Search Results (51)

Gully erosion refers to the landform formed by soil and water loss through gully development, which is a critical manifestation of soil degradation. However, research on the spatio-temporal variations in erosion gullies at the county scale remains insufficient, particularly regarding changes in gully aggregation and their driving factors. This study utilized high-resolution remote sensing imagery, gully interpretation information, topographic data, meteorological records, vegetation coverage, soil texture, and land use datasets to analyze the spatio-temporal patterns and influencing factors of erosion gully evolution in Bin County, Heilongjiang Province of China, from 2012 to 2022. Kernel density evaluation (KDE) analysis was also employed to explore these dynamics. The results indicate that the gully number in Bin County has significantly increased over the past decade. Gully development involves not only headward erosion of gully heads but also lateral expansion of gully channels. Gully evolution is most pronounced in slope intervals. While gentle slopes and slope intervals host the highest density of gullies, the aspect does not significantly influence gully development. Vegetation coverage exhibits a clear threshold effect of 0.6 in inhibiting erosion gully formation. Additionally, cultivated areas contain the largest number of gullies and experience the most intense changes; gully aggregation in forested and grassland regions shows an upward trend; the central part of the black soil region has witnessed a marked decrease in gully aggregation; and meadow soil areas exhibit relatively stable spatio-temporal variations in gully distribution. These findings provide valuable data and decision-making support for soil erosion control and transformation efforts. Full article

Brassinosteroids (BRs) are essential phytohormones that orchestrate various stages of plant growth and development. The BR signaling cascade is mediated through a phosphorylation network involving sequential activation of the plasma membrane-localized receptor kinase Brassinosteroid-Insensitive 1 (BRI1), the cytoplasmic kinase Brassinosteroid-Insensitive 2 (BIN2), and the transcription factors BRI1-EMS suppressor 1 (BES1) and Brassinazole-Resistant 1 (BZR1). These transcription factors activate thousands of nuclear genes. Recent evidence highlights that ubiquitination has emerged as an equally pivotal mechanism that dynamically controls the BR signaling pathway by modulating the activity, subcellular localization, and protein stability of these core signaling components. In this review, we systematically analyze the central role of ubiquitination in determining the function, localization, and degradation of these proteins to fine-tune the outputs of BR signaling. We provide comparative perspectives on the functional conservation and divergence of ubiquitin-related regulatory components in the model plant Arabidopsis versus other plant species. Furthermore, we critically evaluate current knowledge gaps in the ubiquitin-mediated spatiotemporal control of BR signaling, offering insights into potential research directions to elucidate this sophisticated regulatory network. Full article

The sonoscape of a small town at the foot of the Northern Apennines Mountains in north–central Italy was studied using a regular grid of automatic recording devices, which collected ambient sounds during the spring of 2024. The study area is characterized by high landscape heterogeneity, a result of widespread suburban agricultural abandonment and urban development. Sonic data were analyzed using the Sonic Heterogeneity Index and nine derivative metrics. The sonic signatures from 26 stations exhibited distinct, spatially explicit patterns that were hypothesized to be related to a set of 11 landcover types and seven landscape metrics. The unique sound profile of each sample site was consistent with the emerging heterogeneity of landcover typical of many Mediterranean regions. Some sonic indices exhibited stronger correlations with landscape metrics than others. In particular, the Effective Number of Frequency Bins Ratio (ENFBr) and Sheldon’s Evenness (E) proved particularly effective at revealing the link between sonic processes and landscape patterns. The sonoscape and landscape displayed correlations significantly aligned with their variability, highlighting the ecological heterogeneity of the sonic and physical domains in the study area. This case study underscores the importance of selecting appropriate metrics to describe complex ecological processes, such as the relationships and cause-and-effect dynamics of environmental sounds among human altered landscapes. Full article

Background: Point-of-care (POC) diagnostics is an innovative approach to healthcare analysis that brings the diagnostic process closer to the patient’s immediate care setting. This study was conducted to assess POC testing devices’ use in diagnosing cancer and detecting the main challenges facing laboratory specialists. Method: A cross-sectional study was conducted on conveniently selected laboratory specialists working in the Prince Mohammed bin Abdulaziz Hospital in Riyadh for six weeks. Result: A total of 187 study participants (51% males and 49% females) were enrolled. Around one-half of them (96, 51%) were less than 30 years old, and 85% had 1–5 years of experience, with 61% (124) having no previous cancer diagnosis devices training. Most of this study’s cohort was using ABL 90 Radio meter/blood gases (45, 24%), followed by ABL 800 Radio meter/blood gases (39, 20.9%), as the main cancer diagnostic devices. Several challenges were faced by this study’s participants during their work with cancer diagnosis devices. The participants shared that some time was needed to use most of the devices, and learning how to use them was a significantly steep learning curve (2.99 ± 0.07 of participants). Most participants (113, 60.4%) carried out all the control testing, and their results were compared completely (100%) with the central laboratory. They took special precautions to keep the instruments safe (162, 86.6%). Conclusion: The correlation between type of devices used and the challenges faced during the use of POCT cancer diagnosis devices showed that there is a significant correlation between all challenges facing the participants and the type of devices (p = 0.001), except for the need for time to use these devices (p = 0.53). There are many challenges facing workers who operate point-of-care cancer diagnosis devices to a high degree. Full article

