Search Results (1,150)

This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The objective of this study is to evaluate the effectiveness of the Shannon spectral entropy method in detecting and assessing short-term seismic events through a seismogram analysis. This method has demonstrated sensitivity to variations in the spectral characteristics of the registered signals. A threshold value for the increase in spectral entropy information has been pinpointed for reliable P-wave detection. The results could be applied in real-time automated seismic monitoring systems. In addition to the conventional spectral analysis techniques, the proposed methodology may serve as the input to the neural network models used in seismological applications. Full article

Hydrogen offers an effective pathway for the large-scale storage of renewable energy. For a tourist city located in a plateau region rich in renewable energy, hydrogen shows great potential for reducing carbon emissions and utilizing uncertain renewable energy. Herein, the wind–solar–hydrogen stand-alone and grid-connected systems in the plateau tourist city of Lijiang City in Yunnan Province are modeled and techno-economically evaluated by using the HOMER Pro software (version 3.14.2) with the multi-criteria decision analysis models. The system is composed of 5588 kW solar photovoltaic panels, an 800 kW wind turbine, a 1600 kW electrolyzer, a 421 kWh battery, and a 50 kW fuel cell. In addition to meeting the power requirements for system operation, the system has the capacity to provide daily electricity for 200 households in a neighborhood and supply 240 kg of hydrogen per day to local hydrogen-fueled buses. The stand-alone system can produce 10.15 × 10⁶ kWh of electricity and 93.44 t of hydrogen per year, with an NPC of USD 8.15 million, an LCOE of USD 0.43/kWh, and an LCOH of USD 5.26/kg. The grid-connected system can generate 10.10 × 10⁶ kWh of electricity and 103.01 ton of hydrogen annually. Its NPC is USD 7.34 million, its LCOE is USD 0.11/kWh, and its LCOH is USD 3.42/kg. This study provides a new solution for optimizing the configuration of hybrid renewable energy systems, which will develop the hydrogen economy and create low-carbon-emission energy systems. Full article

Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)², a hybrid entropy-based framework designed to detect and mitigate anomalies in large-scale heterogeneous networks. The framework combines Shannon entropy analysis with Associated Random Neural Networks (ARNNs) and integrates semantic reasoning through RDF/SPARQL, all embedded within a distributed Apache Spark 3.5.0 pipeline. We validate (H-DIR)² across three critical attack scenarios—SYN Flood (TCP), DAO-DIO (RPL), and NTP amplification (UDP)—using real-world datasets. The system achieves a mean detection latency of 247 ms and an AUC of 0.978 for SYN floods. For DAO-DIO manipulations, it increases the packet delivery ratio from 81.2% to 96.4% (p < 0.01), and for NTP amplification, it reduces the peak load by 88%. The framework achieves vertical scalability across millions of endpoints and horizontal scalability on datasets exceeding 10 TB. All code, datasets, and Docker images are provided to ensure full reproducibility. By coupling adaptive neural inference with semantic explainability, (H-DIR)² offers a transparent and scalable solution for cloud–IoT cybersecurity, establishing a robust baseline for future developments in edge-aware and zero-day threat detection. Full article

Fissure swarms and dike swarms in Iceland constitute the main parts of volcanic systems that are 40–150 km long, 5–20 km wide, extend to depths of 10–20 km, and contain 2 × 10¹⁴ outcrop-scale (≥0.1 m) and 10^22–23 down to grain-scale (≥1 mm) fractures, suggesting that statistical physics is an appropriate method of analysis. Length-size distributions of 565 outcrop-scale Holocene fissures (tension fractures and normal faults) and 1041 Neogene dikes show good to excellent fits with negative power laws and exponential laws. Here, the Helmholtz free energy is used to represent the energy supplied to the swarms and to derive the Gibbs–Shannon entropy formula. The calculated entropies of 12 sets and subsets of fissures and 3 sets and subsets of dikes all show strong positive correlations with sets/subsets length ranges and scaling exponents. Statistical physics considerations suggest that, at a given time, the probability of the overall state of stress in a crustal segment being heterogeneous is much greater than the state of stress being homogeneous and favourable to the propagation of a fissure or a dike. In a heterogeneous stress field, most fissures/dikes become arrested after a short propagation—which is a formal explanation of the observed statistical size-length distributions. As the size of the stress-homogenised rock volume increases larger fissures/dikes can form, increasing the length range of the distribution (and its entropy) which may, potentially, transform from an exponential distribution into a power-law distribution. Full article

