Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed manually. Existing algorithms for the automatic detection of polar mesocyclones are based on the conventional analysis of patterns of cloudiness and involve different empirically defined thresholds of geophysical variables. As a result, various detection methods typically reveal very different results when applied to a single dataset. We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). As a first step, we demonstrate that DCNN model is capable of performing binary classification of 500x500km patches of satellite images regarding MC patterns presence in it. The training dataset is based on the reference database of MCs manually tracked in the Southern Hemisphere from satellite mosaics. We use a subset of this database with MC diameters falling in the range of 200-400 km. This dataset is further used for testing several different DCNN setups, specifically, DCNN built “from scratch”, DCNN based on VGG16 pre-trained weights also engaging the Transfer Learning technique, and DCNN based on VGG16 with Fine Tuning technique. Each of these networks is further applied to both infrared (IR) and a combination of infrared and water vapor (IR+WV) satellite imagery. The best skills (97% in terms of the binary classification accuracy score) is achieved with the model that averages the estimates of the ensemble of different DCNNs. The algorithm can be further extended to the automatic identification and tracking numerical scheme and applied to other atmospheric phenomena characterized by a distinct signature in satellite imagery.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

In this paper the dynamics of spatially extended infected phytoplankton with the Holling type II functional response and logistically growing susceptible phytoplankton system is studied. The proposed model is an extension of temporal model available [6], in spatiotemporal domain. The reaction diffusion system exhibits spatiotemporal chaos in phytoplankton dynamics. This is particularly important for the spatially extended systems that are studied in this paper as they display a wide spectrum of ecologically relevant behavior, including chaos. In this system stability of the system is studied with respect to disease contact rate and the growth fraction of infected phytoplankton indirectly rejoin the susceptible phytoplankton population. The results of numerical experiments in one dimension and two dimensions in space as well as time series in temporal models are presented using MATLAB simulation. Moreover, the stability of the corresponding temporal model is studied analytically. Finally, the comparison of the three types of numerical experimentations are discussed in conclusion.

Aedes (Stegomyia) albopictus is a mosquito native to Southeast Asia. Currently, it has a wide distribution in America where natural infection with arboviruses of medical and veterinary importance has been reported. In spite of their importance in the transmission of endemic arbovirus, basic information of parameters affecting their vectorial capacity is poorly investigated. The aim of the work was to update the distribution range of Ae. albopictus in the Americas, review the blood-feeding patterns and compare the minimum infection rate (MIR) of the dengue virus (DENV) between studies of vertical and horizontal transmission. The current distribution of Ae. albopictus encompasses 21 countries in the Americas. Extensive review has been conducted for the blood-feeding patterns of Ae. albopictus. The results suggest that the mosquito is capable of feeding on 16 species of mammals and five species of avian. Humans, dogs, and rats are the most common host. Eight arboviruses with the potential to infect humans and animals have been isolated in Ae. albopictus. In the United States of America (USA), Eastern equine encephalitis virus, Keystone virus, La Crosse Virus, West Nile virus, and Cache Valley virus were isolated in the Asian mosquito. In Brazil, Mexico, Colombia, and Costa Rica, DENV (all serotypes) has been frequently identified in field-caught Ae. albopictus. Overall, the estimated MIR in Ae. albopictus infected with DENV is similar between horizontal (10.95) and vertical transmission (8.28). However, in vertical transmission, there is a difference in the MIR values if the DENV is identified from larvae or adults (males and females emerged from a collection of eggs or larvae). MIR estimated from larvae is 14.04 and in adults is 4.04. In conclusion, it has to be highlighted that Ae. albopictus is an invasive mosquito with wide phenotypic plasticity to adapt to broad and new areas, it is highly efficient to transmit the DENV horizontally and vertically, it can participate in the inter-endemic transmission of the dengue disease, and it can spread zoonotic arboviruses across forest and urban settings.

