Search Results (337)

Slowness extraction via Slowness Time Coherence (STC) serves as a fundamental technique for formation evaluation in oil and gas geophysics. Conventional amplitude-dependent peak selection methods often exhibit limitations in complex logging scenarios, including weak wave arrivals, high noise floors, and spurious local maxima. To address these challenges, this paper proposes Neighborhood Width Transform (NWT), an unsupervised data-driven mathematical framework that distinguishes genuine peaks from noise by quantifying local neighborhood structural stability rather than relying on amplitude magnitude. The core of NWT lies in a bilateral neighborhood width metric and a minimum-pooling fusion strategy, which suppresses narrow pseudo-peaks effectively. Experimental validation demonstrates that the proposed method outperforms seven representative peak-selection baseline methods (CWT Ridge Analysis, Gaussian Mixture Fitting, AMPD, SG Derivative Crossing, NMS, Random Forest, and 1D-Unet) in terms of detection reliability and accuracy on the tested challenging logging datasets. The proposed method provides an interpretable, high-throughput mathematical solution for automated geophysical signal processing. Full article

The extraordinary genetic diversity of human immunodeficiency virus type 1 (HIV-1), driven by high mutation and recombination rates, poses significant challenges for diagnostics, therapy, and vaccine development. While variable regions enable immune escape, hyperconserved regions are critical for viral function and represent promising targets for novel therapeutic interventions. This study aimed to develop and validate a bioinformatic algorithm for quantitative assessment of sequence conservation and automated identification of functionally significant conserved regions across all major HIV-1 proteins. A total of 1119 full-length HIV-1 genome sequences representing major subtypes (A1, A2, A6, B, C, D, F1, F2, G, H, J, K) were analyzed. Normalized Shannon entropy (S-index) was calculated for each alignment column. Statistical thresholds for conserved regions were established using 95% confidence intervals derived from bootstrap resampling. Two complementary algorithms, clustering and local maxima detection, were applied to identify conserved regions, which were subsequently mapped to known functional domains based on literature data. Protein conservation varied markedly, with S_m values ranging from 0.784 (Vpu) to 0.920 (Pol). Gag, Pol, and Vpr demonstrated the highest overall conservation, while Env, Rev, Tat, and Vpu exhibited pronounced variability interspersed with conserved domains. In total, 25 conserved regions in Gag, 49 in Pol, 28 in Env, and 6–4 regions in accessory proteins (Vif, Vpr, Rev, Tat, Nef, Vpu) were identified. These regions corresponded to critical functional elements including enzyme catalytic centers, zinc fingers, receptor-binding sites, protein interaction interfaces, and membrane-anchoring domains. The developed computational framework enables statistically grounded identification of evolutionarily constrained regions across analyzed HIV-1 subtypes. The identified conserved regions represent candidate sites for further investigation and may inform downstream studies focused on antiviral target prioritization, immunogen design, and diagnostic assay development. However, their translational applicability requires additional analytical, structural, and experimental validation. Full article

Based on sodium lidar observations obtained at the Haikou station (20° N, 110.2° E) of the Chinese Meridian Project during 2012–2024, this study systematically investigates the climatology of sporadic sodium layers (SSLs) at low latitudes and their relationship with ionospheric sporadic E (Es) layers, and it further analyzes their morphological features and evolution through a case study. The results show that the occurrence rate of SSLs over Hainan exhibits significant interannual variability. In terms of the monthly occurrence rate for individual years, while there is no fixed intra-annual pattern, February and October repeatedly appear as months with high occurrence rates, with February appearing in 4 of the 12 analyzed years and October in 5 of the 12 analyzed years, indicating that SSLs have a certain seasonal preference for late winter and autumn. This feature seems to be related to meteoric injection. The monthly occurrence rate of SSLs averaged over 2012–2024 further shows pronounced maxima in February, June, and October. Comparison with the climatology of Es layers over the same period reveals that the Es occurrence rate reaches its annual maximum in June, while the SSL occurrence rate also shows a local peak in June, indicating that Es layers may play an important role in SSL formation. Nevertheless, the high SSL occurrence rates in February and October indicate that other physical and chemical processes also play important modulating roles. Statistical analysis of SSL over local time indicates that SSLs mostly occur between 21:00 and 01:00 LT, with both onset and peak times concentrated in this interval, durations mostly of the order of tens of minutes, and peak heights at 94–96 km. Overall, SSLs over Hainan exhibit significant interannual variability and a weak seasonal preference, and their formation is jointly influenced by direct meteoric injection, Es-related ionospheric processes, and neutral Na chemistry. Full article

