Search Results (10,784)

Generalized Sinusoidal Frequency Modulation (GSFM) signals can enhance Continuous Active Sonar (CAS) performance by providing high sub-signal processing gain while achieving high target update rates. However, conventional processing methods for GSFM often exhibit high sidelobe levels arising from the waveform’s autocorrelation which degrade detection performance, especially in severe multipath environments. To address this issue, a Mismatched-Channel Filtering (MMCF) method for GSFM in CAS is proposed to focus multipath energy while suppressing sidelobe levels. Adopting the sub-pulse processing scheme, we incorporate the orthogonality of GSFM sub-signals (optimized via a genetic algorithm) and sparse channel estimates into the MMCF design for each sub-signal. The design is formulated as a Quadratically Constrained Quadratic Program (QCQP) and solved iteratively using the Alternating Direction Method of Multipliers (ADMM) for long-duration signal processing in CAS. Numerical simulations demonstrate that, compared with the matched filtering and matched channel filtering methods, the proposed MMCF method effectively suppresses sidelobe levels by approximately 20 dB and produces a Dirack-like main-lobe peak, while efficiently focusing multipath energy. The method’s effectiveness is further validated using experimental data from a lake trial. Therefore, this algorithm has distinct advantages for signal processing in multipath environments. Full article

The soundscape serves as a critical determinant of the quality of urban wetland parks. This study employs a mixed-methods approach to comprehensively evaluate wetland soundscapes. First, field investigations combining sound level measurements and questionnaire surveys were conducted in Aixi Lake Wetland Park to analyze the spatiotemporal characteristics of the soundscape. Second, laboratory-based physiological tracking (using wearable sensors) and cognitive tests (Sustained Attention to Response Task, SART) were utilized to experimentally quantify the restorative benefits of typical soundscapes. The findings reveal that: (1) sound level indicators and sound harmonious degree in urban wetland parks exhibit significant spatiotemporal characteristics and distributional variations; (2) a marked competitive effect among biological, geophysical, and human activity sounds is observed in their spatial distribution; sound harmonious degree demonstrates significant spatial autocorrelation in both global and local models; (3) different sound sources possess varying restorative potentials, with bird song showing the highest restorative effect; the SHDs of biological and geophony, along with L_Aeq, are key factors affecting PRSS; (4) a positive correlation exists between L_Aeq and the PRSS up to 56.4 dB, beyond which PRSS declines with increasing L_Aeq; (5) at the physiological level, short-term exposure to urban wetland park soundscapes can rapidly alleviate stress, with the most pronounced restorative effects occurring within the first 60 s; and (6) in terms of attention, soundscape stimulation reduces SART response times and improves response speed, while bird song from treetops and musical sounds further decrease response errors. Full article

The literature indicates that the qubit requirements for factoring RSA-2048 remain on the order of 1 million, under commonly assumed architectures and error-correction models, leaving a substantial gap between current resource estimates and near-term practical feasibility. Reducing this requirement to the low-thousand-qubit regime therefore remains an important open research objective. This work proposes a hybrid classical–quantum algorithm that uses a classical modular exponentiation subroutine with a Quantum Number Theoretic Transform (QNTT) circuit to increase the speed and reduce the required quantum resources relative to Shor’s algorithm for integer factorization, which underpins cryptographic systems like RSA and ECC. We evaluate multiple coprime numbers, the result of multiplication of two primes, in both simulation and real quantum hardware, using IBM’s reference Shor implementation as the baseline. Because Shor and proposed Jesse–Victor–Gharabaghi (JVG) use different register sizes for the same coprime N, the reported gate/depth reductions should be interpreted as end-to-end quantum-resource budgets for factoring the same N, rather than a per-qubit or transform-only efficiency claim. In simulation, the JVG algorithm achieved substantial practical reductions in computational resources, decreasing runtime from 174.1 s to 5.4 s, memory usage from 12.5 GB to 0.27 GB, and quantum gate counts by approximately 99%. On quantum hardware, JVG reduced the required runtime from 67.8 s to 2 s, and the quantum gate counts by over 98%. We showed that the proposed algorithm can address the relevant RSA-1024 case scenario, establishing that this method can provide validation for large-scale situations. Furthermore, extrapolation to RSA-2048 indicates that the JVG algorithm significantly outperforms Shor’s approach, requiring a projected quantum runtime of 29 h for ten thousand runs for factorization under identical scaling assumptions. Overall, these results support JVG as a more hardware-compatible and robust noise-tolerant substitute for Shor’s framework, offering a viable research direction toward practical quantum integer factorization on near-term Noisy Intermediate-Scale Quantum (NISQ) devices. Full article