Entropy is a foundational concept across scientific domains, playing a role in understanding disorder, randomness, and uncertainty within systems. This study applies Shannon’s entropy in information theory to evaluate and manage complexity in industrial supply chain management. The purpose of the study is to propose a quantitative modeling method, employing Shannon’s entropy model as a proxy to assess the complexity in SCs. The underlying assumption is that information entropy serves as a proxy for the complexity of the SC. The research method is quantitative modeling, which is applied to four focal companies from the agrifood and metalworking industries in Southern Brazil. The results showed that companies prioritizing cost and quality exhibit lower complexity compared to those emphasizing flexibility and dependability. Additionally, information flows related to specially engineered products and deliveries show significant differences in average entropies, indicating that organizational complexities vary according to competitive priorities. The implications of this suggest that a focus on cost and quality in SCM may lead to lower complexity, in opposition to a focus on flexibility and dependability, influencing strategic decision making in industrial contexts. This research introduces the novel application of information entropy to assess and control complexity within industrial SCs. Future studies can explore and validate these insights, contributing to the evolving field of supply chain management. Full article

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article

Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update distribution. ERFO requires no additional communication, adds a single-scalar hyperparameter $\lambda$, and integrates seamlessly into any FedAvg-style training loop. We derive a closed-form gradient for the entropy regularizer and provide convergence guarantees: under $\mu$-strong convexity and L-smoothness, ERFO achieves the same $O(1/T)$ (or linear) rates as FedAvg (with only $O\left(\lambda \right)$ bias for fixed $\lambda$ and exact convergence when ${\lambda }_t\to 0$); in the non-convex case, we prove stationary-point convergence at $O(1/T)$. Empirically, on five-client non-IID splits of the UNSW-NB15 intrusion-detection dataset, ERFO yields a +1.6 pp gain in accuracy and +0.008 in macro-F1 over FedAvg with markedly smoother dynamics. On a three-of-five split of PneumoniaMNIST, a fixed $\lambda$ matches or exceeds FedAvg, FedProx, and SCAFFOLD—achieving 90.3% accuracy and 0.878 macro-F1—while preserving rapid, stable learning. ERFO’s gradient-only design is model-agnostic, making it broadly applicable across tasks. Full article

With the rapid advancement of urbanization, the increasing demand and insufficient supply of public service facilities for children have become urgent problems requiring resolution. This study employs the Shannon diversity index, the location entropy, spatial autocorrelation, and the Geographically Weighted Regression (GWR) to analyze the spatial distribution characteristics and influencing factors of children’s public service facilities in the central urban area of Shenyang. The findings of the study are as follows: (1) There are significant differences in the spatial distribution of children’s public service facilities. Higher quantity distribution and diversity index are observed in the core area and Hunnan District compared to the peripheral areas. The Gini coefficient of various facilities is below the fair threshold of 0.4, but 90.32% of the study units have location entropy values below 1, indicating a supply–demand imbalance. (2) The spatial distribution of various facilities exhibits significant clustering characteristics, with distinct differences between high-value and low-value cluster patterns. (3) The spatial distribution of facilities is shaped by four factors: population, transportation, economy, and environmental quality. Residential area density and commercial service facility density emerge as the primary positive drivers, whereas road density and average housing price act as the main negative inhibitors. (4) The mechanisms of influencing factors exhibit spatial heterogeneity. Positive driving factors exert significant effects on new urban areas and peripheral zones, while negative factors demonstrate pronounced inhibitory effects on old urban areas. Non-linear threshold effects are observed in factors such as subway station density and public transport station density. Full article