Informational (Shannon) measures of symmetry are introduced and analyzed for the patterns built of 1D and 2D shapes. The informational measure of symmetry Hsym (G) characterizes the an averaged uncertainty in the presence of symmetry elements from the group G in a given pattern; whereas the Shannon-like measure of symmetry Ωsym (G) quantifies averaged uncertainty of appearance of shapes possessing in total n elements of symmetry belonging to group G in a given pattern. Hsym(G1)=Ωsym(G1)=0 for the patterns built of irregular, non-symmetric shapes. Both of informational measures of symmetry are intensive parameters of the pattern and do not depend on the number of shapes, their size and area of the pattern. They are also insensitive to the long-range order inherent for the pattern. Informational measures of symmetry of fractal patterns are addressed. The mixed patterns including curves and shapes are considered. Time evolution of the Shannon measures of symmetry is treated. The close-packed and dispersed 2D patterns are analyzed.

Worldwide, about one in eight people live in slums. Empirical studies based on satellite data have identified that the size distributions of this type of settlement are similar in different cities of the Global South. Based on these results, we developed a model describing the formation of slums with a Turing mechanism, in which patterns are created by diffusion-driven instability and the inherent characteristic length of the system is independent of boundary conditions. We examine the model in this paper by critically reflecting its assumptions, comparing them with recent empirical observations and discussing possible adjustments and future extensions based on new methods of identifying pattern formation mechanisms.

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

Long-acting injectable antipsychotics (LAIs) offer many benefits to patients with schizophrenia spectrum disorder (SSD). They are used with very different frequencies due to questions of eligibility or patients’ and prescribers’ attitudes towards LAI use. We assessed the prescribing rates of LAIs in a large academic psychiatric hospital with public service mandate in Switzerland and compared them with other countries and health care systems. To our knowledge this study is the first to investigate the inpatient LAI-use in Europe. Medical records of all patients diagnosed with SSD discharged from the Clinic of Adult Psychiatry of the University Hospital of Psychiatry Zurich over a 12-month period from January to December 2019 were evaluated regarding the prescribed antipsychotics at the time of discharge. The rates of use of LAIs among all patients and among patients receiving LAI eligible antipsychotic substances were assessed retrospectively. We assessed records of 885 patients with SSD. Among all cases 13.9% received an LAI. Among patients who received antipsychotic medication that was eligible for LAI use 28.3% (n=434) received an agent as LAI. These included paliperidone palmitate (69.9%), aripiprazole monohydrate (14.6%), risperidone (4.9%) and first-generation LAIs (9.8%). Compared to international frequencies of LAI administration, the prescription rate of LAIs in SSD patients was low. Further studies will evaluate patient- and prescriber-related reasons for this low rate.

This article introduces ten similarity measures that utilize cosine and cotangent functions to compare (m,n)-rung orthopair fuzzy sets, this a super class of q-rung orthopair fuzzy sets. Further, we applied our established weighted similarity measures to medical diagnosis, pattern recognition,and building material problems and also presented the comparison between established measures with the existing cosine and cotangent measures of q-rung orthopair, pythagorean and intuitionistic fuzzy sets. Lastly, we considered a numerical example that demonstrates the effective and scientific application of these similarity measures in plant leaf disease classification. Furthermore, we present graphical interpretations to demonstrate the effectiveness of our established measures.

The Southern king crab, Lithodes santolla, is an important fishery resource in the inner seas of Chilean Patagonia. The fishing gear used to catch Southern king crab (bait trap) and its operation at different depths over several months provide the opportunity to gather information about relative abundance. The aim of the present study was to analyze the information collected from the catches of Southern king crab and its associated fauna in the Magdalena Fjord to study the biodiversity and ecology of scavenging decapod crustaceans. Specifically, the study aimed to determine the bathymetric patterns of abundance by species, to model the abundance by season (winter and spring) and to determine the structuring pattern of scavenging decapods assemblage. Samples were collected between June and November 2019 by deploying traps in four bathymetric strata: 1 = 0–50 m; 2 = 51–100 m; 3 =101–150 m; 4 = 151–200 m. We registered a total of 673 decapod specimens associated with the Southern king crab fishery, representing five species: Lithodes santolla (n = 96), Libidoclaea smithii (n = 10), Metacarcinus edwardsii (n = 58), Munida gregaria (n = 507) and Peltarion spinulosum (n = 2). The abundance of decapod species varied by month and bathymetric strata. We found bathymetric patterns for L. santolla abundance, M. gregaria, M. edwardsii, and total species abundance. The most informative model to explain scavenging decapod abundance by GLM included species and season. The null model of species co-occurrence analysis revealed that all species associations during the sampled months were random. Our study is the first that provides evidence about patterns of bathymetrical and temporal variation of scavenging decapod abundance and expands the body of knowledge regarding the biodiversity of scavenger decapods in the Magdalena Sound, which currently is affected by multiple stressors such as climate change and anthropic activities (aquaculture and fisheries).