Phosphogypsum (PG), a major by-product of the phosphate industry, has potential for improving acidic and nutrient-poor red soils, yet its agronomic benefits and heavy metal risks require systematic evaluation. A field experiment was conducted with five treatments, CK (soil only), GT (50% modified phosphogypsum, MPG), TT (40% MPG), ZT (50% phosphorite tailings), and DT (25% MPG + 25% lake sediment), to assess their effects on soil properties, enzyme activities, peanut growth, yield, quality, and heavy metal accumulation. All amendments improved soil structure, moisture retention, nutrient availability, and enzymatic activities. Peanut pod and kernel yields increased under all treatments, with DT achieving the greatest improvements (29.89% and 40.88%, respectively), whereas ZT showed the weakest response (1.91% and 6.26%). DT also achieved the highest soil quality index, and performed best in both yield improvement and root development. Although Cd accumulation increased under DT, heavy metal concentrations in peanut kernels remained below national food safety limits. Overall, DT was identified as the most effective amendment for enhancing red soil fertility and peanut productivity, while long-term monitoring of Cd bioavailability is recommended to ensure sustainable and safe application. Full article

Wildfire impact on boreal watersheds was assessed across Alberta’s Regional Aquatics Monitoring Program (RAMP) domain by integrating multidecadal river, lake, and sediment physical–chemical data with historical wildfire perimeters, polycyclic aromatic hydrocarbon (PAH) indicators, continuous multiparameter sonde records, and pre-/post-fire hydrologic simulations. Site classification, distinguishing reference, industrial, wildfire, and combined influences, was used to enable spatial and temporal comparisons before, during, and after fires. Our synthesis indicated that wildfire acts as an important disturbance that alters watershed connectivity and transport pathways, resulting in shifts in water quality and quantity in surface waters and longer-term adjustments retained in sediments. The interpretation of chemical signatures, including PAHs, was complicated by overlap between areas with wildfire and industrial activities, highlighting cumulative effects and the importance of spatio–temporal context when assessing and quantifying source contributions for long-term resource sustainability. Hydrologic alteration emerged as the dominant downstream wildfire effect, emphasizing the need for long-term continuous monitoring of fire-responsive indicators, in addition to improved assessment of subsurface pathways in wildfire-prone boreal systems. Full article

Groundwater quantification is essential for sustainable water resources management, yet it is often hampered by limited data availability and difficulties in measuring spring discharges. This study investigates three carbonate aquifers in Central Italy’s Abruzzo region: the Genzana–Greco, Morrone, and Marsicano mountains. The aim is to resolve uncertainties in spring attribution, and groundwater flow patterns using isotopic analyses combined with field surveys. The Genzana–Greco aquifer was examined to clarify the sources of the Acquachiara spring and the previously unreported Germina spring, assessing whether recharge occurs locally or from the carbonate massif. In this case, the results indicate that the Germina, together with a similar known spring of Capolaia, share a common recharge sector, while the Acquachiara spring is mainly fed by higher-elevation carbonate areas, excluding significant contributions from local alluvial deposits. In the Morrone mountain aquifer, discharge gains along the Pescara River through the Gole di Popoli were quantified, and spring isotopic compositions were compared to the main basal spring Giardino to better define groundwater contributions. In this case study, the stable isotopes and tritium data confirm recharge from the central–southern massif and support the identification of basal springs and Pescara River gains as primary discharge points, with minimal influence from surface water. For the Marsicano mountain aquifer, the role of Lake Scanno in feeding the Villalago springs was investigated through isotopic analysis of inflows, downstream springs, and basal aquifer discharge points to constrain the hydrogeological water budget. The results highlight Lake Scanno’s role in the recharge of Villalago springs and delineate the Cavuto group as a major discharge system receiving inputs from central and northern sectors of the massif. Overall, the integration of isotopic tracers with hydrological measurements allowed a more precise characterization of aquifer recharge areas, Mean Residence Times, and groundwater flow paths, improving the understanding of regional water resources in a complex carbonate setting. Full article

