (AUC = 0.97, 95% CI: 0.93–0.99) and accuracy (training: 95.3%; test: 92.6%), confirming robustness. Conclusions: Structured demographic, morphological, and histopathological data—particularly age, lesion size, cytological and extracellular matrix changes, and epidermal interactions—can effectively support classification of melanocytic lesions. Machine learning approaches (the glmnet model in our study) provide a reliable framework to evaluate such predictors and offer practical diagnostic support in dermatopathology. Full article

The selection of suitable frothers in flotation processes plays a crucial role in controlling bubble formation, foam stability, and ultimately mineral recovery. Therefore, understanding the interfacial behavior of frothers is important to optimize flotation efficiency, especially for valuable sulfide minerals such as galena (PbS). In this study, the interfacial behavior and flotation performance of different frothers in PbS flotation were investigated with a particular focus on surface tension, bubble coalescence, foam stability, and flotation recovery. A high-purity crystalline PbS sample (≈96.65% PbS) obtained from Trabzon, Türkiye, was subjected to systematic experimental analyses including surface tension measurements, critical coalescence concentration (CCC) determination, dynamic foam stability (DFS) tests using the DFA100 analyzer, and micro-flotation experiments. 2-ethylhexanol (EH), polypropylene glycol 250 (PPG250), and methyl isobutyl carbinol (MIBC) were used as frothers, while potassium ethyl xanthate (PEX) was employed as a collector. The results revealed that EH had the highest surface activity (42.67 mN/m at 1000 ppm), and the lowest CCC value (~2 ppm) compared to PPG250 (~3 ppm) and MIBC (~8 ppm). According to the micro-flotation results, the flotation recovery gradually increased with increasing frother dosage; the highest recoveries were obtained with PPG250 (99.45%), EH (98.31%), and MIBC (95.17%). PPG250 and EH achieved higher flotation performance at lower dosages compared to MIBC. These findings highlight the critical role of molecular structure and interfacial properties in the effective selection of frothers for galena flotation. Full article

The Zebra Optimization Algorithm (ZOA) is a metaheuristic algorithm inspired by the collective behavior of zebras in the wild. Like many other swarm intelligence algorithms, the ZOA faces several limitations, including slow convergence, susceptibility to local optima, and an imbalance between exploration and exploitation. To address these challenges, this paper proposes an improved version of the ZOA, termed the Multi-strategy Improved Zebra Optimization Algorithm (MIZOA). First, a multi-population search strategy is introduced to replace the traditional single population structure, dividing the population into multiple subpopulations to enhance diversity and improve global convergence. Second, the mutation operation of genetic algorithm (GA) is integrated with the Metropolis criterion to boost exploration capability in the early stages while maintaining strong exploitation performance in the later stages. Third, a novel selective aggregation strategy is proposed, incorporating the hunting behavior of the Coati Optimization Algorithm (COA) and Lévy flight to further enhance global exploration and convergence accuracy during the defense phase. Experimental evaluations are conducted on 23 benchmark functions, comparing the MIZOA with eight existing swarm intelligence algorithms. The performance is assessed using non-parametric statistical tests, including the Wilcoxon rank-sum test and the Friedman test. The results demonstrate that the MIZOA achieves superior global convergence accuracy and optimization performance, confirming its robustness and effectiveness. The MIZOA was evaluated on real-world engineering problems against seven algorithms to validate its practical performance. Furthermore, when applied to path planning tasks for Automated Guided Vehicles (AGVs), the MIZOA consistently identifies paths closer to the global optimum in both simple and complex environments, thereby further validating the effectiveness of the proposed improvements. Full article

Forests are critical ecosystems that play a fundamental role in supporting biodiversity and maintaining climate stability. However, forest monitoring and exploration present huge challenges due to the vast scale and complex terrain. This paper proposes a novel bionic robot, PteroBot, designed to support a new paradigm for forest exploration inspired by the locomotion of Pteromyini. PteroBot is capable of regulating its gliding posture via a flexible membrane, enabling low-energy and low-disturbance mobility within forest environments. An adaptive gliding control system tailored to the robot’s structure is developed and its effectiveness is validated through aerodynamic analysis, simulation, and experimental testing. Results show that under a cascaded closed-loop attitude controller, PteroBot achieves an average glide ratio of 2.02 and demonstrates controllable turning via attitude modulation. Additionally, comparative tests with UAVs demonstrate that PteroBot offers significant advantages in energy efficiency and acoustic disturbance. Experimental outcomes confirm that PteroBot offers a biologically inspired and ecologically compatible solution for forest exploration, with strong potential in applications such as environmental monitoring, habitat assessment, and covert reconnaissance. Full article

