Search Results (321)

In remote sensing and low-altitude unmanned aerial vehicle(UAV) detection scenarios, small target detection is extremely challenging due to the low pixel proportion, sparse features, and complex backgrounds of targets. The reliability of low-altitude security, in particular, is directly dependent on the accuracy of small target detection. However, current methods still face three major limitations: insufficient detection accuracy for targets smaller than 20 pixels; artifacts and false textures introduced by Generative Adversarial Network-based enhancement, which lead to increased false detection rates; and the reliance of existing approaches on specialized architectures, resulting in weak generalization capability and difficulty in adapting to multi-scenario deployment requirements. To address these issues, this paper proposes a plug-and-play dual-mechanism collaborative enhancement framework named HD-BSNet. Firstly, a High-Frequency Differential Perception mechanism is designed to enhance the detailed feature representation of small targets. Secondly, a Background Semantic Modeling mechanism is introduced to learn key features that distinguish targets from the background. Additionally, a Parallel Multi-Scale Focus Module is constructed to further reinforce target features. Extensive experiments on three small target datasets demonstrate that the proposed method effectively improves the accuracy and generalization ability of small target detection. Full article

Nine technology-related metal(loid)s (Ag, Co, Fe, Mo, Pt, Rh, Sb, Se and Y) were monitored in 137 topsoil samples from urban parks, industrial areas and gardens in Alcalá de Henares (Spain) using ICP–MS. Selenium was not detected, while Mo, Sb and Rh showed a high proportion of values below the detection limit, indicating generally low contamination. In contrast, Fe, Co and Y were detected in all samples, with industrial soils showing about two-fold higher median Co and Fe than urban soils. Garden soils displayed marked silver enrichment (median 0.439 vs. 0.068 mg kg⁻¹ in urban soils), with Ag pollution indices up to 71 and enrichment factors up to 69; around 17% of garden samples exceeded EF > 40, and more than one-quarter had EF > 10. Principal component analysis suggested a predominantly geogenic association for Co, Fe and Y and an anthropogenic component for Ag, Mo, Rh and Sb, while Pt was mainly linked to vehicular emissions. Under standard US EPA exposure scenarios applied to the 2001 topsoil concentrations, oral and inhalation hazard quotients for elements with available benchmarks remained <0.2 and inhalation cancer risks for Co were ≤2.5 × 10⁻⁷, indicating low estimated risk within the model assumptions. However, quantitative risk characterisation remains constrained by benchmark gaps for Pt and Rh and by limited consensus toxicity values for Y, which introduces uncertainty for these technology-related elements. These results should therefore be interpreted primarily as a baseline (2001) in surface soils for Alcalá de Henares rather than as a direct representation of current exposure conditions. Full article

Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analog electronics or high-power stimulation hardware. This study investigates a proof-of-concept digital custom-designed chip that generates a stable 6 Hz oscillation capable of imposing a stable rhythmic pattern onto digitized seizure-like EEG dynamics. Using a publicly available EEG seizure dataset, we extracted and averaged analog seizure waveforms, digitized them to emulate neural front-ends, and directly interfaced the digitized signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip’s pulse train was resampled and low-pass-reconstructed to produce an analog 6 Hz waveform, allowing direct comparison between seizure morphology, its digitized representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable neuromodulation device for future healthcare diagnostics. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

Gesture recognition is important for rehabilitation assistance and intelligent prosthetic control. However, surface electromyography (sEMG) signals exhibit strong non-stationarity, and conventional deep-learning models require long training time and high computational cost, limiting their use on resource-constrained devices. This study proposes a Frozen Multi-Convolution Dual-Branch Network (FMC-DBNet) to address these challenges. The model employs randomly initialized and fixed convolutional kernels for training-free multi-scale feature extraction, substantially reducing computational overhead. A dual-branch architecture is adopted to capture complementary temporal and physiological patterns from raw sEMG signals and intrinsic mode functions (IMFs) obtained through variational mode decomposition (VMD). In addition, positive-proportion (PPV) and global-average-pooling (GAP) statistics enhance lightweight multi-resolution representation. Experiments on the Ninapro DB1 dataset show that FMC-DBNet achieves an average accuracy of 96.4% ± 1.9% across 27 subjects and reduces training time by approximately 90% compared with a conventional trainable CNN baseline. These results demonstrate that frozen random-convolution structures provide an efficient and robust alternative to fully trained deep networks, offering a promising solution for low-power and computationally efficient sEMG gesture recognition. Full article