This research presents a comprehensive framework for optimizing Electric Vehicle (EV) charging infrastructure along the Lake Michigan circuit (LMC) in Michigan to support ecotourism, considering both slow charging at destinations and fast charging along the corridor. The framework identifies the optimum location and number of Level 2 chargers and Direct Current Fast Chargers (DCFC), using heuristic algorithms. The study evaluates infrastructure planning based on four key objectives: (1) minimizing overall charging infrastructure costs, (2) reducing grid network upgrade costs, (3) providing an acceptable level of service to long-distance travelers using DCFCs by minimizing queuing delays and deviations from their intended routes, and (4) minimizing unserved charging demand at Level 2 chargers, which reduces redirection to DCFC and consequently mitigates battery degradation. The integration of Level 2 and DCFC networks facilitates strategic investment by effectively managing charging demand, allowing unserved Level 2 demand to be accommodated at DCFC stations while adhering to budgetary constraints. The results show that increasing the budget from $15 to $20 million reduces user inconvenience by 47%, while a further increase to $25 million yields an additional 18% reduction. Additionally, increasing users’ value of time from $13 to $36 per hour results in a 50% reduction in average queuing time. Full article

In response to the complex erosion environment caused by periodic water level fluctuations, dry–wet cycles, and long-term water flow scouring on the Yangtze River bank, three typical soil-fixing and bank-protecting plants, Cynodon dactylon, Carex breviculmis, and Digitaria sanguinalis, which can adapt to both aquatic and terrestrial conditions, were selected for planting experiments. Tests on root–soil composite shear strength, disintegration, and water flow scouring were conducted to investigate the effects of different bank-protecting plants on bank stabilization. The results show that: 1. The root systems of the three plants significantly enhance the soil shear strength at various soil depths, but the reinforcing effect decreases with increasing soil depth. The cohesion strength of the root–soil composites ranks as Carex breviculmis > Digitaria sanguinalis > Cynodon dactylon, with maximum increases of 54.83 kPa, 20.66 kPa, and 6.5 kPa, respectively, equivalent to 3.16, 1.82, and 1.26 times that of bare soil. 2. Under dry–wet cycling, the water stability of the root–soil composites is significantly higher than that of bare soil. The disintegration residual rate of Cynodon dactylon and Digitaria sanguinalis decreased from 81.76% to 38.23% and from 80.18% to 34.34%, respectively, whereas Carex breviculmis showed only a slight decrease from 80.41% to 75.1%. Carex breviculmis exhibits the strongest stability and is least affected by dry–wet cycles, while the water stability of Cynodon dactylon and Digitaria sanguinalis declines noticeably with increasing cycle numbers. The plants’ ability to improve soil water stability ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis. 3. The enhancement of bank erosion resistance is mainly attributed to the formation of a root-reinforced network, which strengthens the soil through root–soil interlocking and anchorage, thereby increasing resistance to flow-induced shear stress and reducing particle detachment under hydraulic action. The bank erosion resistance index ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis, and decreasing with increasing runoff velocity. Compared to bare soil slopes, the maximum enhancement effects on bank erosion resistance are 75.1%, 63.3%, and 54.2% respectively. Full article

Magneto-fluorescent carbon quantum dots (CQDs) promise compact, dual-readout nanomaterials; however, achieving pronounced photoluminescence alongside magnetic functionality in a simple, scalable formulation remains difficult, especially for emerging doped CQDs. Here, we report Fe-doped carbon quantum dots (Fe-CQDs) as an emerging quantum-dot platform that integrates fluorescence with magnetic-resonance (MR) relaxometry within a single ultrasmall, carbonaceous nanostructure. To enable this, Fe-CQDs are prepared through a straightforward two-step, low-temperature route that uses a magnetic deep eutectic solvent precursor followed by mild carbonization in air at atmospheric pressure. Under UV excitation, the Fe-CQDs display bright blue emission centered at 439 nm, and their optical behavior is characterized by UV-Vis absorption, photoluminescence spectroscopy, and fluorescence microscopy. Meanwhile, dynamic light scattering indicates a narrowly distributed nanoscale hydrodynamic diameter, and X-ray diffraction together with FT-IR supports a carbonaceous framework enriched with oxygenated surface functionalities, consistent with aqueous dispersibility and environmentally responsive photophysics in water, while XPS supports Fe incorporation in an Fe(III)-dominated chemical environment. Importantly, Fe incorporation enables intrinsic MR relaxometric readout, establishing an intrinsic fluorescence/MR dual modality. As a proof-of-concept, Fe-CQDs were tested with a representative per- and polyfluoroalkyl substance (PFAS), showing parallel fluorescence and MR response trends at ppm levels in natural water matrices from Millerton Lake with Stern–Volmer analysis and a NaCl-based ionic strength control. Overall, these results position Fe-CQDs as a versatile magneto-fluorescent nanomaterial for dual-readout screening workflows and motivate future surface engineering and dopant tuning to improve selectivity and expand toward multi-modal readouts. Full article