Timber structures and structural members have undergone rapid development in recent decades and are now fully competitive with traditional structures made of reinforced concrete or structural steel in many areas. Low self-weight, high durability, rapid construction assembly, and a favourable environmental footprint predispose timber structures for wider future use. A persisting drawback is the often-complicated joining of individual elements, especially when moment resistance is required. For CLT panels, this issue is more urgent due to their relatively small thickness and cross-laminated lay-up. This paper presents experimental research investigating parameters related to the actual behaviour of a moment-resisting embedded joint of CLT panels. The test programme consisted of four series (12 specimens) loaded in four-point bending to failure. The proposed and tested joint consists of high-strength steel rods glued into the two connected parts of the CLT panel. In addition to a detailed investigation of the resistance and stiffness of the joint, this research evaluates the effect of composite action with a reinforced-concrete slab on the performance of this type of joint. The experimental results and their detailed analysis are also extended to propose a framework concept for creating a theoretical (mechanical) model based on the component method. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

In this study, a novel compliant ankle structure with three passive degrees of freedom for quadruped robots is presented. First, this paper introduced the bionic principle and structural implementation method of the passively compliant ankle, with a particular focus on the configuration and working principle of the elastic adjustment element. Then, the kinematic model of the ankle and mathematic model of the elastic element, comprising mechanical and pneumatic model, was established by using appropriate theory. Finally, a test rig of the ankle was carried out to verify its actual function. The research results show that: (1) The ankle structure demonstrates excellent stability, maintaining its upright posture even under unreliable foot–ground interactions. (2) Compared to traditional structure, the single-leg module incorporating the proposed design exhibits smoother forward stepping under an appropriate pre-inflation pressure, with its actual motion trajectory showing closer agreement with the planned one; (3) The parallel topology enables a notable reduction in the driving torque of each joint in the leg during motion, thereby improving the energy efficiency of robots. Full article

Background: Despite the high prevalence of anterior cruciate ligament (ACL) surgeries, standardized, evidence-based rehabilitation protocols remain lacking. Digital medical devices (DMDs) like the “Orthelligent” system have gained relevance as adjuncts to traditional physiotherapy, offering continuous, objective monitoring of functional recovery. Methods: A retrospective cohort analysis included 335 patients who underwent ACL reconstruction and used the “Orthelligent home” system between August 2022 and December 2024. In total, 5675 recorded test and exercise events were analyzed. Functional recovery was assessed using the Limb Symmetry Index (LSI) across five defined rehabilitation phases (0–4). All patients followed a structured rehabilitation program aligned with current clinical practice guidelines, supplemented by Orthelligent as a home-based digital tool for daily monitoring. Results: Significant functional improvement was observed during early rehabilitation phases, with the LSI increasing from 0.64 ± 0.02 in phase 0 to 0.81 ± 0.01 in phase 2 (p < 0.001). Time since surgery was a significant positive predictor (p = 0.034), while pain showed a strong negative impact on performance (p < 0.001). Anthropometric factors had no significant effect. Exercises associated with high rates of drop-out, pain, or difficulty were identified and linked to specific rehab phases. Conclusions: This study demonstrates that digital rehabilitation monitoring can reliably reflect patient progress after ACL reconstruction. The early postoperative period (first 3 months) is critical for functional gains, highlighting the need for individualized, pain-sensitive rehabilitation strategies. Full article

Accurate prediction of soil–structure interface shear strength (τ_max) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (R_t) are dominant factors. Irregular grains and rougher interfaces mobilised higher τ_max through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R² = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τ_max. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis. Full article

