Search Results (479)

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

In this work, we present a high-performance tapered quantum cascade laser (QCL) designed to achieve both high output power and low divergence angle. By integrating a tapered waveguide with a Fabry–Perot structure, significant improvements of tapered QCL devices in both output power and beam quality are demonstrated. The optimized 50 µm wide tapered QCL achieved a maximum output power of 2.76 W in pulsed operation with a slope efficiency of 3.52 W/A and a wall-plug efficiency (WPE) of 16.2%, while reducing the divergence angle to 13.01°. The device maintained a maximum power of 1.34 W with a WPE exceeding 8.2%, measured under room temperature and continuous wave (CW) operation. Compared to non-tapered Fabry–Perot QCLs, the tapered devices exhibited a nearly 10-fold increase in output power and over 200% improvement in WPE. This work provides a promising pathway for advancing mid-infrared laser technology, particularly for applications requiring high power, low divergence, and temperature stability. Full article

This study investigates the fabrication, nanomechanical behavior, and tribological performance of nanostructured superlattice coatings (NSCs) composed of alternating TiAlSiNb-N/TiCr-CN bilayers. Deposited via High-Power Ion-Plasma Magnetron Sputtering (HiPIPMS) onto 100Cr6 steel substrates, the coatings achieved nanohardness values of ~25 GPa and elastic moduli up to ~415 GPa. A novel empirical method was applied to extract stress–strain field (SSF) gradient and divergence profiles from nanoindentation load–displacement data. These profiles revealed complex, depth-dependent oscillations attributed to alternating strain-hardening and strain-softening mechanisms. Fourier analysis identified dominant spatial wavelengths, DWL, ranging from 4.3 to 42.7 nm. Characteristic wavelengths WL1 and WL2, representing fine and coarse oscillatory modes, were 8.2–9.2 nm and 16.8–22.1 nm, respectively, aligning with the superlattice period and grain-scale features. The hyperfine structure exhibited non-stationary behavior, with dominant wavelengths decreasing from ~5 nm to ~1.5 nm as the indentation depth increased. We attribute the SSF gradient and divergence spatial oscillations to alternating strain-hardening and strain-softening deformation mechanisms within the near-surface layer during progressive loading. This cyclic hardening–softening behavior was consistently observed across all NSC samples, suggesting it represents a general phenomenon in thin film/substrate systems under incremental nanoindentation loading. The proposed SSF gradient–divergence framework enhances nanoindentation analytical capabilities, offering a tool for characterizing thin-film coatings and guiding advanced tribological material design. Full article

In response to the increasing demands for reliable, fast, and secure communications beyond 5G scenarios, the high-capacity networks have become a focal point. Quantum communication is at the forefront of this research, offering unmatched throughput and security. A free space optics (FSO) communication system integrated with fiber-end is designed and investigated using the Bennett–Brassard 1984 quantum key distribution (BB84-QKD) protocol. Simulation results show that reliable transmission can be achieved over a 10–15 km fiber length with a signal power of −19.54 dBm and high optical-to-signal noise of 72.28–95.30 dB over a 550 m FSO range under clear air, haze, fog, and rain conditions at a data rate of 1 Gbps. Also, the system using rectilinearly and circularly polarized signals exhibits a Stokes parameter intensity of −4.69 to −35.65 dBm and −7.7 to −35.66 dBm Stokes parameter intensity, respectively, over 100–700 m FSO range under diverse weather conditions. Likewise, for the same scenario, an FSO range of 100 m incorporating 2.5–4 mrad beam divergence provides the Stokes power intensity of −6.03 to −11.1 dBm and −9.04 to −14.12 dBm for rectilinearly and circularly polarized signals, respectively. Moreover, compared to existing works, this work allows faithful and secure signal transmission in free space, considering FSO–fiber link losses. Full article

