Search Results (1,258)

Bow-tie microwave diodes have proven to be effective sensors of electromagnetic radiation across a wide wavelength range, from centimeter-scale radio waves to micrometer-scale mid-infrared radiation. Their operation is based on electron heating by strong electric fields. However, the experimental data obtained so far remain inconclusive, and the exact nature of the voltage detected by bow-tie diodes is not yet fully understood. In this work, we extend the investigation of the electrical properties of bow-tie diodes based on modulation-doped semiconductor structures with a wide spacer. The analysis focuses on the influence of diode metal contact geometry, illumination conditions, and orientation relative to the crystallographic axes. To elucidate the origin of the voltage detected by bow-tie diodes, we compare theoretical predictions of their electrical parameters—including voltage sensitivity, electrical resistance, asymmetry of the I–V characteristic in weak electric fields, and the nonlinearity coefficient of the I–V characteristic in strong electric fields—with corresponding experimental results. The results of our investigations indicate that, for most diodes, the detected voltage originates from electron heating by the microwave electric field, as evidenced by the polarity of the detected voltage matching the thermoelectric emf of hot carriers. Full article

We propose State-DynAttn, a hybrid architecture for robust air traffic flow prediction under weather disruptions, which integrates state-space models (SSMs) with dynamic graph attention to address the challenges of long-range dependency modeling and adaptive spatial–temporal relationship learning. The increasing complexity of air traffic systems, exacerbated by unpredictable weather events, demands methods that can simultaneously capture global temporal patterns and localized disruptions; existing approaches often struggle to balance these requirements efficiently. The proposed method employs two parallel branches: an SSM branch for continuous-time recurrent modeling of long-range dependencies with linear complexity, and a dynamic graph attention branch that adaptively computes node-pair weights while incorporating weather severity features through sparsification strategies for scalability. These branches are fused via a data-dependent gating mechanism, enabling the model to dynamically prioritize either global temporal dynamics or localized spatial interactions based on input conditions. Moreover, the architecture leverages memory-efficient attention computation and HiPPO initialization to ensure stable training and inference. Experiments on real-world air traffic datasets demonstrate that State-DynAttn outperforms existing baselines in prediction accuracy and robustness, particularly under severe weather scenarios. The framework’s ability to handle both gradual traffic evolution and abrupt disruption-induced changes makes it suitable for real-world deployment in air traffic management systems. Furthermore, the design principles of State-DynAttn can be extended to other spatiotemporal prediction tasks where long-range dependencies and dynamic relational structures coexist. This work contributes a principled approach to hybridizing state-space models with graph-based attention, offering insights into the trade-offs between computational efficiency and modeling flexibility in complex dynamical systems. Full article

We present a stochastic method for classifying high-elevation coniferous forest coverage that includes an uncertainty estimate using Landsat images. We evaluate trends in coniferous coverage from 1986 to 2024 in a sub-basin of the Great Salt Lake basin in the western United States This work was completed before the recent release of the extended National Land Cover Database (NLCD) data, so we use the 9 years of NLCD data previously available over the period from 2001 to 2021 for training and validation. We perform 100 draws of 5130 data points each using stratified sampling from the paired NLCD and Landsat data to generate 100 Random Forest Models. Even though extended NLCD data are available, our model is unique as it is trained on high elevation dense coniferous stands and does not classify wester pinyon (Pinus edulis) or Utah juniper (Juniperus osteosperma) shrub trees as “coniferous”. We apply these models, implemented in Google Earth Engine, to the nearly 40-year Landsat dataset to stochastically classify coniferous forest extent to support trend analysis with uncertainty. Model accuracy for most years is better than 94%, comparable to published NLCD accuracy, though several years had significantly worse results. Coniferous area standard deviations for any given year ranged from 0.379% to 1.17% for 100 realizations. A linear fit from 1985 to 2024 shows an increase of 65% in coniferous coverage over 38 years, though there is variation around the trend. The method can be adapted for other specialized land cover categories and sensors, facilitating long-term environmental monitoring and management while providing uncertainty estimates. The findings support ongoing research forest management impacts on snowpack and water infiltration, as increased coniferous coverage of dense fir and spruce increases interception and sublimation, decreasing infiltration and runoff. NLCD data cannot easily be used for this work in the west, as the pinyon (Pinus edulis) and juniper (Juniperus osteosperma) forests are classified as coniferous, but have much lower impact on interception and sublimation. Full article