Arid Central Asia (ACA) is a major source of atmospheric dust in the Northern Hemisphere; however, the evolutionary models and driving mechanisms of Holocene aeolian activity in this region remain debated. Based on 13 reliable AMS ¹⁴C dates from the Sayram Lake SLM2009 sediment core, this study reconstructs the Holocene sequence in aeolian activity through end-member modeling analysis (EMMA). It evaluates its relationship with regional atmospheric circulation. Four end-members were identified from base to top: EM1, with a modal grain size of 7.58 μm, represents low-energy suspension deposition; EM2 (26.30 μm) reflects lacustrine hydrodynamic processes; while EM3 (52.48 μm) and EM4 (416.86 μm) serve as proxies for regional aeolian activity. The results indicate that aeolian activity was relatively strong during the early Holocene (reaching peaks at 11.7–11.2 and 9.2–8.1 cal ka BP), significantly intensified during the mid-Holocene (7.3–5.3 cal ka BP), and gradually weakened in the late Holocene (since 4.0 cal ka BP). Comparison of the aeolian record from Lake Sayram with Greenland ice cores, North Atlantic ice-rafted debris events, and the GISP2 K⁺ record indicates that variations in aeolian activity in arid Central Asia are closely linked to the Northern Hemisphere climate system. We propose that these variations were primarily modulated by large-scale atmospheric circulation, driven by the synergistic interaction between the Siberian High and the mid-latitude westerlies. Full article

Coal-mining subsidence lakes are an expanding artificial wetland type in China, yet the relationships between waterbird diversity components and water-quality and landscape gradients remain unclear. We conducted monthly point-count surveys from January to December 2025 at 28 subsidence lakes in Huaibei, Anhui, China (lake area: 0.01–1.05 km²), and used generalized linear mixed models (GLMMs) to test relationships between waterbird diversity and water quality, lake morphology, landscape composition, and anthropogenic disturbance. Associations differed among diversity components. Species richness was positively associated with surrounding cropland and built-up area, whereas total abundance was positively associated with total nitrogen but negatively associated with total phosphorus, indicating that nutrient-related associations were not uniform across water-quality variables. Both Shannon and Margalef diversity were positively associated with surrounding cropland and also showed positive, context-dependent associations with built-up area. These findings suggest that different components of waterbird diversity were associated with different environmental gradients, with landscape context more strongly associated with richness and diversity indices, whereas water-quality gradients were more strongly associated with abundance. Conserving waterbird diversity in subsidence lakes therefore requires attention not only to nutrient conditions within lakes, but also to the surrounding wetland–farmland landscape context. Full article

This study examines the design and pedagogical evaluation of Extended Reality (XR) applications, with a primary focus on location-based Augmented Reality (AR). The XR applications were implemented within an environmental education program delivered by the Education Center for the Environment and Sustainability (E.S.E.C.) of Kastoria, aiming to enhance students’ understanding of lake ecosystems and environmental awareness through immersive, situated learning experiences. The development followed the ADDIE instructional design framework and was grounded in principles of experiential and situated learning. The educational intervention was conducted in an authentic field setting along the shoreline of Lake Kastoria and combined location-based AR activities with complementary immersive VR experiences. Evaluation data were collected through a structured questionnaire administered to 271 primary and secondary school students, employing XR-relevant constructs including Challenge/Satisfaction/Enjoyment, Ease of Use, Usefulness/Knowledge, Experiential and Situated Learning, Interaction/Collaboration, and Intention to Reuse. In addition, accompanying teachers provided supplementary qualitative feedback to support the interpretation of the findings under authentic field conditions. Descriptive statistical analysis indicated consistently high scores across all constructs (M = 3.27–4.40, SD = 0.41–0.64). Pearson correlation analysis revealed strong associations between Experiential/Situated Learning and Usefulness/Knowledge (r = 0.737), Experiential/Situated Learning and Challenge/Satisfaction/Enjoyment (r = 0.642), Intention to Reuse and Challenge/Satisfaction/Enjoyment (r = 0.635), as well as Usefulness/Knowledge and Challenge/Satisfaction/Enjoyment (r = 0.619). Multiple regression analyses further supported key relationships, including Usefulness/Knowledge as a predictor of Experiential/Situated Learning (β = 0.57, p < 0.001), Experiential/Situated Learning as a predictor of Challenge/Satisfaction/Enjoyment (β = 0.47, p < 0.001), and Interaction/Collaboration as a predictor of Intention to Reuse (β = 0.31, p < 0.001). Intention to reuse was mainly associated with interaction and collaboration, enjoyment and motivation, perceived usefulness/knowledge, and ease of use. Overall, the findings indicate that XR-supported outdoor learning is positively associated with key experiential, emotional, social, and perceived learning dimensions when embedded within a coherent pedagogical framework. Full article