Urban surface subsidence is primarily induced by intensive above-ground and underground construction activities and excessive groundwater extraction. Integrating InSAR techniques for safety monitoring of urban subway infrastructure is therefore of great significance for urban safety and sustainable development. However, single-track high-spatial-resolution SAR imagery is insufficient to achieve full coverage over large urban areas, and direct mosaicking of inter-track InSAR results may introduce systematic biases, thereby compromising the continuity and consistency of deformation fields at the regional scale. To address this issue, this study proposes an inter-track InSAR correction and mosaicking approach based on the mean vertical deformation difference within overlapping areas, aiming to mitigate the overall offset between deformation results derived from different tracks and to construct a spatially continuous urban surface deformation field. Based on the fused deformation results, subsidence characteristics along subway lines and in key urban infrastructures were further analyzed. The main urban area and the eastern and western new districts of Zhengzhou, a national central city in China, were selected as the study area. A total of 16 Radarsat-2 SAR scenes acquired from two tracks during 2022–2024, with a spatial resolution of 3 m, were processed using the SBAS-InSAR technique to retrieve surface deformation. The results indicate that the mean deformation rate difference in the overlapping areas between the two SAR tracks is approximately −5.54 mm/a. After applying the difference-constrained correction, the coefficient of determination (R²) between the mosaicked InSAR results and leveling observations increased to 0.739, while the MAE and RMSE decreased to 4.706 and 5.538 mm, respectively, demonstrating good stability in achieving inter-track consistency and continuous regional deformation representation. Analysis of the corrected InSAR results reveals that, during 2022–2024, areas exhibiting uplift and subsidence trends accounted for 37.6% and 62.4% of the study area, respectively, while the proportions of cumulative subsidence and uplift areas were 66.45% and 33.55%. In the main urban area, surface deformation rates are generally stable and predominantly within ±5 mm/a, whereas subsidence rates in the eastern new district are significantly higher than those in the main urban area and the western new district. Along subway lines, deformation rates are mainly within ±5 mm/a, with relatively larger deformation observed only in localized sections of the eastern segment of Line 1. Further analysis of typical zones along the subway corridors shows that densely built areas in the western part of the main urban area remain relatively stable, while building-concentrated areas in the eastern region exhibit a persistent relative subsidence trend. Overall, the results demonstrate that the proposed inter-track InSAR mosaicking method based on the mean deformation difference in overlapping areas can effectively support subsidence monitoring and spatial pattern identification along urban subway lines and key regions under relative calibration conditions, providing reliable remote sensing information for refined urban management and infrastructure risk assessment. Full article

Objective: The purpose of this study is to develop a mechatronic device capable of characterizing the kinematics of the knee joint, based on the acquisition and analysis of data focused on the knee joint point. Methods: A mechatronic device was designed using dimensional data from a participant’s lower limb (1.59 m, 57 kg), obtained through 3D scanning. The device, based on a proportional mechanism aligned with anatomical reference points, allows the evolution of the knee joint pivot point (PPKJ) to be recorded. Ten healthy subjects (aged 22–26 years, height 1.50–1.63 m, body mass 48–59 kg) were selected for testing. The device was placed on each knee to record joint trajectories during squats. The trajectories were classified into two groups: extension to flexion and flexion to extension. For each group, the average trajectory was calculated. Results: Forty PPKJ trajectories were obtained, divided into two sets: extension to flexion with a range of 8° to 51.3° and flexion to extension with a range of 6.7° to 56.83°, which allowed the mean trajectory and cubic polynomial regression to be calculated as the best approximation for characterizing the trajectory of the instantaneous center of rotation of the knee joint. Conclusions: The developed mechatronic device offers an accessible and non-invasive solution for recording the trajectory of the knee joint pivot point in individuals with characteristics like those in the study. This alternative approach could improve the representation of knee kinematics in the design of customized prostheses, exoskeletons, and rehabilitation devices for lower limbs. Full article