With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments present challenges such as dynamic three-dimensional topologies, highly restrictive energy constraints, and extremely low latency demands, which substantially degrade the efficiency of conventional routing protocols. To this end, this work presents a Q-learning-enhanced ad hoc on-demand distance vector (QL-AODV). This intelligent routing protocol uses reinforcement learning within the AODV protocol to support adaptive, data-driven route selection in highly dynamic aerial networks. QL-AODV offers four novelties, including a multipath route set collection methodology that retains up to ten candidate routes for each destination using an extended route reply (RREP) waiting mechanism, a more detailed RREP message format with cumulative node buffer usage, enabling informed decision-making, a normalized 3D state space model recording hop count, average buffer occupancy, and peak buffer saturation, optimized to adhere to aerial network dynamics, and a light-weighted distributed Q-learning approach at the source node that uses an ε-greedy policy to balance exploration and exploitation. Large-scale simulations conducted with NS-3.34 for various node densities and mobility conditions confirm the better performance of QL-AODV compared to conventional AODV. In high-mobility environments, QL-AODV offers up to 9.8% improvement in packet delivery ratio and up to 12.1% increase in throughput, while remaining persistently scalable for various network sizes. The results prove that QL-AODV is a reliable, scalable, and intelligent routing method for next-generation AAV networks that will operate in intensive environments that are expected for 6G. Full article

In the coming decades, anticipated population growth is projected to escalate the demand for essential resources such as NaCl, KCl, and LiCl, which are critical for human consumption, agriculture, and battery production. A substantial proportion of these salts is produced from brines using solar evaporation ponds. This article presents a one-dimensional surrogate mathematical model of an ideal solar evaporation pond working at a steady state. The ideal pond considers only water evaporation, with a uniform evaporation rate per unit area. The model’s equation, or the ideal solar evaporation law, allows calculating the ion concentration profile in an ideal pond just given the feed and discharge concentrations. The validation of the law was conducted with industrial data collected in the year 2023 in a lithium recovery plant throughout 15 ponds in series at the Salar de Atacama, Chile. The results verified that the model could accurately predict the monthly concentration profiles (R² in the range 0.9646 to 0.9864) if lithium does not precipitate in the pond. The model provides accurate values of pond inventories and area requirements for designing stages. The model’s relevance extends beyond the lithium industry to encompass any solar evaporation processes for salt recovery or solution concentration. Full article

Polymer-Dispersed Liquid Crystal (PDLC) films offer a promising actuation method for dynamically controlling natural light, particularly in applications like smart greenhouses that require optimized Photosynthetically Active Radiation (PAR). Building upon previous work that established a control-oriented model and validated it under laboratory conditions, this study presents significant extensions. Key novel contributions include (1) the design and implementation of a Mini Greenhouse (MGH) test rig featuring PDLC films angled at 45° to accommodate typical sun angles; (2) extensive in situ validation of the previously developed Proportional–Integral (PI) control scheme under real-world environmental conditions, including varying natural sunlight, cloud cover, rain, and snow over several weeks; (3) analysis of system performance at different PAR setpoints (4 PAR and 10.5 PAR) under these conditions; (4) characterization of the system’s controllable PAR range and transmittance under natural light; (5) calculation of a light reduction ratio attributable to the MGH structure for accurate disturbance modeling; and (6) validation of an extended simulation model using the collected in situ data. The results demonstrate the system’s capability to effectively track setpoints and reject disturbances under dynamic natural light, confirming the robustness of the PDLC control approach. The validated simulation provides a reliable tool for predicting performance and optimizing control strategies for energy-efficient smart greenhouse applications. This work significantly advances the practical assessment of PDLC actuators for agricultural light management beyond laboratory settings. Full article