An inverse-square probability mass function (PMF) is at the Newcomb–Benford law (NBL)’s root and ultimately at the origin of positional notation and conformality. $Pr\left(Z\right)={\left(2Z\right)}^{-2}$, where $Z\in {\mathbb{Z}}^{+}$. Under its tail, we find information as harmonic likelihood $\mathcal{L}\left(\left[s,t\right)\right)=H_{t-1}-H_{s-1}$, where $H_n$ is the nth harmonic number. The global $\mathbb{Q}$-NBL is $Pr\left(b,q\right)={\displaystyle \raisebox{1ex}{$\mathcal{L}\left(\left[q,q+1\right)\right)$}\!\left/ \!\raisebox{-1ex}{$\mathcal{L}\left(\left[1,b\right)\right)$}\right.}={\left(q{H}_{b-1}\right)}^{-1}$, where b is the base and q is a quantum ($1\le q<b$). Under its tail, we find information as logarithmic likelihood $\ell \left(\left[i,j\right)\right)=ln{\displaystyle \raisebox{1ex}{$j$}\!\left/ \!\raisebox{-1ex}{$i$}\right.}$. The fiducial $\mathbb{R}$-NBL is $Pr\left(r,d\right)={\displaystyle \raisebox{1ex}{$\ell \left(\left[d,d+1\right)\right)$}\!\left/ \!\raisebox{-1ex}{$\ell \left(\left[1,r\right)\right)$}\right.}={log}_r\left(1+{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$d$}\right.}\right)$, where $r\ll b$ is the radix of a local complex system. The global Bayesian rule multiplies the correlation between two numbers, s and t, by a likelihood ratio that is the NBL probability of bucket $\left[s,t\right)$ relative to b’s support. To encode the odds of quantum j against i locally, we multiply the prior odds ${\displaystyle \raisebox{1ex}{$Pr\left(b,j\right)$}\!\left/ \!\raisebox{-1ex}{$Pr\left(b,i\right)$}\right.}$ by a likelihood ratio, which is the NBL probability of bin $\left[i,j\right)$ relative to r’s support; the local Bayesian coding rule is $\tilde{o}\left(j:i|r\right)={\displaystyle \raisebox{1ex}{$i$}\!\left/ \!\raisebox{-1ex}{$j$}\right.}{log}_r{\displaystyle \raisebox{1ex}{$j$}\!\left/ \!\raisebox{-1ex}{$i$}\right.}$. The Bayesian rule to recode local data is $\tilde{o}\left(j:i|r^{\prime }\right)=\tilde{o}\left(j:i|r\right){\displaystyle \raisebox{1ex}{$lnr$}\!\left/ \!\raisebox{-1ex}{$ln{r}^{\prime }$}\right.}$. Global and local Bayesian data are elements of the algebraic field of “gap ratios”, ${\displaystyle \frac{A-B}{C-D}}$. The cross-ratio, the central tool in conformal geometry, is a subclass of gap ratio. A one-dimensional coding source reflects the global Bayesian data of the harmonic external world, the annulus $\left\{x\in \mathbb{Q}|1\le \left|x\right|<b\right\}$, into the local Bayesian data of its logarithmic coding space, the ball $\left\{x\in \mathbb{Q}|\left|x\right|<1-{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$b$}\right.}\right\}$. The source’s conformal encoding function is $y={log}_r\left(2x-1\right)$, where x is the observed Euclidean distance to an object’s position. The conformal decoding function is $x={\displaystyle \frac{1}{2}}\left(1+r^y\right)$. Both functions, unique under basic requirements, enable information- and granularity-invariant recursion to model the multiscale reality. Full article

