Abstract

We investigate the effect of partial order parameter adaptation in the form of general functions on the synchronization behavior of coupled Kuramoto oscillators on top of random hypergraph models. The interactions between the oscillators are considered pairwise and triangular. Using the Ott–Antonsen ansatz, we obtain a set of self-consistent equations of the order parameter that describe the synchronization patterns. A broad diversity of synchronization transitions is observed as a result of the interplay between the partial adaptation approach, generalized adaptation functions, and coupling strengths. The system specifically shows a double-jump transition under a power law form of the adaptation function. A polynomial form of the adaptation function leads to the emergence of an intermediate synchronization state for specific combinations of one negative and one positive coefficient. Moreover, the synchronization transition may become continuous or explosive when the pairwise coupling strength varies. This type of synchronization behavior is further found by using a Gaussian adaptation function.

Abstract

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein–protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power-law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four-parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, lognormal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks.

Abstract

This study aims to develop a generalized concept that will enable double explosive transitions in the forward and backward directions or a combination thereof. We found two essential factors for generating such phase transitions: the use of higher-order (triadic) interactions and the partial adaptation of a global order parameter acting on the triadic coupling. A compromise between the two factors may result in a double explosive transition. To reinforce numerical observations, we employed the Ott-Antonsen ansatz. We observed that for a wide class of hypergraphs, combining two elements can result in a double explosive transition.

Abstract

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the statedependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator subdynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches.

Abstract

An embodiment herein provides a re-configurable unmanned aerial vehicle that re-configures its shape based on the shape, size, weight of a payload, and efficiently performs payload delivery in real-time. The re-configurable unmanned aerial vehicle includes one or more rotor units placed at corners and is connected by one or more scissor units. The re-configurable unmanned aerial vehicle approaches the payload in a first location, and analyses the position and dimension of the payload with a camera, that enables the one or more scissor units to adjust its length by at least one elongation or compression following size and shape of the payload and fit the payload within the re- configurable unmanned aerial vehicle. The re-configurable unmanned aerial vehicle takes off carrying the payload from the first location and lands at a second location.

Abstract

Grasping using an aerial robot can have many applications ranging from infrastructure inspection and maintenance to precise agriculture. However, aerial grasping is a challenging problem since the robot has to maintain an accurate position and orientation relative to the grasping object, while negotiating various forms of uncertainties (e.g., contact force from the object). To address such challenges, in this paper, we integrate a novel passive gripper design and advanced adaptive control methods to enable robust aerial grasping. The gripper is enabled by a pre-stressed band with two stable states (a flat shape and a curled shape). In this case, it can automatically initiate the grasping process upon contact with an object. The gripper also features a cable-driven system by a single DC motor to open the gripper without using cumbersome pneumatics. Since the gripper is passively triggered and initially has a straight shape, it can function without precisely aligning the gripper with the object (within an 80 mm tolerance). Our adaptive control scheme eliminates the need for any a priori knowledge (nominal or upper bounds) of uncertainties. The closed-loop stability of the system is analyzed via Lyapunov-based method. Combining the gripper and the adaptive control, we conduct comparative realtime experimental results to demonstrate the effectiveness of the proposed integrated system for grasping. Our integrated approach can pave the way to enhance aerial grasping for different applications.

Abstract

Contributing catchment land use patterns under rapid urbanization, agricultural practices and climatic variability in tropical regions significantly influence the sediment load of inland waterbodies such as reservoirs. This study presents a modeling framework to estimate qualitative turbidity dynamics in two tropical reservoirs (Bhadra and Tungabhadra) within same river basin but different landscape characteristics, using Sentinel-2 satellite data from 2016 to 2021 using Google Earth Engine (GEE). The framework demonstrates a robust capability of Sentinel-2 data, in detecting, mapping spatiotemporal trends and classifying turbidity levels from low to high along with impact of potential catchment characteristics. The Normalized Difference Turbidity Index (NDTI) values have a strong correlation with in-situ data (Earth Observing and Mapping EOMAP and field-based observations) having values > 10 NTU (nephelometric turbidity unit) with R2 = 0.81 and standard Error Estimate (SEE) of 5.3 respectively. In addition, the correlation between red band reflectance values with <10 NTU, has demonstrated a strong relation with an R2 and SEE of 0.74 and 1.7 respectively. Thus, the combination of NDTI and single red band reflectance values were used further for classifying the different turbidity levels (low, moderate and high). It was also observed that diverse land use patterns, particularly high proportions of urban and agriculture area along with rainfall, impact the annual and seasonal fluctuation of turbidity level in the reservoirs. The study demonstrates that remote sensing (RS) approach can effectively estimate turbidity levels in tropical reservoirs, highlighting the impact of catchment characteristics and supporting improved monitoring and sustainable water resource management.

Abstract

Abstract- Data is useful information collected, programmed, generated and/or altered for a specific application/s or for future use. Such data sampling/data record is of utmost importance in single use airborne systems especially the military warfare. The operator will be interested in acquisition of high speed data during flight but will be denied such opportunity due to protocol and bandwidth restrictions therein. This paper deals with one such data network which is popular with many avionics designers, the conventional command response protocol MIL-STD- 1553B which restricts the overall baud rate to 1Mbps. This paper proposes a unique data fusion technique to combine (i) a 100x baud rate Multi Carrier Wave (MCW) with (ii) conventional baud rate of 1Mbps on standard MIL-STD-1553B hardware. OFDM (Orthogonal Frequency Division Multiplexing) technique] and bus protocol that is used to achieve the data flow and fusion are explained in detail.