This work develops an inverse data envelopment analysis (Inverse DEA) framework for portfolio optimization, treating return as a desirable output and volatility as an undesirable output. Using 20 industry-level portfolios from the Taiwan Stock Exchange (1365 stocks; FY-2020), we first evaluate efficiency with a directional-distance DEA model and identify 7 inefficient industries. We then formulate an Inverse DEA model that holds inputs and desirable outputs fixed and estimates the maximum feasible reduction in volatility. Estimated reductions range from 0.000827 to 0.007610, and substituting these targets into the base model drives each portfolio’s inefficiency score to zero $(\varphi =0)$, thereby making them efficient. To test robustness, we extend the analysis to a calm pre-crisis year (2019) and a recovery year (2021), which confirm that inefficiency and volatility-reduction targets behave logically across regimes, smaller cuts in stable markets, larger cuts in stressed conditions, and intermediate adjustments during recovery. We interpret these targets as theoretical envelopes that inform risk-reduction priorities rather than investable guarantees. The approach adds a forward-planning layer to DEA-based performance evaluation and provides portfolio managers with quantitative, regime-sensitive volatility-reduction targets at the industry level. Full article

Understanding how atmospheric pollutants interact with soil pollution is essential for assessing long-term environmental and human health risks. This study compares concentrations of polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) in PM₁₀ and surface soil near air quality monitoring stations in Zagreb, Croatia. While previous work identified primary emission sources affecting PM₁₀ composition in the area, this study extends the analysis to investigate potential pollutant transfer and accumulation in soils. Multivariate statistical tools, including correlation analysis and principal component analysis (PCA), were employed to gain a deeper understanding of the sources and behavior of pollutants. Results reveal significant correlations between air and soil concentrations for several PTEs and PAHs, particularly when air pollutant data are averaged over extended periods (up to 6 months), indicating cumulative deposition effects. Σ11PAH concentrations in soils ranged from 1.2 to 524 µg/g, while mean BaP in PM₁₀ was 2.2 ng/m³ at traffic-affected stations. Strong positive air–soil correlations were found for Pb and Cu, whereas PAH associations strengthened at longer averaging windows (3–6 months), especially at 10 cm depth. Seasonal variations were observed, with stronger associations in autumn, reflecting intensified emissions and atmospheric conditions that facilitate pollutant transfer. PCA identified similar pollutant groupings in both air and soil matrices, suggesting familiar sources such as traffic emissions, industrial activities, and residential heating. The integrated PCA approach, which jointly analyzed air and soil pollutants, showed coherent behaviour for heavier PAHs and several PTEs (e.g., Pb, Cu), as well as divergence in more volatile or mobile species (e.g., Flu, Zn). Spatial differences among monitoring sites show localized influences on pollutant accumulation. Furthermore, this work demonstrates the value of coordinated air–soil monitoring in urban environments and provides an understanding of pollutant distributions across different components of the environment. Full article

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

This study examines the design and implementation of a sensor developed to measure total pressure in supersonic flow conditions using nitrogen as the working fluid. Using a combination of absolute and differential pressure sensors, the total pressure distribution downstream of a nozzle—where normal shock waves are generated—was characterized across a range of low-pressure regimes. The experimental results were employed to validate and calibrate computational fluid dynamics (CFD) models, particularly within pressure ranges approaching the limits of continuum mechanics. The validated analyses enabled a more detailed examination of shock-wave behavior under near-continuum conditions, with direct relevance to the operational environment of differentially pumped chambers in Environmental Scanning Electron Microscopy (ESEM). Furthermore, an entropy increase across the normal shock wave at low pressures was quantified, attributed to the extended molecular mean free path and local deviations from thermodynamic equilibrium. Full article

This work presents the development of carbon nanotubes functionalised with carboxylic acid groups (CNT-COOH) as an oxygen-sensitive electrochemical platform for parallel multi-analyte enzymatic biosensing. The platform was constructed by depositing carboxylic-acid-functionalised single-walled carbon nanotubes covalently onto nanostructured gold electrodes modified with a self-assembled monolayer of 4-aminothiophenol. Atomic force microscopy characterization revealed that the nanotubes attached via their ends to the surface and had a predominantly horizontal orientation. Glucose oxidase, lactate oxidase, glutamate oxidase, and tyrosinase were immobilised onto the electrodes to create selective biosensor for lactate, glucose, glutamate, and dopamine, respectively. A key finding is that incorporating catalase significantly extends the linear detection range for analytes by mitigating the accumulation of hydrogen peroxide. The resulting multifunctional biosensor demonstrated its capability for the simultaneous and independent measurement of glucose, lactate as the key bioanalytes under uniform conditions in blood plasma samples, highlighting its potential for applications in health and food technologies. Full article