The study of order patterns of three equally spaced values $x_t,x_{t+d},x_{t+2d}$ in a time series is a powerful tool. The lag $d$ is changed in a wide range so that differences of frequencies of order patterns become autocorrelation functions. Similar to a spectrogram in speech analysis, four ordinal autocorrelation functions are used to visualize big data series, as for instance heart and brain activity over many hours. The method applies to real data without preprocessing, outliers and missing data do not matter. On the theoretical side, we study properties of order correlation functions and show that the four autocorrelation functions are orthogonal in a certain sense. An analysis of variance of a modified permutation entropy can be performed with four variance components associated with the functions.

The plant productivity-richness relationship (PPR) is one of the most debated and important issues in ecology. There have been distinct stages in the research of this issue, including the discovery of the different PPR shapes, respective tests of influencing processes, and integrative research with vegetation investigation, manipulation experiments, and theoretical analysis. The debate largely focuses on what the dominant shapes and underlying mechanisms are. Recent integrative research works following analyses of respective processes affecting PPR have found that the humped, asymptotic, positive, negative, and irregular shapes of PPR are linked to each other. One shape of PPR may change into another. The balance between positive and negative processes determines the different shapes of PPR. Plant diversity has a globally positive effect on plant productivity.

In this paper we present an efficient technique for the synthesis of very large sparse arrays, with arbitrary circularly symmetrical upper bounds for the pattern specifications. The algorithm, that is based on iterative smooth re-weighted L1 minimizations is very flexible, and is capable of achieving very good performances with respect to competitive algorithms. Furthermore, thanks to its efficiency, planar arrays of hundreds of wavelengths can be synthesized with limited computational effort.

This research article investigates the effectiveness of digital forensics analysis (DFA) techniques in identifying patterns and trends in malicious failed login attempts linked to public data breaches or compromised email addresses in Microsoft 365 (M365) environments. Pattern recognition techniques are employed to analyze security logs, revealing insights into negative behavior patterns. The findings contribute to the literature on digital forensics, opposing behavior patterns, and cloud-based cybersecurity. Practical implications include the development of targeted defense strategies and the prioritization of prevalent threats. Future research should expand the scope to other cloud services and platforms, capture evolving trends through more prolonged, more extended analysis periods, and assess the effectiveness of specific mitigation strategies for identified tactics, techniques, and procedures (TTPs).

Traditionally, neutron scattering is an essential method for the analysis of spin structures and spin excitations in bulk materials. Over the last 30 years, polarized neutron scattering in terms of reflectometry has also contributed largely to the analysis of magnetic thin films and magnetic multilayers. More recently it has been shown that polarized neutron reflectivity is, in addition, a suitable tool for the study of thin films laterally patterned with magnetic stripes or islands. We provide a brief overview of the fundamental properties of polarized neutron reflectivity, considering different domain states, domain fluctuations, and different domain sizes with respect to the neutron coherence volume. The discussion is exemplified by a set of simulated reflectivities assuming either complete polarization and polarization analysis, or a reduced form of polarized neutron reflectivity without polarization analysis. Furthermore, we emphasize the importance of the neutron coherence volume for the interpretation of specular and off-specular intensity maps, in particular when studying laterally non-homogeneous magnetic films. Finally, experimental results, fits, and simulations are shown for specular and off-specular scattering from a magnetic film that has been lithographically patterned into a periodic stripe array. These experiments demonstrate the different and mutually complementary information that can be gained when orienting the stripe array parallel or perpendicular to the scattering plane.