Lake ice monitoring is critical for assessing climate change, but in-situ observations are often limited. Sentinel-1 Synthetic Aperture Radar (SAR) data is a strong method for ice detection because it is not restricted by cloud cover and it is readily available. However, SAR-based classification can be affected by atmospheric and surface-related noise. This study examines the impact of noise on machine learning-based lake ice detection over Lake Śniardwy, Poland, using Sentinel-1 Vertical-Vertical (VV) and Vertical-Horizontal (VH) backscatter data. Binary logistic regression models were trained on scenes with strong class separability between ice and water and then validated on separate low- and high-noise datasets. The models achieved high accuracy under low-noise scenes, reaching up to 96.9%, but performed poorly on high-noise scenes. The results show that wind-related surface roughness and associated atmospheric conditions can significantly reduce classification reliability. Comparison with backscatter from a nearby coniferous forest confirmed that the main disturbances were concentrated over the lake surface. The study highlights the importance of careful scene selection and noise assessment in SAR-based lake ice classification. Full article

Microplastic (MP) contamination in freshwater systems has emerged as a growing environmental concern. This study investigated the occurrence and seasonal variability of MPs in surface water, the water column, and sediments at selected sites in Lake Alhajuela, Panama. Lake Alhajuela is an artificial reservoir that supplies water to the Panama Canal lock system and to the cities of Panama and Colón, serving more than 50% of the country’s population. MPs were isolated using two digestion protocols followed by density separation, and fragments and films larger than 1 mm were chemically characterized using FTIR–ATR spectroscopy. Mean MP concentrations were 759 ± 536 MPs L⁻¹ in surface water, 328 ± 140 MPs L⁻¹ in the water column, and 109 ± 87 MPs g⁻¹ in sediments. Statistical analyses revealed no significant differences among sampling sites; however, significant seasonal differences were observed (p < 0.01). Smaller MPs (63–249 µm) were more abundant compared to larger MPs (>250 µm). Fragments and fibers were the most predominant type of MP reported. Our results confirm the presence of MPs in the surface and water column, as well as sediments of the Alhajuela Lake. Further studies are needed to elucidate the fate, sources, transport, and distribution of MPs across Lago Alhajuela as well as to assess the lake’s potential contribution of MPs to Gatun Lake and the Panama Canal system. Full article

The stabilization of soft, organic-rich soils with cement is often hindered by retarded hydration and poor long-term performance under cyclic loads. While nano-silica or sand are known modifiers, their individual efficacy in high-organic environments remains limited, and a systematic comparison of their composite effect across different soil types is lacking. This study investigates the synergistic enhancement of cement-stabilized soils using a combined nano-SiO₂ and sand composite, comparing its effectiveness in high-organic soft soil and low-organic clay. Laboratory tests, including unconfined compressive strength (UCS), cyclic loading, scanning electron microscopy (SEM), and X-ray diffraction (XRD), were conducted. Results showed a stark contrast in 28-day UCS between unmodified soft soil cement (0.13 MPa) and clay cement (1.04 MPa). The optimal composite of 3.5% nano-SiO₂ and 40% sand increased the 28-day UCS to 1.39 MPa for soft soil (a 969% improvement) and 5.51 MPa for clay (a 430% improvement), respectively. Notably, under a cyclic stress ratio (CSR) of 0.7~0.8, unmodified specimens failed after fewer than 120 load cycles, whereas the composite-modified soils withstood 20,000 cycles without failure, demonstrating exceptional fatigue resistance independent of static strength gain. Microstructural analysis revealed that the composite effectively promoted the formation of cementitious hydration products, counteracting the inhibitory effect of organic matter. This research demonstrates that the nano-silica sand composite provides a superior and more broadly applicable improvement for cement-stabilized soils across the tested organic content range (3.3–7.7% LOI) compared to single-additive approaches, significantly enhancing both mechanical strength and long-term durability. Full article