The topographic features of the seafloor can be observed clearly via high-resolution side-scan sonar imagery. However, the faithful interpretation of a sonar image depends strongly on the accuracy with which the location of the sea bottom line can be tracked within the image, and current tracking methods function poorly under high sonar signal noise or suffer from high complexity. The present work addresses this issue by applying the characteristics of simple sonar waterfall maps in conjunction with robust edge detection and multi-scale analysis based on wavelet transform modulus maxima. The proposed tracking method is demonstrated to provide superior effectiveness and accuracy in comparison with existing baseline methods based on the results of experiments conducted with a representative side-scan sonar image with and without applied speckle noise. This superiority can be attributed to the good localization characteristics and multi-scale detection features of wavelet transform analysis, which can suppress the impact of noise in the sonar image on the accurate extraction of edge information. Full article

The Mediterranean limpet (Patella caerulea, Linnaeus, 1758) is a native species in Türkiye that is not yet a major commercial species but has potential for future commercialisation, particularly given the country’s substantial mollusc export market. This study aimed to evaluate seasonal and station-level variation in trace-element burdens in P. caerulea collected from the Sinop inner harbour (southern Black Sea coast, Türkiye) and to assess the associated trace-element–related non-carcinogenic health risks under a precautionary consumption scenario. Spatial and seasonal variations in the concentrations of 10 trace elements (Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb, Hg, and total As) were analysed in specimens collected seasonally from autumn 2022 to summer 2023. Permutational multivariate analysis of variance revealed that season was the primary factor influencing trace element concentration variability, accounting for 76.9% of the total variance, followed by station (11.2%) and the season × station interaction (7.2%). All elements varied significantly among seasons (Kruskal–Wallis, p < 0.001), with maxima in autumn and minima in winter. Spatial differences were significant only for Mn, Co, Pb, Zn, and Hg, indicating localised sources. A human health risk assessment was performed for 6-year-old children, 12-year-old children, and adults. Total target hazard quotient (TTHQ) values were <1 across all groups; however, Cd was the dominant contributor, with the highest value observed in children (max TTHQ = 0.94). TTHQ followed the seasonal contamination pattern, peaking in autumn. Even under the high-consumption scenario, TTHQ for P. caerulea from the Sinop inner harbour remained below the non-carcinogenic risk threshold. The strong seasonal signal supports its use in locally focused biomonitoring, while the health-risk assessment should be limited to the analysed trace elements and associated non-carcinogenic effects. Full article

Most air pollution research in India has predominantly focused on the Indo-Gangetic Plain (IGP) owing to its high pollution levels and dense populations, leaving peninsular India comparatively undercharacterized. In contrast, South India remains underexplored because of its relatively limited long-term monitoring and more favorable meteorology. This geographical imbalance restricts a comprehensive national understanding of particulate matter (PM) dynamics. Addressing this gap, the present study delivers a multi-scale (hourly to interannual) spatiotemporal assessments of PM_2.5 across eight monitoring stations in Andhra Pradesh, a South Indian State, for the period 2020–2024. The analysis reveals pronounced seasonal variability, with persistent winter and post-monsoon maxima. Although overall concentrations are low compared to northern India, urban–industrial centers such as Visakhapatnam and Rajahmahendravaram frequently exceeded both the National Ambient Air Quality Standards (NAAQS) and World Health Organization (WHO) guidelines. Notably, Amaravati, a non-industrial and low-lying inland site, exhibited anomalously moderate PM_2.5 levels, with ~11.58% of hourly values surpassing 60 µg m⁻³. The COVID-19 lockdown period further offered a natural experiment, revealing substantial reductions (30–65%) in PM_2.5 and PM₁₀ at major urban sites while concurrent ozone enhancements (up to ~50%) at Tirupati and Rajahmundry exposed complex photochemical sensitivity under reduced NO_x conditions. Satellite-based MERRA-2 estimates corroborated inter-annual variability and the short-lived improvement in air quality. This study demonstrates that air quality dynamics in the state of Andhra Pradesh are governed by region-specific meteorological controls, episodic processes, and localized emission characteristics, necessitating expanded long-term monitoring infrastructure and improved satellite–ground calibration frameworks. Full article