To address the challenges brought by the high penetration of renewable energy in power systems, such as multi-scale dynamic interactions, high feature dimensionality, and limited model generalization, this paper proposes a transient stability assessment (TSA) method that combines temporal feature selection with deep learning-based modeling. First, a two-stage feature selection strategy is designed using the inter-class Mahalanobis distance and Spearman rank correlation. This helps extract highly discriminative and low-redundancy features from wide-area measurement system (WAMS) time-series data. Then, a parallel LSTM-Transformer architecture is constructed to capture both short-term local fluctuations and long-term global dependencies. A variational inference mechanism based on a Gaussian mixture model (GMM) is introduced to enable dynamic representations fusion and uncertainty modeling. A composite loss function combining improved focal loss and Kullback–Leibler (KL) divergence regularization is designed to enhance model robustness and training stability under complex disturbances. The proposed method is validated on a modified IEEE 39-bus system. Results show that it outperforms existing models in accuracy, robustness, interpretability, and other aspects. This provides an effective solution for TSA in power systems with high renewable energy integration. Full article

Understanding how fish communities respond to long-term environmental changes in regulated floodplain ecosystems is essential for managing biodiversity amid increasing anthropogenic and climatic pressures. This study evaluates the spatiotemporal dynamics of functional diversity in juvenile fish assemblages within the Dnipro-Orilskiy Nature Reserve (Ukraine) from 1997 to 2015. By employing a combination of extensive ichthyological field surveys and satellite-derived environmental indices (including NDVI, chlorophyll-a, turbidity, and spectral proxies for algal blooms), we assessed the impacts of eutrophication, hydrological alterations, and climate warming on functional structure. Our results reveal three key responses in fish functional diversity: (1) a decline in functional specialization and imbalance, indicating the loss of unique ecological roles and increased redundancy; (2) a rise in functional divergence, reflecting a shift toward species with outlying trait combinations; and (3) a complex pattern in functional richness, with trends varying by site and trait structure. These shifts are linked to increasing eutrophication and warming, particularly in floodplain areas. Remote sensing effectively captured spatial variation in eutrophication-related water quality and proved to be a powerful tool for linking environmental change to fish community dynamics, not least in inaccessible areas. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

For over a century, forgetting research has shown that recall decreases along a power or exponential function over time. It is tempting to assume that empirical forgetting curves are equivalent to the rate at which individual memories are forgotten. This assumption would be erroneous, because forgetting curves are influenced by an often-neglected factor: the distribution of memory strengths relative to a recall threshold. For example, if memories with normally distributed initial strengths were forgotten at a linear rate, percent correct would not be linear, it would decrease rapidly when the peak of the distribution was crossing the recall threshold and slowly when one of the tails was crossing the threshold. We describe a distribution model of memory that explains the divergence between forgetting curves and item forgetting rates. The model predicts that forgetting curves can be approximately linear (or even concave, like the right side of a frown) when percent correct is high. This prediction was supported by previous evidence and an experiment where participants learned word pairs to a criterion. Beyond its theoretical implications, the distribution model also has implications for education: Creating memories that are just above the threshold helps on short-term tests but does not form lasting memories. Full article

The Bayesian network is a directed, acyclic graphical model that can offer a structured description for probabilistic dependencies among random variables. As powerful tools for classification tasks, Bayesian classifiers often require computing joint probability distributions, which can be computationally intractable due to potential full dependencies among feature variables. On the other hand, Naïve Bayes, which presumes zero dependencies among features, trades accuracy for efficiency and often comes with underperformance. As a result, non-zero dependency structures, such as trees, are often used as more feasible probabilistic graph approximations; in particular, Tree Augmented Naïve Bayes (TAN) has been demonstrated to outperform Naïve Bayes and has become a popular choice. For applications where a variable is strongly influenced by multiple other features, TAN has been further extended to the k-dependency Bayesian classifier (KDB), where one feature can depend on up to k other features (for a given $k\ge 2$). In such cases, however, the selection of the k parent features for each variable is often made through heuristic search methods (such as sorting), which do not guarantee an optimal approximation of network topology. In this paper, the novel notion of k-tree Augmented Naïve Bayes (k-TAN) is introduced to augment Naïve Bayesian classifiers with k-tree topology as an approximation of Bayesian networks. It is proved that, under the Kullback–Leibler divergence measurement, k-tree topology approximation of Bayesian classifiers loses the minimum information with the topology of a maximum spanning k-tree, where the edge weights of the graph are mutual information between random variables conditional upon the class label. In addition, while in general finding a maximum spanning k-tree is NP-hard for fixed $k\ge 2$, this work shows that the approximation problem can be solved in time $O\left(n^{k+1}\right)$ if the spanning k-tree also desires to retain a given Hamiltonian path in the graph. Therefore, this algorithm can be employed to ensure efficient approximation of Bayesian networks with k-tree augmented Naïve Bayesian classifiers of the guaranteed minimum loss of information. Full article