This study explores the anticancer, antioxidant, and phytochemical activities of Laurus nobilis L. ethanolic leaf extract. The extract demonstrated selective cytotoxicity against four human cancer cell lines, showing strong cytotoxic effect against ovarian (ES2), head and neck (SAS), and colorectal (HT-29) cancer cells, with IC₅₀ values ranging from 3.8 ± 0.3 to 4.4 ± 0.6 µg/mL. Notably, it exhibited only moderate inhibition of the MDA-MB-231 breast cancer cell line (IC₅₀ = 18.5 ± 0.8 µg/mL), possibly reflecting intrinsic differences in cell line sensitivity. Importantly, the extract showed low toxicity toward normal human fibroblasts (HDF), with an IC₅₀ value exceeding 100 µg/mL, indicating a favorable selectivity profile. The flow cytometry analysis showed that the extract caused cell death and stopped the cell cycle in both SAS and ES2 cancer cell lines. In SAS cells, extract treatment significantly increased apoptotic cells (21.1% ± 0.3%) compared to the control (6.3% ± 0.4%), along with G2 phase accumulation, indicating G2 arrest. Similarly, in ES2 cells, apoptosis increased (16.2% ± 1.3% vs. control 8.1% ± 1.0%), and a significant cell accumulation in the S phase was observed, suggesting disruption of cell cycle progression. Antioxidant screenings showed impressive dose-dependent DPPH radical scavenging activity (25–2000 µg/mL), although less potent than ascorbic acid (2.6 µg/mL). UPLC-QTOF/MS phytochemical analysis revealed various phenolic constituents, such as flavonoids and phenolic acids, and an inferred association with the recorded bioactivities. This preliminary work indicates that L. nobilis extracts may act as natural anticancer and antioxidant agents; however, it was limited to in vitro testing with non-standardized samples, underscoring the need for further research to validate and extend these findings for future applications. Full article

Symmetry is a fundamental principle in mathematics, physics, and biology, where it governs structure and invariance. Classical symmetry analysis focuses on exact group-theoretic descriptions, but rarely addresses how robust a symmetric configuration is to perturbations. In this work, we introduce a probabilistic framework for quantifying the stability of finite point-set symmetries under random deletions. Specifically, given a finite set of points with a prescribed nontrivial symmetry group, we define the probability $P_N$ that removing N points reduces the symmetry to the trivial group $C_1$. The complementary quantity $S_N=1-P_N$ serves as a measure of symmetry stability, providing a robustness profile of the configuration. We calculate $S_N$ explicitly for representative families of symmetric point sets, including linear arrays, polygons, polyhedra, directed necklace of points, and crystallographic unit cells. Our results demonstrate unexpected behaviors: the regular hexagon loses symmetry with a probability of 0.6 under the removal of three vertices, while cubes and tetrahedra exhibit the maximal robustness ($S_N=1$) for all admissible N. We further introduce a Shannon entropy of symmetry stability, which quantifies the overall uncertainty of symmetry breaking across all deletion sizes. This framework extends classical symmetry studies by incorporating randomness, linking group theory with probabilistic combinatorics, and suggesting applications ranging from crystallography to defect tolerance in physical systems. Full article

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article

The integration of a pixelated Fabry–Pérot filter array onto the image sensor enables on-chip snapshot multispectral imaging, significantly reducing the size and weight of conventional spectral imaging equipment. However, a traditional Fabry–Pérot cavity, based on metallic or dielectric layers, exhibits a narrow bandwidth, which restricts their utility in broader applications. In this work, we propose novel Fabry–Pérot filter structures that employ dielectric thin films for phase modulation, enabling single-peak filtering across a broad operational wavelength range from 400 nm to 1100 nm. The proposed structures are easy to fabricate and compatible with complementary metal-oxide-semiconductor (CMOS) image sensors. Moreover, the structures show low sensitivity to oblique incident angles of up to 30° with minimal wavelength shifts. This advanced Fabry–Pérot filter design provides a promising pathway for expanding the operational wavelength of snapshot spectral imaging systems, thereby potentially extending their application across numerous related fields. Full article