Search Results (4,936)

This paper explores the feasibility of using acoustic wave propagation, particularly in the ultrasonic range, as a solution for data transmission in environments where traditional radio frequency (RF) communication is ineffective due to signal attenuation—such as in liquids or dense media like metal or stone. Leveraging GNU Radio and commercially available audio hardware, a low-cost, SDR (Software Defined Radio) system was developed to transmit data blocks (e.g., images, text, and audio) through various substances. The system employs BFSK (Binary Frequency Shift Keying) and BPSK (Binary Phase Shift Keying), operates at ultrasonic frequencies (typically 40 kHz), and has performance validated under real-world conditions, including water, viscous substances, and flammable liquids such as hydrocarbon fuels. Experimental results demonstrate reliable, continuous communication at Nyquist–Shannon sampling rates, with effective demodulation and file reconstruction. The methodology builds on concepts originally developed for Ad Hoc Sensor Networks in shipping containers, extending their applicability to submerged and RF-hostile environments. The modularity and flexibility of the GNU Radio platform allow for rapid adaptation across different media and deployment contexts. This work provides a reproducible and scalable communication solution for scenarios where RF transmission is impractical, offering potential applications in underwater sensing, industrial monitoring, railways, and enclosed infrastructure diagnostics. Across controlled laboratory experiments, the system achieved 100% successful reconstruction of transmitted image files up to 100 kB and sustained packet delivery success exceeding 98% under stable coupling conditions. Full article

Background: Canada’s cervical cancer elimination plan is challenged by suboptimal screening participation and rising incidence of cervical cancer over the past decade. Cytology, the primary cervical screening method in Canada, is being replaced with HPV testing, which offers superior sensitivity for detecting pre-cancerous lesions and supports initiating screening at age 25 or older and extending screening intervals to five years. Research has shown that women’s insufficient knowledge and negative attitudes toward HPV screening represent a significant barrier to screening uptake. Methods: We conducted a web-based national survey using Best–Worst Scaling (trade off utilities) to quantify women’s preferences for screening test modality, age of initiation, and screening intervals. We also assessed preferences for information sources, provider type, and communication methods. Underscreened individuals were oversampled. Results: Among adequately screened (N = 1778) and underscreened (N = 1570) individuals, preferences favoured co-testing (cytology plus HPV testing), initiating screening at age 21, and three-year screening intervals. Underscreened participants showed relatively higher preference for HPV self-sampling, and as opposed to adequately screened participants, preferred screening by a gynecologist rather than a family physician. Across groups, participants preferred receiving screening-related information and communication by email over postal mail. Conclusions: The misalignment between women’s preferences and current HPV test-based screening implementation plans requires immediate education interventions and modernized, user-preferred communication channels for cervical screening-eligible individuals in Canada. Full article

Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R² of 0.51, followed by SVM (R² = 0.44) and DNN (R² = 0.32); the accuracy of XGBoost (R² = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km², 0.13 km², and 1.35 km², respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community. Full article

Individualization of buildings is one of the key issues in the establishment of three-dimensional (3D) building models. Most existing individualization methods rely on inefficient manual separation, while deep learning approaches require extensive pre-training and are highly influenced by the spatial structure of the models. To address these issues, this paper proposes a novel method for 3D building individualization that integrates SAMPolyBuild with multiple spatial-geometric features. Leveraging the zero-shot learning capability of SAMPolyBuild, the method first performs coarse extraction of individual buildings, then refines the extraction accuracy using multiple spatial-geometric features. Innovatively, two statistical parameters—Jensen-Shannon Divergence and Earth Mover’s Distance—are introduced into the building identification process. To validate the feasibility and effectiveness of the proposed method, experiments were conducted on the Semantic Urban Meshes (SUM) dataset. The results demonstrate that the method can effectively extract individual building models from urban oblique photogrammetric 3D models, achieving an F1-score of approximately 0.83 for buildings with typical spatial structures. Full article

L. pratensis is a significant pest of cotton. Clarifying the intestinal bacterial structure of L. pratensis can provide a theoretical basis for the development of new pest biological control strategies. In this study, high-throughput sequencing was employed to characterize the intestinal bacterial communities across five L. pratensis populations, and the functions of their core metabolic pathways were predicted. The results showed that the intestinal bacterial communities of the five L. pratensis populations comprised 16 phyla, 25 classes, 54 orders, 85 families, 133 genera, and 187 species. Diversity analysis revealed that the diversity of the intestinal bacterial community exhibited a dynamic trend of first increasing and then decreasing during the pest’s growth and development. Specifically, the Shannon and Simpson diversity indices of the nymphal stage were significantly higher than those of the egg and adult stages (p < 0.05). The dominant phylum, class, order, family, genus and species shared by the five groups were Proteobacteria (93.17%), Gammaproteobacteria (48.71%), Rickettsiales (43.83%), Anaplasmataceae (49.39%), Wolbachia (43.83%) and Wolbachia (43.82%). Among them, Acinetobacter was mainly found in the first instar nymph stage, and Serratia was mainly distributed in the fifth instar nymph and female and male adults. Functional prediction results showed that the intestinal bacterial community was mainly enriched in core pathways, including metabolism, genetic information processing, and environmental information processing. This study provides a new target for green prevention and control of L. pratensis and also provides a theoretical basis for further elucidating the succession law and functional mechanism of its gut microbiota. Full article