This paper presents an approach to the application of the Model-Based Systems Engineering (MBSE) and Model-Based Systems Architecting (MBSA) principles to develop a Model-Based Pattern Language (MBPL). It takes too long for systems engineers and architects to develop a new system from scratch, particularly new space-based systems derived from the existing space systems architectures. A pattern language is a holistic view of reusable logical model artifacts; many are interdisciplinary and introductory, if at all. The results are mostly a combination of the application-specific logical solution, which further results in the best possible overall solution. The main benefit of the pattern language is reducing the time and validation required to generate a new space-based system architecture; this approach will develop top-level requirements in the initial phase of the system development. The rationale of the methodology proposed by the paper is as follows, collect, and decompose published literature and other open-source information available on space system architectures and system models; develop SysML models for systems, subsystems, products, assembly, subassembly level, and mission-specific requirements using CAMEO SysML software. Arrange these patterns to develop a functional ontology and construct a logical architecture pattern library. This approach created, updated, and managed SysML pattern language, which evaluated the expedited new model construction. Again, our objective is to develop a logical pattern language using public domain information and evaluate patterns by constructing a new space mission concept—for example, planetary surface habitat.

This article presents an automated vision-based algorithm for the die-scale inspection of wafer images captured using scanning acoustic tomography (SAT). This algorithm can find defective and abnormal die-scale patterns, and produce a wafer map to visualize the distribution of defects and anomalies on the wafer. The main procedures include standard template extraction, die detection through template matching, pattern candidate prediction through clustering, and pattern classification through deep learning. To conduct the template matching, we first introduce a two-step method to obtain a standard template from the original SAT image. Subsequently, a majority of the die patterns are detected through template matching. Thereafter, the columns and rows arranged from the detected dies are predicted using a clustering method; thus, an initial wafer map is produced. This map is composed of detected die patterns and predicted pattern candidates. In the final phase of the proposed algorithm, we implement a deep learning-based model to determine defective and abnormal patterns in the wafer map. The experimental results verified the effectiveness and efficiency of our proposed algorithm. In conclusion, the proposed method performs well in identifying defective and abnormal die patterns, and produces a wafer map that presents important information for solving wafer fabrication issues.

Pressure drop and flow pattern of oil-water flows were investigated in a 19 mm ID clear polyvinyl chloride pipe consisting of U-bend with radius of curvature of 100 mm. The range for oil and water superficial velocities tested were and respectively. Measurements were carried out under different flow conditions in a test section that consisted of four different parts: upstream of the bend, at the bend and at two redeveloping flow locations after the bend. The result indicated that the bend had limited influence on downstream flow patterns. However, the shear forces imposed by the bend caused some shift flow pattern transition and bubble characteristics in the redeveloping flow section after the bend relative to develop flow before the bend. Generally, pressure gradient at all the test sections increased with both oil fraction and water superficial velocity and there was a sharp change of pressure gradient profile during phase inversion. The transition point where phase inversion occurred was always within the range of . Pressure losses differed at the various test sections and the difference was strongly linked to the superficial velocity of the phases and the flow pattern. At high mixture velocity, pressure losses at the redeveloping section after the bend were higher than that at the bend and that for fully developed flows. At low mixture velocity, pressure losses at the bend are higher than in the straight sections. Pressure drop generally decreased with level of flow development downstream of the bend.

The emerging advancements in separation and classification of various biological matters (e.g., living cells and proteins) using magnetic levitation (MagLev) technology have proven to be effective for improving disease diagnostics. MagLev technique has the capacity to detect and separate useful diagnostic biomarkers from biocomplex environments (e.g., blood and plasma), minimizing the unpleasant daunting task of sample preparations and labeling procedures. Here, we demonstrate the capability of this technique combined with image analysis and machine learning approaches for discriminating the various types of multiple sclerosis (MS) as an important model disease. To arrive at a systematic expert system, we combined robust statistical analysis with machine learning to (1) detect and remove outliers from the raw MagLev image datasets; then (2) process the images and output a low dimensional representation of massive data without losing the main statistical features; and finally (3) predict the MS clinical disease type (Relapsing-Remitting, Primary–Progressive, or Secondary–Progressive) using a classifier. This is expected to improve MS diagnostics since the current practices rely solely on clinical observation and central nervous system imaging, making management approaches are often reactional and inefficient. Thus, there is a need to identify the disease type early on. MagLev is expected to improve MS diagnostics, thereby aiding in prognosis and guiding adequate treatment choices before the patient exhibits signs of permanent neurological deficits.