This study explores an alternative design methodology for architectural image generation using generative AI, addressing the challenge of how AI-generated imagery can preserve formative tendencies while enabling creative variation and user agency. Departing from conventional prompt-based approaches, the process utilizes only a minimal initial image set and proceeds by reintroducing solely the synthesized outcomes during the blending and iterative synthesis stages. The central research question asks whether AI can sustain and transform architectural tendencies through iterative synthesis despite limited input data, and how such tendencies might accumulate into consistent typological patterns. The research examines how formative tendencies are preserved and transformed, based on four aesthetic elements: layer, scale, density, and assembly. These four elements reflect diverse architectural ideas in spatial, proportional, volumetric, and tectonic characteristics commonly observed in architectural representations. Observing how these tendencies evolve across iterations allows the study to evaluate how AI negotiates between structural preservation and creative deviation, revealing the generative patterns underlying emerging AI typologies. The study employs SSIM, LPIPS, and CLIP similarity metrics as supplementary indicators to contextualize these tendencies. The results demonstrate that iterative blending enables the deconstruction and recomposition of archetypal formal languages, generating new visual variations while preserving identifiable structural and semantic tendencies. These outputs do not converge into generalized imagery but instead retain identifiable tendencies. Furthermore, the study positions user selection and intervention as a crucial mechanism for mediating between accidental transformation and intentional direction, proposing AI not as a passive generator but as a dialogical tool. Finally, the study conceptualizes such consistent formal languages as “AI Typologies” and presents the potential for a systematic design methodology founded upon them as a complementary alternative to prompt-based workflows. Full article

Background and Objectives: Acute type A aortic dissection (ATAAD) remains a life-threatening condition requiring prompt surgical management. Over the last decades, improvements in diagnosis, surgical techniques, and perioperative care have influenced patient characteristics and outcomes. This study analyzes temporal trends in the clinical profiles and results of patients surgically treated for acute type A aortic dissection (ATAAD) in a Northern Italian region over a fifteen-year period. Materials and Methods: All consecutive patients undergoing emergency surgery for acute Stanford type A aortic dissection or acute intramural hematoma (IMH) between January 2010 and December 2024 were retrospectively reviewed. Patients with chronic penetrating atherosclerotic ulcer or traumatic etiology were excluded. Demographic, clinical, and perioperative variables were analyzed to assess temporal changes. Trends were evaluated using linear regression and Cochran–Armitage tests for trend. Results: A total of 427 patients underwent surgery for AAD during the study period. The proportion of patients presenting with preoperative intubation significantly decreased over time (p for trend < 0.05), as did the incidence of preoperative shock (p for trend < 0.001). Conversely, the mean EuroSCORE showed a non-significant increase over the years. No significant differences were observed in age or other baseline parameters. A non-significant but progressive increase in female representation was observed over time (p = 0.064). Given this observation, a sex-based subanalysis was performed: women were significantly older (p < 0.001) and presented with higher EuroSCORE values (p < 0.001) compared to men, yet postoperative mortality was similar between sexes. This finding contrasts with recent reports suggesting worse outcomes among female patients. Conclusions: Over fifteen years, patients undergoing surgery for acute type A aortic dissection have shown decreasing rates of preoperative critical conditions, reflecting earlier diagnosis and improved management. Despite higher operative risk scores, women demonstrated comparable short-term survival to men within our regional program. Multivariable analysis showed that sex was dependently associated with in-hospital mortality. Full article

Transistor-level Integrated Circuit (IC) design is fundamental to modern electronics, yet it remains one of the most expertise-intensive and time-consuming stages of chip development. As circuit complexity continues to rise, the need to automate this low-level design process has become critical to sustaining innovation and productivity across the semiconductor industry. This study presents a fully automated methodology for transistor-level IC design using a novel framework that integrates Grammatical Evolution (GE) with Cadence SKILL code. Beyond automation, the framework explicitly examines how symmetry and asymmetry shape the evolutionary search space and resulting circuit structures. To address the time-consuming and expertise-intensive nature of conventional integrated circuit design, the framework automates the synthesis of both digital and analogue circuits without requiring prior domain knowledge. A specialised attribute grammar (AG) evolves circuit topology and sizing, with performance assessed by a multi-objective fitness function. Symmetry is analysed at three levels: (i) domain-level structural dualities (e.g., NAND/NOR mirror topologies and PMOS/NMOS exchanges), (ii) objective-level symmetries created by logic threshold settings, and (iii) representational symmetries managed through grammatical constraints that preserve valid connectivity while avoiding redundant isomorphs. Validation was carried out on universal logic gates (NAND and NOR) at multiple logic thresholds, as well as on a temperature sensor. Under stricter thresholds, the evolved logic gates display emergent duality, converging to mirror-image transistor configurations; relaxed thresholds increase symmetric plateaus and slow convergence. The evolved logic gates achieve superior performance over conventional Complementary Metal–Oxide–Semiconductor (CMOS), Transmission Gate Logic (TGL), and Gate Diffusion Input (GDI) implementations, demonstrating lower power consumption, a reduced Power–Delay Product (PDP), and fewer transistors. Similarly, the evolved temperature sensor exhibits improved sensitivity, reduced power, and Integral Nonlinearity (INL), and a smaller area compared to the conventional Proportional to Absolute Temperature (PTAT) or “gold” circuit, without requiring resistors. The analogue design further demonstrates beneficial asymmetry in device roles, breaking canonical structures to achieve higher performance. Across all case studies, the evolved designs matched or outperformed their manually designed counterparts, demonstrating that this GE-based approach provides a scalable and effective path toward fully automated, symmetry-aware integrated circuit synthesis. Full article