The electrical grid is undergoing increasing integration of decentralized power sources connected to the low-voltage network. In this context, the concept of a microgrid has emerged as a system comprising small-scale energy sources, loads, and storage devices, coordinated to operate as a single controllable entity capable of functioning in either grid-connected or islanded mode. The microgrid may be organized in a centralized configuration, such as a master-slave scheme, wherein the centralized converter, i.e., the grid-forming converter (GFC), plays a pivotal role in ensuring system stability and control. This paper introduces a plug-in repetitive controller (RC) strategy tuned to even harmonic orders for application in a three-phase GFC, diverging from the conventional approach that focuses on odd harmonics. The proposed control is designed within a synchronous reference frame and is targeted at centralized AC microgrids, particularly during islanded operation. Simulation results are presented to assess the microgrid’s power flow and power quality, thereby evaluating the performance of the GFC. Additionally, the proposed control was implemented on a Texas Instruments TMS320F28335 digital signal processor and validated through hardware-in-the-loop (HIL) simulation using the Typhoon HIL 600 platform, considering multiple scenarios with both linear and nonlinear loads. The main results highlight that the RC improves voltage regulation, mitigates harmonic distortion, and increases power delivery capability, thus validating its effectiveness for GFC operation. Full article

Background/Objectives: Previous studies have typically investigated injury risk factors either by body region or sport type in isolation, limiting their practical applicability to real-world settings where multiple factors interact. However, injury risk is inherently multifactorial—shaped by a complex interplay of demographic, physiological, and training-related characteristics that differ by anatomical site and sport context. This study addresses that gap by simultaneously analyzing predictors across multiple body regions and sport-specific environments. This integrated approach is critical for developing more precise, evidence-based injury prevention strategies tailored to the specific demands and risk profiles of amateur athletes. This study aimed to identify key predictors of injury risk across various body regions and sport-specific contexts among amateur athletes. Specifically, we sought to (1) develop predictive models that include demographic and body composition variables, and (2) compare the relative predictive strength of these variables across models, highlighting differences in their influence by injury location and sport type. Methods: A total of 454 amateur athletes (219 males and 235 females) participated. Data on anthropometry, body composition, training load were collected. Injury history was obtained via self-administered questionnaires, with participants reporting injuries that had occurred during the 12 months prior to the time of data collection. Logistic regression models were used to identify significant predictors, and Nagelkerke’s R² was calculated to assess model fit. Results: Overall, 49.78% of athletes experienced injuries, with a higher proportion in females (54.47%) than in males (44.75%). Age demonstrated divergent effects: it was protective against both upper and lower limb injuries in male individual-sport athletes (OR = 0.62 and OR = 0.69, respectively) and in female athletes across sport types (ORs = 0.75–0.64), but conversely increased the risk of upper limb injuries in male team-sport athletes (OR = 1.88). In female individual athletes, higher Skeletal Muscle Index (SMI) predicted upper limb injuries (OR = 1.18, p = 0.034). In female team athletes, higher Muscle-to-Fat Ratio (MFR) (OR = 2.46, p = 0.017) and BMI (OR = 1.67, p = 0.008) predicted upper limb injuries, while higher Fat Mass Index (FMI) predicted lower limb injuries (OR = 1.70, p = 0.009). Models showed moderate explanatory power (Nagelkerke’s R² ranging from 0.03 to 0.33). Conclusions: These findings suggest that injury risk profiles are highly context-dependent. Preventive strategies should be tailored by sex and sport type, for example, younger athletes in team sports may benefit from age-sensitive load monitoring, while in female team athletes, targeted interventions addressing both fat and muscle balance could be essential. Age, body composition, and sport-specific demands should be considered in individualized injury prevention planning. Full article