The entropic region is formed by the collection of the Shannon entropies of all subvectors of finitely many jointly distributed discrete random variables. For four or more variables, the structure of the entropic region is mostly unknown. We utilize a variant of the Maximum Entropy Method to obtain five-variable non-Shannon entropy inequalities, which delimit the five-variable entropy region. This method adds copies of some of the random variables in generations. A significant reduction in computational complexity, achieved through theoretical considerations and by harnessing the inherent symmetries, allowed us to calculate all five-variable non-Shannon inequalities provided by the first nine generations. Based on the results, we define two infinite collections of such inequalities and prove them to be entropy inequalities. We investigate downward-closed subsets of non-negative lattice points that parameterize these collections, and based on this, we develop an algorithm to enumerate all extremal inequalities. The discovered set of entropy inequalities is conjectured to characterize the applied method completely. Full article

Financial time series often show periods during which market index values or asset prices increase or decrease monotonically. These events are known as price runs, uninterrupted trends, or simply runs. By identifying such runs in the daily DJIA and IPC indices from 2 January 1990 to 17 October 2025, we construct their associated returns to obtain a non-arbitrary sample of multi-scale returns, which we call trend returns (TReturns). The timescale of each multi-scale return is determined by the exponentially distributed duration of its corresponding run. We empirically show that the distribution of these coarse-grained returns exhibits distinctive statistical properties: the central region displays an exponential decay, likely resulting from the exponential distribution of trend durations, while the tails follow a power-law decay. This combination of exponential central behavior and asymptotic power-law decay has also been observed in other complex systems, and our findings provide additional evidence of its natural emergence. We also explore the informational properties of multi-scale returns using three measures: Shannon entropy, permutation entropy, and compression-based complexity. We find that Shannon entropy increases with coarse-graining, indicating a wider range of values; permutation entropy drops sharply, revealing underlying temporal patterns; and compression ratios improve, reflecting suppressed randomness. Overall, these findings suggest that constructing TReturns filters out microscopic noise, reveals structured temporal patterns, and provides a complementary and clear view of market behavior. Full article

Background/Objectives: The Metabolic Score for Insulin Resistance (METS-IR), a non-insulin-based index of insulin resistance (IR), and subclinical myocardial injury (SCMI), identified by electrocardiogram (ECG), are each associated with cardiovascular disease (CVD). However, their joint impact on mortality remains unclear. We examined the association of the METS-IR with SCMI and evaluated the individual and combined associations of SCMI and IR with cardiovascular mortality. Methods: We analyzed adults without baseline CVD from the Third National Health and Nutrition Examination Survey (1988–1994) with mortality follow-up through 31 December 2019. The METS-IR was calculated from fasting glucose, triglycerides, high-density lipoprotein cholesterol, and body mass index and categorized as low (<75th percentile) or high (≥75th percentile). SCMI was defined as a cardiac infarction injury score ≥ 10 on ECG. Multivariable logistic regression assessed associations between the METS-IR and SCMI, and Cox regression estimated cardiovascular mortality risk across SCMI-IR combinations. Results: Among 6079 participants, 14.1% had SCMI. Higher METS-IR values were associated with greater SCMI odds (OR (95% CI): 1.58 (1.31–1.90)). Over a median of 18.8 years, 563 (9.1%) cardiovascular deaths occurred. Both SCMI and high IR were individually associated with increased cardiovascular mortality ((HR (95% CI): 1.41 (1.19–1.69) and 1.32 (1.09–1.59), respectively). Participants with both SCMI and high IR had the highest risk (HR 1.92; 95% CI 1.49–2.50) compared with those with neither condition. Conclusions: In adults without prior CVD, the METS-IR was positively associated with SCMI. The coexistence of SCMI and high IR identified a subgroup at nearly twofold higher risk of cardiovascular mortality, supporting the combined use of ECG-based injury markers and metabolic indices for cardiovascular risk stratification. Full article