Unmanned underwater vehicles (UUVs) face significant challenges in achieving safe and efficient autonomous navigation in complex marine environments due to uncertain perception, dynamic obstacles, and nonlinear coupled motion control. This study proposes a hierarchical autonomous navigation framework that integrates improved particle swarm optimization (PSO) for 3D global route planning, and a deep deterministic policy gradient (DDPG) algorithm enhanced by noisy networks and proportional prioritized experience replay (PPER) for local collision avoidance. To address dynamic sideslip and current-induced deviations during execution, a novel 3D adaptive line-of-sight (ALOS) guidance method is developed, which decouples nonlinear motion in horizontal and vertical planes and ensures robust tracking. The global planner incorporates a multi-objective cost function that considers yaw and pitch adjustments, while the improved PSO employs nonlinearly synchronized adaptive weights to enhance convergence and avoid local minima. For local avoidance, the proposed DDPG framework incorporates a memory-enhanced state–action representation, GRU-based temporal processing, and stratified sample replay to enhance learning stability and exploration. Simulation results indicate that the proposed method reduces route length by 5.96% and planning time by 82.9% compared to baseline algorithms in dynamic scenarios, it achieves an up to 11% higher success rate and 10% better efficiency than SAC and standard DDPG. The 3D ALOS controller outperforms existing guidance strategies under time-varying currents, ensuring smoother tracking and reduced actuator effort. Full article

Children with autism spectrum disorder (ASD) often display atypical emotional expressions and physiological responses, making emotion recognition challenging. This study proposes a multimodal recognition model employing a late fusion framework combining facial expression with physiological measures: electrodermal activity (EDA), temperature (TEMP), and heart rate (HR). Emotional states are annotated using two complementary schemes derived from a shared set of labels. Three annotators provide one categorical Ekman emotion for each timestamp. From these annotations, a majority-vote label identifies the dominant emotion, while a proportional distribution reflects the likelihood of each emotion based on the relative frequency of the annotators’ selections. Separate machine learning models are trained for each modality and for each annotation scheme, and their outputs are integrated through decision-level fusion. A distinct decision-level fusion model is constructed for each annotation scheme, ensuring that both the categorical and likelihood-based representations are optimally combined. The experiments on the EMBOA dataset, collected within the project “Affective loop in Socially Assistive Robotics as an intervention tool for children with autism”, show that the late fusion model achieves higher accuracy and robustness than unimodal baselines. The system attains an accuracy of 68% for categorical emotion classification and 78% under the likelihood-estimation scheme. The results obtained, although lower than those reported in other studies, suggest that further research into emotion recognition in autistic children using other fusions is warranted, even in the case of datasets with a significant number of missing values and low sample representation for certain emotions. Full article