The exponential growth of Internet of Things (IoT) devices has led to massive volumes of data, challenging traditional centralized processing paradigms. The cloud–edge continuum computing model has emerged as a promising solution to address this challenge, offering a distributed approach to data processing and management and improved performances in terms of the overhead and latency of the communication network. In this paper, we present a novel resource allocation optimization solution in cloud–edge continuum architectures designed to support multiple heterogeneous mobile clients that run a set of applications in a 5G-enabled environment. Our approach is structured across three layers, mist, edge, and cloud, and introduces a set of innovative resource allocation models that addresses the limitations of the traditional bin-packing optimization problem in IoT systems. The proposed solution integrates task offloading and resource allocation strategies designed to optimize energy consumption while ensuring compliance with Service Level Agreements (SLAs) by minimizing resource consumption. The evaluation of our proposed solution shows a longer period of active time for edge servers because of the lower energy consumption. These results indicate that the proposed solution is viable and a sustainability model that prioritizes energy efficiency in alignment with current climate concerns. Full article

Saffron (Crocus sativus L.) is one of the most expensive spices in the world due to its strong market demand combined with its labor-intensive production process, which needs a lot of labor and has significant costs. New cultivation methods and traceability systems are required to improve and valorize local Italian saffron production. In this study, we conducted a three-year trial in Umbria (Central Italy), looking for a soilless cultivation method based on wooden bins posted at a suitable height from the ground to ease the sowing of corms and harvesting of flowers. Moreover, the spice traceability could be based on investigating the genetic variability of Italian saffron populations using SNP markers. The proposed novel cultivation method showed significantly higher stigma and corm production than the traditional one. At the same time, the genetic analysis revealed a total of 55 thousand SNPs, 53 of which were specific to the Italian saffron populations suitable to start a food traceability and spice certification. Full article

Leveraging the photometric data of the Sloan Digital Sky Survey and the Galaxy Evolution Explorer (GALEX), we construct mean/median spectral energy distributions (SEDs) for unique bright quasars in redshift bins of 0.2 and up to $z\simeq 3$, after taking the GALEX non-detection into account. Further correcting for the absorption of the intergalactic medium, these mean/median quasar SEDs constitute a surprisingly redshift-independent mean/median composite SED from the rest-frame optical down to ≃$500 \mathring{\mathrm{A}}$ for quasars with bolometric luminosity brighter than ${10}^{45.5}\mathrm{erg}{\mathrm{s}}^{-1}$. Moreover, the mean/median composite quasar SED is plausibly also independent of black hole mass and Eddington ratio, and suggests similar properties of dust and gas in the quasar host galaxies since cosmic noon. Both the mean and median composite SEDs are nicely consistent with previous mean composite quasar spectra at wavelengths beyond ≃$1000 \mathring{\mathrm{A}}$, but at shorter wavelengths, are redder, indicating, on average, less ionizing radiation than previously expected. Through comparing the model-predicted to the observed composite quasar SEDs, we favor a simply truncated disk model, rather than a standard thin disk model, for the quasar central engine, though we request more sophisticated disk models. Future deep ultraviolet facilities, such as the China Space Station Telescope and the Ultraviolet Explorer, would prompt revolutions in many aspects, including the quasar central engine, production of the broad emission lines in quasars, and cosmic reionization. Full article

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing “Smart Bins” and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process—which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods. Full article

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information–theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of $n=3$ variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller–Maddow method is derived for the O-information. We find that for systems of $n=3$ variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information. Full article

The family Beggiatoaceae is currently represented by 25 genera in the Genome Taxonomy Database, of which only 6 have a definite taxonomic status. Two metagenome-assembled genomes (MAGs), WS_Bin1 and WS_Bin3, were assembled from metagenomes of the sulfur mats coating laminaria remnants in the White Sea. Using the obtained MAGs, we first applied phylogenetic analysis based on whole-genome sequences to address the systematics of Beggiatoaceae, which clarify the taxonomy of this family. According to the average nucleotide identity (ANI) and average amino acid identity (AAI) values, MAG WS_Bin3 was assigned to a new genus and a new species in the family Beggiatoaceae, namely, ‘Candidatus Albibeggiatoa psychrophila’ gen. nov., sp. nov., thus providing the revised taxonomic status of the candidate genus ‘BB20’. Analysis of 16S rRNA gene homology allowed us to identify MAG WS_Bin1 as the only currently described species of the genus ‘Candidatus Parabeggiatoa’, namely, ‘Candidatus Parabeggiatoa communis’, and consequently assign the candidate genus ‘UBA10656’, including four new species, to the genus ‘Ca. Parabeggiatoa’. Using comparative whole-genome analysis of the members of the genera ‘Candidatus Albibeggiatoa’ and ‘Ca. Parabeggiatoa’, we expanded information on the central pathways of carbon, sulfur and nitrogen metabolism in the family Beggiatoaceae. Full article