The genetic diversity and population structure of five tapertail anchovy (Coilia nasus) populations—four wild populations from the Yangtze River (Taizhou, Anqing, Shanghai, Hukou) and one cultured population from Yangzhong—were analyzed using 18 highly polymorphic microsatellite loci. All loci exhibited high polymorphism, with genetic parameters as follows: mean number of alleles = 20.567, expected heterozygosity = 13.506, Shannon information index = 2.743, and polymorphic information content = 0.9624. F-statistics ranged from 0.02898 to 0.05714, indicating varying degrees of genetic differentiation between all populations. Analysis of molecular variance revealed that 4% of the total genetic variation was attributable to differences among populations, 23% to variation among individuals within populations, and 73% to within-individual genetic variation. A UPGMA phylogenetic tree based on Nei’s genetic distance showed that the Shanghai population clustered first with Anqing, followed by Taizhou, Hukou, and finally Yangzhong. Additionally, discriminant functions developed from microsatellite data enabled accurate population assignment for all individuals. These findings provide critical insights into the genetic relationships and structure of C. nasus populations, offering valuable implications for their conservation and management. Full article

As bispecific T-cell engagers (TCEs) gain traction in the oncology treatment landscape, cancer centres must develop robust clinical pathways to ensure their safe and efficient delivery. Given the limited experience of the Canadian medical oncology community with TCEs, collecting and publishing early clinical experiences with these novel agents will be essential to inform best practices and support their safe and effective adoption across the broader Canadian oncology community. The approval of tarlatamab, the first-in-class delta-like ligand 3 (DLL3)-targeted TCE for extensive-stage small cell lung cancer (ES-SCLC), underscores the importance of sharing early clinical experience with this agent, particularly given its unique safety profile, specific monitoring requirements, and use in a population that often has multiple comorbidities. Like other TCEs, tarlatamab is associated with cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), adverse events (AEs) that necessitate the development of dedicated protocols by medical oncologists and multidisciplinary inpatient and outpatient clinical teams to ensure prompt recognition and management of these associated toxicities. By sharing insights into administration protocols, dose ramp-up procedures, post-cycle 1 monitoring, and AE management strategies implemented at their centres, early adopters of tarlatamab can help other institutions develop and refine their own protocols more efficiently. Lessons learned during the early implementation phase, including the roles of various healthcare providers and the transition from inpatient to outpatient care, should facilitate the smoother integration of tarlatamab and other TCEs for solid tumours into clinical pathways across Canada. Full article

Accurate patent valuation remains a persistent challenge in intellectual property management, particularly in the food industry, where technological homogeneity and rapid innovation cycles introduce substantial noise into observable performance indicators. Traditional valuation approaches, whether based on subjective expert judgment or citation-based metrics, often struggle to effectively reduce information uncertainty in this context. To address this limitation, this study proposes an objective, data-driven patent valuation framework grounded in information theory. We construct a multidimensional evaluation system comprising nine indicators across technological, legal, and economic dimensions and apply it to a large-scale dataset of 100,648 invention patents. To address the heavy-tailed nature of patent indicators without sacrificing the information contained in high-impact outliers, we introduce a square-root transformation strategy that stabilizes dispersion while preserving ordinal relationships. Indicator weights are determined objectively via Shannon entropy, capturing the relative scarcity and discriminatory information content of each signal, after which comprehensive value scores are derived using the TOPSIS method. Empirical results reveal that the entropy-based model assigns dominant weights to so-called “costly signals”, specifically PCT applications (29.53%) and patent transfers (24.36%). Statistical correlation analysis confirms that these selected indicators are significantly associated with patent value ($p<0.001$), while bootstrapping tests demonstrate the robustness of the resulting weight structure. The model’s validity is further evaluated using an external benchmark (“ground truth”) dataset comprising 55 patents recognized by the China Patent Award. The proposed framework demonstrates substantially stronger discriminatory capability than baseline methods, awarded patents achieve an average score 2.64 times higher than that of ordinary patents, and the enrichment factor for award-winning patents within the Top-100 ranking reaches 91.5. Additional robustness analyses, including benchmarking against the Weighted Sum Model (WSM), further confirm the methodological stability of the framework, with sensitivity analysis revealing an exceptional enrichment factor of 183.1 for the Top-50 patents. These findings confirm that the Entropy–TOPSIS framework functions as an effective information-filtering mechanism, amplifying high-value patent signals in noise-intensive environments. Consequently, the proposed model serves as a generalizable and theoretically grounded tool for objective patent valuation, with particular relevance to industries characterized by heavy-tailed data and high information uncertainty. Full article