This paper introduces the Marshall–Olkin unit-exponentiated-half-logistic (MO-UEHL) distribution, a novel three-parameter model designed to enhance the flexibility of the unit-exponentiated-half-logistic distribution through the incorporation of the Marshall–Olkin transformation. Defined on the unit interval $(0,1)$, the MO-UEHL distribution is well-suited for modeling proportional data exhibiting asymmetry. The Marshall–Olkin tilt parameter $\alpha$ explicitly controls the degree and direction of asymmetry, enabling the density to range from highly right-skewed to nearly symmetric unimodal forms, and even to left-skewed configurations for certain parameter values, thereby offering a direct mathematical representation of symmetry breaking in bounded proportional data. The resulting model achieves this versatility without relying on exponential terms or special functions, thus simplifying computational procedures. We derive its key mathematical properties, including the probability density function, cumulative distribution function, survival function, hazard rate function, quantile function, moments, and information-theoretic measures such as the Shannon and residual entropy. Parameter estimation is explored using maximum likelihood, maximum product spacing, ordinary and weighted least-squares, and Cramér–von Mises methods, with simulation studies evaluating their performance across varying sample sizes and parameter sets. The practical utility of the MO-UEHL distribution is demonstrated through applications to four real datasets from environmental and engineering contexts. The results highlight the MO-UEHL distribution’s potential as a valuable tool in reliability analysis, environmental modeling, and related fields. Full article

Background/Purpose: The study aimed to analyze the dimensions and width-to-length ratios of the maxillary anterior teeth in young native Cambodian adults and to assess their relationship with the golden proportion, symmetry, and sexual dimorphism. Materials and Methods: Maxillary study casts of 193 eligible Cambodian subjects, aged 18 to 25 years, were retrospectively evaluated. The width and length of their maxillary anterior teeth were measured using a digital caliper. Descriptive statistics, independent-samples t-test at 95% confidence intervals, Kolmogorov-Smirnov, Shapiro-Wilk, and Kruskal-Wallis tests were performed to analyze the data. Results: There was a high level of similarity between first and second quadrant measurements. Females showed slightly higher standard deviations for central incisors and lateral incisors than males across most ratios, indicating more variability in the width-in-length ratios for females. Males exhibited significantly greater tooth dimensions than females. The following results showed statistical significance with p < 0.05 and 95% confidence intervals. The mean crown width of the central incisors was 8.16 mm in males (CI: 8.03–8.29) and 7.87 mm in females (CI: 7.78–7.96). For the lateral incisors, the mean crown width was 6.69 mm in males (CI: 6.53–6.85) and 7.64 mm in females (CI: 7.43–7.85). The width-to-length ratio of the central incisors was higher in females (mean = 0.88; CI: 0.86–0.91) compared with males (mean = 0.87; CI: 0.84–0.89). Overall, proportional relationships remained consistent across genders. The golden proportion guideline was not applicable, as observed ratios ranged from 0.90 to 1.67 (all below 1.618), and RED values exceeded 80%. The null hypothesis was rejected due to the significant gender differences found in tooth dimensions and width-to-length ratios. Conclusions: There was no significant difference in maxillary anterior tooth dimensions for the right and left sides among the Cambodian population. Males had statistically larger teeth than females. Width-to-length ratios were greater in females for central incisors; however, the proportional relationships between the genders remained relatively consistent. The golden proportion and RED proportions did not exist within this population. A smaller size characterizes Cambodian dentition compared to that of other ethnic groups. Finally, these results can serve as an indicator for planning customized esthetic treatment in Cambodians. Future studies with larger sample sizes are needed to ensure the representation of the whole Cambodian population. Full article

Research in Educational Sciences has suggested that a student’s participation in the classroom varies according to their gender. In STEM fields of higher education, male students tend to participate more in class than female students. However, with respect to the specific form of participation represented by students’ questioning, the conclusions of the existing studies are not yet perfectly overlapping in their findings. Considering that the posing of questions by students is one of the activities that reveal greater student participation and involvement in class, we aimed to investigate and characterize the questioning patterns of male and female students in chemistry classes and within the contexts of various teaching strategies designed to encourage their questioning. In an effort to contribute to a deeper understanding of student questioning in higher education, and with a particular focus on gender differences, this study also sought to identify the contexts and conditions that are more conducive to questioning by male and female students. This study was conducted in two first-year chemistry classes attended by sciences and engineering students at a Portuguese university, following a mixed methodology. Concerning the students’ questioning patterns, specifically in terms of their frequency and contextual occurrence, our findings showed that although in the first semester the female students asked fewer questions in class than the male students, in proportion to the representation of each gender in the class, this trend was reversed in the second semester. As the year developed and students became more acquainted with each other, female students asked more questions. The number of questions asked in each phase of the study leads us to conclude that, with respect to asking questions, female students benefit from strategies that foster familiarisation with the context. On the other hand, male students do not benefit from an increased familiarisation with the context. Full article