Additive manufacturing powders require a well-defined particle size distribution (PSD) and spherical morphology to ensure good flowability. To simplify characterisation, powders can be prepared using standard metallurgical techniques followed by optical imaging of the cross-sectioned particles. Measured PSDs of particle sections are typically underestimates of the true PSD; hence, stereological corrections are required. Variations arise in the histogram binning methods (central binning versus upper limit binning) of commonly used stereological corrections. Although the results show some sensitivity to the binning method used, the GCO method seemed reasonably robust to changes in the binning method. However, authors are encouraged to follow the method as it is intended within the literature, which was found to be especially true when using Saltykov’s method. Full article

Quantization is a widespread method for reducing the inference time of neural networks on mobile Central Processing Units (CPUs). Eight-bit quantized networks demonstrate similarly high quality as full precision models and perfectly fit the hardware architecture with one-byte coefficients and thirty-two-bit dot product accumulators. Lower precision quantizations usually suffer from noticeable quality loss and require specific computational algorithms to outperform eight-bit quantization. In this paper, we propose a novel 4.6-bit quantization scheme that allows for more efficient use of CPU resources. This scheme has more quantization bins than four-bit quantization and is more accurate while preserving the computational efficiency of the later (it runs only 4% slower). Our multiplication uses a combination of 16- and 32-bit accumulators and avoids multiplication depth limitation, which the previous 4-bit multiplication algorithm had. The experiments with different convolutional neural networks on CIFAR-10 and ImageNet datasets show that 4.6-bit quantized networks are 1.5–1.6 times faster than eight-bit networks on the ARMv8 CPU. Regarding the quality, the results of the 4.6-bit quantized network are close to the mean of four-bit and eight-bit networks of the same architecture. Therefore, 4.6-bit quantization may serve as an intermediate solution between fast and inaccurate low-bit network quantizations and accurate but relatively slow eight-bit ones. Full article

The range accuracy of single-photon-array three-dimensional (3D) imaging systems is limited by the time resolution of the array detectors. We introduce a method for achieving super-resolution in 3D imaging through sub-bin delayed scanning acquisition and fusion. Its central concept involves the generation of multiple sub-bin difference histograms through sub-bin shifting. Then, these coarse time-resolution histograms are fused with multiplied averages to produce finely time-resolved detailed histograms. Finally, the arrival times of the reflected photons with sub-bin resolution are extracted from the resulting fused high-time-resolution count distribution. Compared with the sub-delayed with the fusion method added, the proposed method performs better in reducing the broadening error caused by coarsened discrete sampling and background noise error. The effectiveness of the proposed method is examined at different target distances, pulse widths, and sub-bin scales. The simulation analytical results indicate that small-scale sub-bin delays contribute to superior reconstruction outcomes for the proposed method. Specifically, implementing a sub-bin temporal resolution delay of a factor of 0.1 for a 100 ps echo pulse width substantially reduces the system ranging error by three orders of magnitude. Furthermore, Monte Carlo simulations allow to describe a low signal-to-background noise ratio (0.05) characterised by sparsely reflected photons. The proposed method demonstrates a commendable capability to simultaneously achieve wide-ranging super-resolution and denoising. This is evidenced by the detailed depth distribution information and substantial reduction of 95.60% in the mean absolute error of the reconstruction results, confirming the effectiveness of the proposed method in noisy scenarios. Full article

Utilizing the Modified Hagedorn function with embedded flow, we analyze the transverse momenta ($p_T$) and transverse mass ($m_T$) spectra of ${\pi }^{+}$ in Au–Au, Cu–Cu, and d–Au collisions at $\sqrt{s_{NN}}$ = 200 GeV across various centrality bins. Our study reveals the centrality and system size dependence of key freezeout parameters, including kinetic freezeout temperature $\left(T_0\right)$, transverse flow velocity $\left({\beta }_T\right)$, entropy-related parameter $\left(n\right)$, and kinetic freezeout volume (V). Specifically, $T_0$ and n increase from central to peripheral collisions, while ${\beta }_T$ and V show the opposite trend. These parameters also exhibit system size dependence; $T_0$ and ${\beta }_T$ are smaller in larger collision systems, whereas V is larger. Importantly, central collisions correspond to a stiffer Equation of State (EOS), characterized by larger ${\beta }_T$ and smaller $T_0$, while peripheral collisions indicate a softer EOS. These insights are crucial for understanding the properties of Quark–Gluon Plasma (QGP) and offer valuable constraints for Quantum Chromodynamics (QCD) models at high temperatures and densities. Full article