Complex biological and medical systems often exhibit irregular and self-similar structures that can be effectively analyzed using fractal and multifractal frameworks. This study aims to provide a comprehensive overview of multifractal analysis as a mathematical tool for characterizing complex biomedical patterns and improving disease diagnosis. The methods discussed include the Wavelet Transform Modulus Maxima (WTMM) and box-counting techniques, which quantify local scaling behaviors and heterogeneity within medical images. A review of recent studies demonstrates that multifractal parameters have successfully differentiated between normal and pathological tissues in diseases such as cancer, cardiac disorders, and Alzheimer’s disease. This paper also examines the integration of artificial intelligence, particularly machine learning algorithms, with multifractal features to enhance diagnostic accuracy and automate image interpretation. The results indicate that this hybrid approach improves the reliability and sensitivity of early disease detection. In conclusion, multifractal analysis, when systematically applied and combined with AI, offers a promising complementary framework for advancing precision medicine and supporting clinical decision-making. Full article

Citrus genomes as storehouses of genetic information of immense commercial utility remain untapped for the improvement of fruit quality traits and other production-related stresses. With the rapid expansion of transcriptomic datasets, integrative meta-analysis has further aided in uncovering interspecies molecular mechanisms associated with fruit quality development. In this study, we performed a cross-project RNA-Seq meta-analysis, integrating multiple publicly available BioProjects encompassing diverse citrus species, viz., Citrus sinensis, C. reticulata, C. maxima, C. clementina, C. japonica, and C. papeda, known to dominate the morphogenetic evolution of the citrus industry. High-throughput RNA-Seq data were processed using various bioinformatics tools. A total of 15 interspecies comparisons identified 676 unique DEGs, enriched in pathways related to secondary juice yield and processing quality traits. We also established that domestication aided in metabolism, oxidative stress responses, phenylpropanoid and flavonoid biosynthesis, and hormone-mediated signaling. Multivariate analyses (PCA and heatmap visualization) highlighted distinct yet overlapping expression patterns across these citrus species. By combining differential expression, co-expression network analysis and QTL-GWAS integration, we identified 19 high-confidence candidate genes responsible for transcriptomic variation associated with measurable fruit quality traits. Genes such as LOC102612823 and LOC102607495, which co-localized with seed number QTLs on chromosome 1, represented strong candidates regulating reproductive development and seed formation, the traits that directly influence fruit texture and market acceptability. Genes linked to juice content QTLs, including LOC102611137 and LOC102612553 on chromosome 5, suggested their roles in metabolic regulations behind juice accumulation. These loci provided definitive breeding clues for enhancing the reshaping of citrus fruit transcriptomes while retaining key ancestral regulatory components. Full article