Soft-matter systems when driven out-of-equilibrium often give rise to structures that usually lie in-between the macroscopic scale of the material and microscopic scale of its constituents. In this paper we review three such systems, the two-dimensional square-lattice Ising model, the Kuramoto model and the Rayeligh-Bénard convection system which when driven out-of-equilibrium give rise to emergent spatio-temporal order through self-organization. A common feature of these systems is that the entities that self-assemble are coupled to one another in some way, either through local interactions or through a continuous media. Therefore, the general nature of non-equilibrium fluctuations of the intrinsic variables in these systems are found to follow similar trends as order emerges. Through this paper, we attempt to look for connections between among these systems and systems in general which give rise to emergent order when driven out-of-equilibrium.

Glycine is an important neurotransmitter in vertebrates, performing both excitatory and inhibitory actions. Synaptic levels of glycine are tightly controlled by the action of two glycine transporters, GlyT1 and GlyT2, located on the surface of glial cells and glycinergic or glutamatergic neurons, respectively. Glycinergic neurotransmission in invertebrates has so far only been investigated in a very limited number of species, and, although it was suggested that its functions are to some extent conserved with vertebrates, the evolution of glycinergic neurotransmission remains very poorly understood. Here, by combining phylogenetic and gene expression analyses, we characterized the glycine transporter complement of amphioxus, an important invertebrate model for studying the evolution of chordates. We show that amphioxus possesses three glycine transporter genes, two of which (GlyT2.1 and GlyT2.2) are closely related to GlyT2 of vertebrates, while the other (GlyT) is a member of an ancestral clade of deuterostome glycine transporters. While expression of GlyT2.2 is predominantly non-neural, GlyT and GlyT2.1 are widely expressed in the amphioxus nervous system and are characterized by differential expression in neurons and glia, respectively. However, in vertebrates, glycinergic neurons express GlyT2 and glia GlyT1, suggesting that the evolution of the chordate glycinergic system was accompanied by complex genetic remodeling leading to the paralog-specific inversion of gene expression. Albeit this genetic divergence between amphioxus and vertebrates, we found strong evidence for a general conservation of the role of glycinergic neurotransmission during larval swimming, allowing us to hypothesize that the neural networks controlling the rhythmic movement of chordate bodies are homologous.

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating this data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited to painless future expansion. Availability:https://glyconnect.expasy.org/glystreem/wiki

Cancer is a complex systemic disease that changes the entire proteome. The analysis of this transformation makes it possible to determine tumor markers, that is, the most characteristic biomacromolecules produced by tumor cells. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Unfortunately, none of the tumor markers, even those used in the clinic, has all these characteristics. Despite this, many tumor markers have demonstrated excellent clinical relevance for monitoring the effectiveness of different treatments for cancer patients. The use of markers also aids in the early detection of cancer recurrence and prognosis. Therefore, the situation in this area can be improved in two ways – by attempting to find an ideal single tumor marker or generating panels of different markers. In both cases, proteomics certainly plays a major role. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers digging very deep plasma proteome. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. Here, we present data about these profiles generated by two-dimensional electrophoresis with the following mass-spectrometry and immunodetection.

[Background] Previous research achievements of the service-oriented tourist city network have often focused on the analysis of its geographical distribution and service role of the important cities instead of the connections and hierarchical tendencies between the whole city in a large region.[Method]Using big data approaches on the regional connections of 38 tourism organizations including famous hotels, air passenger transport, tourism service agencies across 63 most important tourist cities in China. Fuzzy c-means clustering analysis is used to define 8 city arena clusters. [Results]According to the distributions of connectivity between 63 cities, these eight clusters play different service functional roles in the urban tourism network at four hierarchies. With their “center-edge” memberships, these arena clusters are formed by the interweaving process of regional and hierarchical tourism service connections. The results include the analysis of the various service-oriented tourist city in China and point out the geography “gap” faced by network. [Conclusion] Service-oriented tourist cities need to find their hierarchies and positioning in the network scientifically to avoid blind development, to make regional urban tourism sustainable development.

Many clustering approaches succeed in pattern segmentation in many applications. This unsupervised segmentation should be effective to reduce an expert labelling time: i.e, they must be able to detect the number of patterns and identify them in a sequence or map with the right cuts. Several direct and hierarchical clustering approaches are compared for this task. A divisive spectral clustering architecture with a no-cut criteria is also proposed. This new algorithm achieves promise segmentation of spatial UCI databases and marine time series compared to other approaches.