Abstract

The rapid advancement of Large Language Models (LLMs) has en- abled them to generate complex, multi-step plans and itineraries. However, these generated plans often lack temporal and spatial consistency, particularly in scenarios involving physical travel con- straints. This research aims to study the temporal performance of di!erent LLMs and presents a validation framework that eval- uates and improves the temporal consistency of LLM-generated travel itineraries. The system employs multiple state-of-the-art LLMs to generate travel plans and validates them against real-world "ight duration constraints using the AeroDataBox API. This work contributes to the understanding of LLM capabilities in handling complex temporal reasoning tasks like itinerary generation and provides a framework to rectify any temporal inconsistencies like overlapping journeys or unrealistic transit times in the itineraries generated by LLMs before the itinerary is given to the user. Our experiments reveal that while current LLMs frequently produce temporally inconsistent itineraries, these can be systematically and reliably corrected using our framework, enabling their practical deployment in large-scale travel planning.

Abstract

Background Music Emotion Recognition (MER) systems primarily rely on audio features (Wang et al., 2021), with recent approaches incorporating lyrics to analyze sentiment and structure for improved accuracy (Agrawal et al., 2021). Musical content, particularly melody, has been found to have a stronger capacity to convey perceived emotional expression than lyrics, although lyrics tend to enhance negative emotions more easily than positive emotions (Ali & Peynircioğlu, 2006). This variability raises important questions: Which component carries the greatest emotional information? Do listeners rely on music, vocals, or lyrics when assessing emotional content? Aim Here, we systematically analyse the contribution of music, vocals, and lyrics to perception of emotion in music. Methods For this study, we used the DEAM (Soleymani et al., 2013) and PMEmo (Zhang et al., 2018) datasets, comprising a total of 2,700 songs. The DEAM dataset includes 1,802 audio items annotated for both dynamic and static emotion using continuous valence and arousal ratings. Participants utilised a two-dimensional interface based on the Self‑Assessment Manikins (SAM) to continuously rate the emotional content at a 2‑Hz sampling rate. The PMEmo dataset contains 794 popular music choruses sourced from international music charts (Billboard Hot 100, iTunes Top 100, and UK Top 40) and was annotated using a similar interface to capture continuous dynamic ratings for valence and arousal at the same sampling rate. Songs were source-separated using Ultimate Vocal Remover (Takahashi et al., 2017) to isolate music and vocals, while lyrics were transcribed using OpenAI’s Whisper model (Radford et al., 2022) and verified manually. Deep learning models trained on diverse song and speech datasets predicted VA values for musical and vocal components, while lyrics were analyzed using models trained on general and lyrically annotated texts (Çano, 2017). Spearman's correlation was calculated between predicted and human-annotated (HA) VA values. Additionally, quadrant-based (Q1: positive V, high A; Q2: negative V, high A; Q3: negative V, low A; Q4: positive V, low A) concurrency analyses evaluated prediction alignment with human-identified emotional quadrants. Results The musical component exhibited the highest correlation with HA ratings (valence: r = 0.70, arousal: r = 0.74), followed by vocals (valence: r = 0.54, arousal: r = 0.65; all p < .001), while lyrics contributed the least (valence: r = 0.11, arousal: r = 0.01). In terms of overall quadrant concurrency, the musical component showed the highest concurrency (60.41%) followed by vocals (48.84%) and then lyrics (31.45%). Specifically, the highest quadrant concurrency was observed in Q3 (musical: 90.53%, vocal: 93.53%, lyrics: 66.16%) followed by Q1 for musical (64.51%) and vocals (39.10%), and Q4 for lyrics (42.50%). Discussion Our findings emphasize the dominant role of the musical component in shaping perceived emotional expression in Western tonal music, aligning with prior work highlighting melody’s emotional salience (Ali & Peynircioğlu, 2006). Furthermore, for music signifying negative valence and low arousal (Q3), there is a higher degree of congruence between the components in conveying sadness and its related emotions. These results suggest that MER systems could prioritize musical and vocal components over lyrical content (Wang et al., 2021; Agrawal et al., 2021), as they appear to primarily drive the perception of emotional expression. References Agrawal, Y., Shanker, R. G. R., & Alluri, V. (2021). Transformer-based approach towards music emotion recognition from lyrics. In V. U. Rao & R. Kailash (Eds.), Advances in information retrieval (pp. 167–175). Springer. https://doi.org/10.1007/978-3-030-72240-1_12 Ali, S. O., & Peynircioğlu, Z. F. (2006). Songs and emotions: Are lyrics and melodies equal partners? Psychology of Music, 34(4), 511–534. https://doi.org/10.1177/0305735606067168 Çano, E. (2017). MoodyLyrics: A sentiment-annotated lyrics dataset. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2017) (pp. 118–124). Association for Computational Linguistics. https://doi.org/10.18653/v1/S17-1017 Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022). Robust speech recognition via large-scale weak supervision. arXiv. https://doi.org/10.48550/arXiv.2212.04356 Soleymani, M., Caro, M., Schmidt, E., Sha, C.-Y., & Yang, Y.-H. (2013). 1000 songs for emotional analysis of music. In P. Herrera, E. Gómez, & T. Kaneko (Eds.), Proceedings of the 2nd ACM International Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies (pp. 1–6). Association for Computing Machinery. Takahashi, N., Huang, P.-S., & Lee, P. (2017). Multi-scale multi-band DenseNets for audio source separation. arXiv. https://doi.org/10.48550/arXiv.1706.09588 Wang, S., Xu, C., Ding, A. S., & Tang, Z. (2021). A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network. Electronics, 10(15), 1769. https://doi.org/10.3390/electronics10151769 Zhang, K., Zhang, H., Li, S., Yang, C., & Sun, L. (2018). The PMEmo dataset for music emotion recognition. In Proceedings of the 2018 ACM International Conference on Multimedia Retrieval (pp. 135–142). Association for Computing Machinery. https://doi.org/10.1145/3206025.3206037