Extreme sea levels along the Mexican coasts pose an increasing risk to coastal infrastructure and communities, particularly under the combined influence of tropical cyclones and ongoing sea-level rise. This study analyzes tide-gauge records from the Mexican Pacific and Gulf of Mexico–Caribbean coasts to characterize the statistical behavior and seasonal modulation of extreme sea-level residuals. Astronomical tides were removed through harmonic analysis to isolate the meteorological residual associated with storm-driven processes. Extreme events were evaluated using complementary extreme-value frameworks, including Generalized Extreme Value (GEV) distributions applied to monthly maxima and a Peaks-Over-Threshold (POT) approach applied to the continuous residual series with temporal declustering and Generalized Pareto Distribution (GPD) fitting. While both approaches consistently capture regional patterns, the POT–GPD framework is adopted as the primary basis for return-level estimation due to its explicit representation of event-scale extremes. The results reveal marked regional variability. Pacific stations exhibit bounded or near-Gumbel behavior (ξ ≈ −0.30 to −0.02) and a strong seasonal concentration of extremes during the tropical cyclone season. In contrast, Gulf of Mexico–Caribbean stations display higher absolute extremes and a broader seasonal footprint, with Veracruz showing a tendency toward heavier-tailed behavior (ξ ≈ 0.13). Return levels for a 25-year return period range from approximately 0.85–0.95 m in the Pacific to about 1.7 m in Veracruz. Longer return periods (e.g., 100 years) exceed 2.2 m in Veracruz but are associated with substantial uncertainty due to record-length limitations. The analysis of ENSO variability indicates that ENSO acts primarily as a secondary modulator of background sea-level variability rather than a deterministic driver of extreme events, with the largest anomalies typically associated with tropical cyclone activity. Overall, the results demonstrate that extreme sea levels along the Mexican coasts are governed by region-specific forcing and tail behavior requiring localized extreme-value modeling strategies. The proposed framework provides a robust and reproducible baseline for coastal hazard assessment and supports the integration of sea-level rise into future risk and design analyses. Full article

Tidal-scale wave modulation is a critical yet complex process in macro-tidal estuaries. This study investigates semidiurnal wave modulations in the Changjiang River Estuary (CRE) using unique, long-term in situ observations and high-resolution ADCIRC–SWAN coupled simulations. Pronounced semidiurnal signals are identified in significant wave height (H_s), mean wave period, and wave direction. Observational results demonstrate that the modulation intensity is highest in Hangzhou Bay and the CRE mouth, decreasing gradually offshore. A key finding is that semidiurnal H_s maxima systematically coincide with peak flood currents and precede high water by approximately three hours. Long-term records confirm that this modulation persists year-round and intensifies during energetic events such as typhoons. The expression of the tidal signal depends on wave composition: wind-sea-dominated conditions exhibit stronger period modulation, whereas swell-dominated conditions favor coherent H_s modulation as kinematic tidal effects remain more apparent in the absence of strong local wind forcing. Numerical sensitivity experiments demonstrate that tidal currents are the primary driver of the observed wave modulation, while water-level effects are largely confined to shallow shoals. The results highlight that accurately reproducing the observed frequency–directional structure requires the inclusion of current-induced Doppler shifts and refraction. Beyond the classical following-current effects, the analysis suggests that the spatial deceleration of currents along the wave path acts as a kinematic trap that focuses wave action and sustains H_s intensification. This mechanism provides a physically plausible explanation for the observed phase relationship and points to the non-local nature of estuarine wave dynamics, where the wave state appears as an integrated response to cumulative current gradients along the propagation path. These findings emphasize the necessity of incorporating wave–current coupling in future coastal modeling and hazard forecasting. Full article

Traditional markets play an important role in the exchange of plant resources and the preservation of traditional food knowledge. This study documents the diversity of food plants traded in Renmin Market, located in Youjiang District, Baise City, Guangxi, China, and evaluates their cultural importance using the Cultural Food Significance Index (CFSI). Field surveys were conducted through market observations and interviews with vendors and local informants. All edible plant species were recorded, including their scientific names, vernacular names, used parts, and modes of consumption. A total of 104 food plant taxa were documented, representing a wide range of plant families and growth forms. The recorded plants were used in four main utilization categories: vegetables, spices, fruits, and beverages. Frequently used plant parts included fruits, leaves, shoots, and underground organs such as roots, rhizomes, and tubers. The CFSI values showed considerable variation in cultural importance among species, ranging from 21.6 to 1764. The highest CFSI values were recorded for Cucurbita pepo, Allium cepa, Cucurbita maxima, and Houttuynia cordata, reflecting their frequent consumption and versatility in local cuisine. Comparative analysis with previous studies in Baise City indicated that 38 species were shared among three markets, while 30 species were recorded exclusively in Renmin Market. These findings highlight the diversity of food plants available in local markets and their importance in maintaining regional culinary traditions and plant-based dietary diversity. Full article