The present study analyzed the vegetation diversity in three forests located in southern Romania and assessed their degree of similarity. Data were collected using frame quadrat sampling and species taxonomic identification. The methodology included the calculation of ecological indices (Shannon–Wiener, equitability, maximum entropy, Menhinick, Margalef, McIntosh, Gleason, and Simpson) and statistical analysis using ANOVA and Duncan tests (p < 0.05). Similarity between forests was evaluated using the Jaccard and Dice/Sørensen coefficients. The results showed that biodiversity increases with area size, and the forest ecosystem in Vlădila exhibited the highest number of woody and herbaceous species. Although the forest ecosystem in Studinița had the greatest floristic diversity, according to the Shannon–Wiener index, it also showed higher equitability (0.911 compared to 0.673 in Vlădila) due to a more uniform species distribution. The forest ecosystem in Studinița acted as an intermediate zone between those in Grădinile and Vlădila. Variations in diversity among the three areas reflect ecological differences influenced by location-specific factors such as soil type, climatic conditions, and human interventions. This suggests that ecological conditions and the physical characteristics of forests significantly impact the number and types of species that can coexist within an ecosystem. Full article

We revisit the problem of random search walks in the two-dimensional (2D) space between concentric absorbing annuli, in which a searcher performs random steps until finding either the inner or the outer ring. By considering step lengths drawn from a power-law distribution, we obtain the exact analytical result for the search efficiency $\eta$ in the ballistic limit, as well as an approximate expression for $\eta$ in the regime of searches starting far away from both rings, and the scaling behavior of $\eta$ for very small initial distances to the inner ring. Our numerical results show good overall agreement with the theoretical findings. We also analyze numerically the absorbing probabilities related to the encounter of the inner and outer rings and the associated Shannon entropy. The power-law exponent marking the crossing of such probabilities (equiprobability) and the maximum entropy condition grows logarithmically with the starting distance. Random search walks inside absorbing annuli are relevant, since they represent a mean-field approach to conventional random searches in 2D, which is still an open problem with important applications in various fields. Full article

The wave–particle duality is the quintessence of quantum mechanics. This duality gives rise to distinct behaviors depending on the experimental setup, with the system exhibiting either wave-like or particle-like properties, depending on whether the focus is on interference (wave) or trajectory (particle). In the interaction with a beam splitter, photons with particle behavior can transform into a wave behavior and vice versa. In Wheeler’s delayed-choice gedanken experiment, this interaction is delayed so that the wave that initially travels through the interferometer can become a particle, avoiding the interaction. We show that this contradiction can be resolved using polarized entangled photon pairs. An analysis of Shannon’s entropy supports this proposal. Full article

The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon’s entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders. Full article

Existing activity measurement methods, such as gas analyzers, activity trackers, and camera-based systems, have limitations in accuracy, convenience, and privacy. To address these issues, this study proposes an improved activity estimation algorithm using a 60 GHz Frequency-Modulated Continuous-Wave (FMCW) radar. Unlike conventional methods that rely solely on distance variations, the proposed method incorporates both distance and velocity information, enhancing measurement accuracy. The algorithm quantifies activity levels using Shannon entropy to reflect the spatial–temporal variation in range signatures. The proposed method was validated through experiments comparing estimated activity levels with motion sensor-based ground truth data. The results demonstrate that the proposed approach significantly improves accuracy, achieving a lower Root Mean Square Error (RMSE) and higher correlation with ground truth values than conventional methods. This study highlights the potential of FMCW radar for non-contact, unrestricted activity monitoring and suggests future research directions using multi-channel radar systems for enhanced motion analysis. Full article

Cinematic aesthetic values have not been studied as thoroughly as those in music and the visual arts. Three hypotheses for these values are that they are like those of artistic paintings, that they emphasize the spatial coherence of the optical flow, and that they are temporally smooth. Here, we test these hypotheses and investigate other candidate aesthetic values by comparing the statistics of narrative movies and those obtained spontaneously. We perform these tests by using narrative movies from the early stages of cinematic history because these films are simple. We statistically compare these films with spontaneous movies of scenes from daily life. These statistical comparisons do not support the first hypothesis for early movies. The comparisons show that symmetry, balance, and image complexity (normalized Shannon entropy) are not different in early and spontaneous movies. For similar reasons, our data do not support the spatial coherence of early-movie optical flows as having cinematic aesthetic functions. However, in support of the third hypothesis, the temporal smoothness of luminance, but not of motions, appears to have cinematic aesthetic value. The data also uncovered two other cinematic aesthetic value candidates in both statistical surprise and spatial and temporal complexities. We discuss these candidates, pointing out similarities to music and the importance of film editing. Full article