This paper focuses on the combination of the method of moments and the wire-grid approximation as an effective computational technique for modeling symmetrical antennas with low computational cost and quite accurate results. The criteria and conditions for the use of wire-grid surface approximation from various sources are presented together with new recommendations for modeling symmetrical antenna structures using the wire-grid approximation. These recommendations are used to calculate the characteristics of biconical and horn antennas at different frequencies. The results obtained using different grid and mesh settings are compared to those obtained analytically. Moreover, the results are compared to those obtained using the finite difference time domain numerical method, as well as the measured ones. All results are shown to be in a good agreement. The used recommendations for building a symmetrical wire-grid of those symmetrical antenna elements provided the most advantageous parameters of the grid and mesh settings and the wire radius, which are able to give a quite accurate results with low computational cost. Additionally, the known equal area rule was modified for a rectangular grid form. The obtained radiation patterns of a conductive plate using both the original rule and the modified one are compared with the electrodynamic analysis results. It is shown that the use of the modified rule is more accurate when using a rectangle grid form.

This short paper demonstrates that the galactic spiral equations developed from ROTASE model can be used to morphologically simulate the phyllotactic spiral-ring patterns of plants. It is also noticed during this project that although the numbers in the Fibonacci sequence dominate the phyllotactic numbers, there are still significant phyllotactic numbers are not in the Fibonacci sequence, however, most of those numbers are well fit into a Fibonacci sibling sequence starting with numbers 0 and 2. Many plants show the phyllotactic number pairs of 10-16, 16-26 and 26-42 from the Fibonacci sibling sequence, which is the second dominated sequence in the phyllotaxis.

Motivated by the way animals react in nature, we introduce a fully-connected neural-network model, which incorporates the external pattern presented to the system as a fundamental tool of the recognition process. In this new scenario, in the absence of this external field, memories are not attractors inside basins of attraction, as in usual attractor neural networks, although basins may be created according to the external pattern, thus allowing storing a much larger number of memories. The key point consists in calibrating the influence of the external pattern such as to cancel the noise generated by those memories not correlated with the external pattern.We illustrate how this proposal works by including this new contribution in the standard Hopfield model, showing a significant increase in its recognition capacity (typically by a factor $10^{2}$). As an additional feature of this model, we show its ability to react promptly to changes in the external environment, a crucial attribute of living beings. This procedure can be applied to a wide variety of neural-network models to increase considerably their recognition and reaction capabilities.

A better understanding of the expected future cyclonic activity, especially in the Mediterranean Basin in winter, is essential for developing scientifically based adaptation and mitigation methods to extreme precipitation and wind anomalies. The aim of this study is to analyze the change of winter cyclonic activity in the Mediterranean-Black Sea region, within the Atlantic–European region, at the beginning (as the recent historical period), middle and end of the 21st century. The projections are based on an ensemble of seven CMIP6 models, which showed the best consistency with NCEP/NCAR and ERA5 reanalysis, under the intermediate SSP2-4.5 and highest-emission SSP5-8.5 scenarios. The results show a consistent increase of the frequency of cyclones over Central Europe and the British Isles associated with the shift of cyclone tracks: norward from the Western Mediterranean region and southward from the Iceland Low. The latter leads to a decrease of the frequency in the north of the Atlantic–European region. At the same time, there is a reduction of the frequency of cyclones over the east of the Mediterranean Sea consistent with the decrease of cyclogenesis events. Area-averaged cyclone numbers in the Western and Eastern Mediterranean and the Black Sea subregions reduce to the end of the century under the highest-emission scenario, but not constantly and with a raise in the middle of the 21st century under both scenarios, which may be linked to the long-term multidecadal variability or regional features. In general, our study shows that the future winter cyclonic activity in the Mediterranean-Black Sea region responds unevenly to global climate changes, because regional and monthly features are important, as well as accounting for the long-term quasiperiodic variability.