The harvesting of dragon fruit remains challenging due to uneven clamping forces, high fruit damage rates, and low redundancy in conventional end-effectors. To address these issues, we developed a novel embracing end-effector with a Variable-Span Arch (VSA) structure. The VSA design enables adaptive clamping force distribution and effective torsional fruit separation, significantly reducing static pressure damage. Theoretical modeling, mechanical testing, and field experiments were conducted to evaluate its performance. Results show that the proposed end-effector achieves a 95% harvesting success rate, with an average picking time of 15 s per fruit, and can output a maximum torque of 18 kgf·cm, which is sufficient for dragon fruit detachment. These findings demonstrate that the VSA-based embracing end-effector offers a low-damage, efficient, and robust solution for dragon fruit harvesting, providing practical guidance for robotic applications in tropical fruit production. Full article

Evolutionary Algorithms (EAs) are those based on the phenomenon of survival of the fittest. Differential Evolution (DE) is a member of this family, and it is well-suited for handling problems with real-valued variables. However, to use DE to solve binary problems, it is necessary to employ some adaptation. The primary objective of the present study is to develop a new binary version of DE. The proposed algorithm is called Binary Differential Evolution with a limited maximum number of dimension changes (NBDE), and it was tested with the OneMax Problem, five variants of the Knapsack Problem (KP), and Feature Selection (FS). The results showed that NBDE is competitive and superior to the other tested algorithms in many instances. For the 0/1 KP and 0/1 Multidimensional KP, NBDE outperforms all the other algorithms for all instances. For the FS problem, the proposed algorithm demonstrates good accuracy as its primary quality. The proposed algorithm exhibits a satisfactory performance, particularly in high-dimensional problems, which appears to be a quality inherited from the method that inspired its creation. This is particularly interesting because it provides empirical evidence that the importation of operators can perpetuate a pattern of behavior in algorithms with different structures. Full article

Fostering mathematical thinking, communication, and creativity has become a central goal in mathematics education as these competencies are strongly linked to flexible problem solving and innovative engagement. Prior research has shown that students’ beliefs and dispositions play a crucial role in shaping their learning, strategy use, and persistence, yet limited evidence exists on how these constructs interrelate among pre-service elementary mathematics teachers. Addressing this gap, the present study examines the relationships among mathematical thinking perceptions, mathematical communication dispositions, and creative thinking dispositions. A correlational survey design was employed to test a hypothetical model developed within the framework of structural equation modeling (SEM). Data were collected from 615 pre-service teachers. Analyses involved descriptive statistics, correlations, and predictive algorithms via IBM SPSS Statistics 24, along with standardized regression coefficients and fit indices using AMOS. The results revealed that while perceptions of problem-solving and higher-order thinking predicted creative thinking dispositions both directly and indirectly, perceptions of reasoning did so only indirectly through mathematical communication. Mathematical communication dispositions had the strongest direct effect on creative thinking dispositions, underscoring their mediating role. These findings highlight the importance of fostering communication alongside creativity in teacher education, thereby equipping future teachers to promote creative thinking through cognitive, social, and representational processes. Full article

This paper investigates the application of image segmentation techniques in endodontics, focusing on improving diagnostic accuracy and achieving faster segmentation by delineating specific dental regions such as teeth and root canals. Deep learning architectures, notably 3D U-Net and GANs, have advanced the image segmentation process for dental structures, supporting more precise dental procedures. However, challenges like the demand for extensive labeled datasets and ensuring model generalizability remain. Two semi-automatic segmentation workflows, Grow From Seeds (GFS) and Watershed (WS), were developed to provide quicker acquisition of ground truth training data for deep learning models using 3D Slicer software version 5.8.1. These workflows were evaluated against a manual segmentation benchmark and a recent dental segmentation automated tool on three separate datasets. The evaluations were performed by the overall shapes of a maxillary central incisor and a maxillary second molar and by the region of the root canal of both teeth. Results from Kruskal–Wallis and Nemenyi tests indicated that the semi-automated workflows, more often than not, were not statistically different from the manual benchmark based on dice coefficient similarity, while the automated method consistently provided significantly different 3D models from their manual counterparts. The study also explores the benefits of labor reduction and time savings achieved by the semi-automated methods. Full article