Reliable subsurface temperature estimates are crucial for most geoenergy projects, as they directly influence the properties of both rocks and fluids. They are particularly important in geothermal energy exploration, where errors in estimating the static formation temperature (SFT) can lead to significant misinterpretations, potentially resulting in incorrect classification of the geothermal resource. Various corrections are applied to bottom-hole temperatures (BHTs), with the Horner correction being the most widely used. In addition, empirical methods have been developed to improve accuracy at the local scale. In this study, maximum temperature values (Tmax) reported for deep exploration wells in the Sava and Drava Basins were compared to both Horner-corrected temperatures (HPCTs) and those recorded during drill-stem tests (TDST). In both basins, Tmax values frequently significantly diverge from HPCT measurements, emphasizing the limited reliability of Tmax for estimating subsurface temperatures. In the Sava Basin, 61% of wells show Tmax-HPCT differences greater than 10 °C, and in seven wells the discrepancy exceeds 20 °C. Similarly, in the Drava Basin, nearly half of the wells exhibit differences greater than 10 °C, with five wells showing deviations above 20 °C. In most cases, the reported Tmax values do not represent true maxima, so the linear regression was performed between Tmax and temperatures obtained from DST measurements, providing a basis for refining subsurface temperature estimates. Full article

We study one-dimensional diffusions reflected at a boundary and analyze their pathwise “episodes” away from the boundary through Itô’s excursion theory. Under a fixed height cap of $a>0$, each excursion is equipped with three natural marks: its lifetime $\zeta$, its maximum $M$, and an additive (area-type) functional $A_f={\int }_0^{\zeta }f\left(e_t\right)dt$. Our main object is the height-truncated Itô-excursion Laplace exponent ${\mathsf{\Psi }}_{\alpha ,\lambda ;af}:=n\left(1-e^{-\alpha \zeta -\lambda A_f}; M<a\right)$ which jointly characterizes episode duration and cumulative load while excluding barrier-crossing spikes. We establish a general boundary–flux representation: ${\mathsf{\Psi }}_{\alpha ,\lambda ;af}$ is obtained as a boundary flux (in scale) of the unique solution to a one-dimensional killed Feynman–Kac boundary-value problem on $(0, a)$. This transfer principle yields a unified and tractable route to explicit computation. We implement it in three solvable families—the reflected arithmetic Brownian motion, reflected Ornstein–Uhlenbeck diffusions, and squared Bessel/Bessel-type diffusions—obtaining closed forms in terms of Airy, parabolic-cylinder, and confluent hypergeometric/Whittaker functions. Using the Poisson point process structure of excursions indexed by local time, we derive explicit extreme-burst laws (maxima and order statistics) for the additive marks up to a local-time horizon, and connect tail intensities to Laplace exponents via numerical Laplace inversion. Finally, we identify the strictly truncated cumulative load in local time as a (typically infinite-activity) subordinator whose Lévy measure coincides with the excursion-mark intensity, linking cumulative-load and extreme-burst statistics through the same exponent. Full article

Precise object tracking of space objects is an image acquisition method that uses the mount of the telescope to orient the instrument in real time towards the target to be tracked, compensating for the target’s motion. Using this method, the object of interest will appear as a circular or point-like shape in the acquired image, while the background stars will appear as streaks. Using precise object tracking, the light from a faint object accumulates in the same region of the image, increasing the chance of observation, but longer exposures also increase the length of the background star streaks and makes the astrometric calibration difficult. This paper presents a method for the automatic processing of image sequences acquired in precise object tracking mode. Our proposed method includes a filtering mechanism that will ensure local maxima in the center of star streaks in order to allow for a publicly available astrometric calibration software to work even if the stars are not point-like, a weighted stacking mechanism to increase the signal-to-noise ratio for faint targets while excluding the stars, an automatic object detection and astrometric reduction mechanism and a constraint-based filtering of outliers for the final generation of the tracklet. The method was tested on multiple observation sessions for surveying the CLUSTER II highly eccentric orbit satellites, including the CLUSTER II FM5 satellite (Rumba) on its final passes before reentry, and the accuracy of the measurements was estimated based on ground truth from ESA’s reentry team. The method was also tested on lower orbit objects and found to be accurate for objects with ranges of more than 1300 km from the observer. Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article