The detection of weak moving targets in heterogeneous clutter backgrounds is a significant challenge in radar systems. In this paper, we propose a track-before-detect (TBD) method based on information geometry (IG) theory applied to range-azimuth measurements, which extends the IG detectors to multi-frame detection through inter-frame information integration. The approach capitalizes on the distinctive benefits of the information geometry detection framework in scenarios with strong clutter, while enhancing the integration of information across multiple frames within the TBD approach. Specifically, target and clutter trajectories in multi-frame range-azimuth measurements are modeled on the Hermitian positive definite (HPD) and power spectrum (PS) manifolds. A scoring function based on information geometry, which uses Kullback–Leibler (KL) divergence as a geometric metric, is then devised to assess these motion trajectories. Moreover, this study devises a solution framework employing dynamic programming (DP) with constraints on state transitions, culminating in an integrated merit function. This algorithm identifies target trajectories by maximizing the integrated merit function. Experimental validation using real-recorded sea clutter datasets showcases the effectiveness of the proposed algorithm, yielding a minimum 3 dB enhancement in signal-to-clutter ratio (SCR) compared to traditional approaches. Full article

This study presents, for the first time, a new class of interval-valued superquadratic stochastic processes and examines their core properties through the lens of the center-radius total order relation on intervals. These processes serve as a powerful tool for modeling uncertainty in stochastic systems involving interval-valued data. By utilizing their intrinsic structure, we derive sharpened versions of Jensen-type and Hermite–Hadamard-type inequalities, along with their fractional extensions, within the framework of mean-square stochastic Riemann–Liouville fractional integrals. The theoretical findings are validated through extensive graphical representations and numerical simulations. Moreover, the applicability of the proposed processes is demonstrated in the domain of information theory by constructing novel stochastic divergence measures and Shannon’s entropy grounded in interval calculus. The outcomes of this work lay a solid foundation for further exploration in stochastic analysis, particularly in advancing generalized integral inequalities and formulating new stochastic models under uncertainty. Full article

Wind power, as a clean and renewable energy source, plays an increasingly important role in the global transition to low-carbon energy systems. However, its inherent volatility and unpredictability pose challenges for accurate short-term prediction. This study proposes an ultra-short-term wind power prediction framework that integrates multiple technical indicators with the extreme gradient boosting (XGBoost) algorithm. Inspired by financial time series analysis, the model incorporates K-line representations, power fluctuation features, and classical technical indicators, including the moving average convergence divergence (MACD), Bollinger bands (BOLL), and average true range (ATR), to enhance sensitivity to short-term variations. The proposed method is validated on two real-world wind power datasets from Inner Mongolia, China, and Germany, sourced from the European network of transmission system operators for electricity (ENTSO-E). The experimental results show that the model achieves strong performance on both datasets, demonstrating good generalization ability. For instance, on the Inner Mongolia dataset, the proposed model reduces the mean squared error (MSE) by approximately 11.4% compared to the long short-term memory (LSTM) model, significantly improving prediction accuracy. Full article

A pioneering detailed comparative study of the dynamics of plasma flows generated by high-power nanosecond and high-intensity femtosecond laser pulses with similar fluences of up to $3\times {10}^4$ J/cm² is presented. The experiments were conducted on the petawatt laser facility PEARL using two types of high-power laser radiation: femtosecond pulses with energy exceeding 10 J and a duration less than 60 fs, and nanosecond pulses with energy exceeding 10 J and a duration on the order of 1 ns. In the experiments, high-velocity (>100 km/s) flows of «femtosecond» (created by femtosecond laser pulses) and «nanosecond» plasmas propagated in a vacuum across a uniform magnetic field with a strength over 14 T. A significant difference in the dynamics of «femtosecond» and «nanosecond» plasma flows was observed: (i) The «femtosecond» plasma initially propagated in a vacuum (no B-field) as a collimated flow, while the «nanosecond» flow diverged. (ii) The «nanosecond» plasma interacting with external magnetic field formed a quasi-spherical cavity with Rayleigh–Taylor instability flutes. In the case of «femtosecond» plasma, such flutes were not observed, and the flow was immediately redirected into a narrow plasma sheet (or «tongue») propagating across the magnetic field at an approximately constant velocity. (iii) Elongated «nanosecond» and «femtosecond» plasma slabs interacting with a transverse magnetic field broke up into Rayleigh–Taylor «tongues». (iv) The ends of these «tongues» in the femtosecond case twisted into vortex structures aligned with the ion motion in the external magnetic field, whereas the «tongues» in the nanosecond case were randomly oriented. It was suggested that the twisting of femtosecond «tongues» is related to Hall effects. The experimental results are complemented by and consistent with numerical 3D magnetohydrodynamic simulations. The potential applications of these findings for astrophysical objects, such as short bursts in active galactic nuclei, are discussed. Full article