Substitution boxes, or S-boxes, are critical elements of symmetric block cipher algorithms—these being the ones that provide “confusion” (a concept defined by Claude Shannon). This work presents a method for generating $8\times 8$ S-boxes using a multi-objective evolutionary algorithm, aiming to simultaneously optimize Nonlinearity (NL) and the Strict Avalanche Criterion (SAC), which are two important cryptographic properties of S-boxes. Chaotic maps were used to generate the initial population. Experimental executions were carried out, and the results were compared to evaluate the different Pareto fronts obtained. The results indicate that the proposed versions of our algorithm achieve good performance, comparable to the state of the art (with only 150 generations of execution), and it was also found that they continue to improve for runs of 5000 generations. This method can be used for the generation of S-boxes with specific properties. Full article

Estimating runoff in ungauged catchments remains a major challenge in hydrology, particularly in remote Andean headwaters where limited accessibility and budgetary constraints hinder the long-term operation of monitoring networks. This study integrates satellite-derived rainfall data, hydrological modeling, and benthic macroinvertebrate diversity analysis to explore how short-term antecedent flow conditions relate to temporal variation in community structure. The research was conducted in a pristine 0.26 km² micro-catchment of the upper Collay basin (southern Ecuador). Daily simulated discharge was used to compute antecedent flow descriptors representing short-term variability and cumulative changes in stream conditions, which were related to taxonomic (i.e., H = Shannon diversity, E = Pielou evenness, and D = Simpson dominance) and functional indices (i.e., Rao = Rao’s quadratic entropy, FAD1 = Functional Attribute Diversity, and wFDc = weighted functional dendrogram-based diversity) using Generalized Additive Models. Results showed progressively higher hydrology–biology associations with increasing antecedent flow integration length, suggesting that biological variability responds more strongly to cumulative than to instantaneous flow conditions. Among hydrological descriptors, the cumulative magnitude of negative flow changes was consistently associated with taxonomic diversity. H and E showed more coherent and robust patterns than functional metrics, indicating a faster response of community composition to short-term hydrological variability, whereas functional diversity integrates slower ecological processes. While based on modeled discharge under severe hydrometeorological data limitations, this study provides a practical ecohydrological starting point for identifying short-term hydrological memory signals potentially relevant to aquatic biodiversity in ungauged headwater systems. Full article

This study analyzed the phenotypic and internal fruit quality diversity of six superior Nai plum trees to provide detailed phenotypic profiles and preliminary relational hypotheses, supporting superior genotype re-selection for breeding. Using leaves and mature fruits, we conducted diversity, correlation, and principal component analysis (PCA) on all quantitative traits. The average Shannon–Wiener index (H′) for qualitative traits was 0.543, and the average coefficient of variation for quantitative traits was 19.98%. Correlation analysis revealed complex trait relationships, including the synchronous variation between the total number of soluble solids (TSS) and reducing sugars (RS) or soluble sugars (SS) and the opposite trends between the TSS and potassium (K), magnesium (Mg), or soluble protein (SP). PCA extracted four principal components (cumulative contribution: 91.074%) from all traits. Based on factor scores, S6 ranked highest, indicating its potential as a comprehensive candidate. The findings offer a theoretical basis for Nai plum cultivation and breeding. Full article

Cotton (Gossypium spp.) is the most important fiber crop for the textile industry globally. Abiotic stresses, including drought, have become prevalent in affecting cotton production worldwide. There is a shortage of studies on the use of biochar as a soil amendment in the semi-arid and arid Southwest and West U.S. Cotton Belt to alleviate drought stress. This study was conducted to examine the effects of biochar at four application rates (0, 6.25, 12.5, and 25.0 t ha⁻¹) on cotton yield and yield components using six tetraploid cotton genotypes, including one Pima (G. barbadense L.) and five Upland cottons (G. hirsutum L.), under well-watered (WW) and drought stress (DS) conditions in an arid region of New Mexico, USA. The six cotton genotypes consistently showed that DS at the flowering stage significantly decreased boll number (BN), boll weight (BW), and lint percentage (LP), and thereby seed cotton weight (SCW) per plant and lint weight (LW) per plant. However, Pima DP 359 RF had the lowest reduction (23–33%) in BN, SCW, and LW due to drought, while DP 2020 B3XF was the most sensitive to drought, with a 45–48% reduction in the traits. Under DS conditions, biochar at the rate of 12.5 t ha⁻¹ had the highest SCW and LW, and the lowest reduction in BN, BW, SCW, and LW due to drought, which was significantly different from the non-biochar control, and no genotype × biochar interaction was detected. However, biochar had no positive effects on cotton productivity under non-drought conditions. This study has demonstrated the positive effects of biochar on cotton yield and yield components in alleviating drought stress, laying the foundation for more follow-up studies toward its utility in cotton production in semi-arid and arid areas. Full article