Background: Hypereosinophilic syndromes (HES) are a group of relatively rare disorders in which neurological manifestations are not uncommon including ischemic stroke. The hypothesized pathophysiological mechanisms are hypercoagulability, cardioembolism (mainly mediated by myocardial involvement) and damage to the endothelium. A variable ischemic pattern has been described, including an association of territorial and border zone ischemic stroke. Methods: Three patients who presented to our department with acute stroke were selected aiming to show these three different mechanisms inferred from the stroke pattern on brain Magnetic Resonance Imaging (MRI) and to simultaneously illustrate the three main causes of HES. Results and Discussion: The first patient is a 55-year-old man with an abrupt onset of aphasia due to an acute ischemic stroke involving the left parietal lobule and the angular gyrus; recent lab test had shown hypereosinophilia. An extensive workup excluded primary and secondary causes of hy-pereosinophilia so a diagnosis of idiopathic hypereosinophilia was formulated and he was treated with high doses of steroids. The second patient had severe hypereosinophilia and developed multiple small scattered ischemic lesions, mainly in watershed zones. The history of severe asthma and recurrent sinusitis supported the diagnosis of EGPA (Eosinophilic Granulomatosis with Polyangiitis); considering the severe clinical conditions and the presumptive role of hypereo-sinophilia in determining her symptoms, steroid treatment was promptly started, with good clinical response. The third patient also presented with multiple metachronous ischemic lesions, both in cortical and watershed distribution and marked eosinophilia; the diagnostic work-up found an ovarian cancer. She was treated with steroids and then underwent surgery and adjuvant chemotherapy. Conclusions: HES should be considered in stroke etiological evaluation, although it is a rare disorder, and border zones pattern on neuroimaging is quite suggestive. A thorough research of the sources of hypereosinophilia should be performed to select the appropriate therapy.

Background: Anemia is one of the most frequent diseases worldwide, affecting a third of the general population. Anemia in general and in particular, iron-deficient anemia (IDA), has been associated to a higher risk of thrombotic manifestations, including ischemic stroke and cerebral venous thrombosis (CVT), as well as systemic extra cerebral arterial and venous thrombosis. Despite these data, anemia is seldom considered as an etiological factor of stroke. Methods: An individual case encompassing all known neurovascular and systemic arterial and venous thrombotic manifestations related to IDA is presented with the focus on clinical reasoning issues in the diagnostic pathways, starting from the neuroradiological signs. The main questions have been identified and addressed in a narrative review of the most relevant data in the literature from a pragmatic and clinical viewpoint. Results and Discussion: The presented case concerns a 46 years old man admitted to the Stroke Unit because of acute is-chemic stroke with multiple thrombi in large intracranial and extracranial vessels, multifocal ischemic lesions in several arterial territories and the concurrent finding of asymptomatic CVT, pulmonary embolism with lung infarction and aortic thrombosis. An extended diagnostic work-up excluded the main etiologies (arterial dissection, cardiac embolism, genetic and acquired prothrombotic disorders, as cancer and antiphospholipid syndrome), except for a severe IDA, such as to require blood transfusions followed by anticoagulant therapy for the several thrombotic manifestations. Neuroimaging and systemic vascular findings have been analyzed and the main issues proposed by the case in the diagnostic pathway have been identified and discussed in a pragmatic clinical road map reviewing the data provided by the literature. Conclusions: IDA is a common but treatable condition that, independently or synergically, may increase the risk of thrombotic events. The diagnostic and therapeutic approach has not yet defined and each case should be individually addressed in a pragmatic clinical road map.

The angle of seismic excitation is a significant factor of the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses using in each one of them different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerograms for the evaluation of seismic response. Thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on Multilayered Feedforward Perceptron Neural Networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class which arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as Pattern Recognition problem. As inputs of networks the ratios of seismic parameters’ values along the two horizontal seismic records' components, as well as appropriately chosen structural parameters, were used. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.

Josephson junctions (JJs) in the presence of a magnetic field exhibit qualitatively different interference patterns depending on the spatial distribution of the supercurrent through the junction. In JJs based on two-dimensional topological insulators (2DTIs), the electrons/holes forming a Cooper pair (CP) can either propagate along the same edge or be split into the two edges. The former leads to a SQUID-like interference pattern, with the superconducting flux quantum $\phi_0$ (where $\phi_0=h/2e$) as a fundamental period. If CPs’ splitting is additionally included, the resultant periodicity doubles. Since the edge states are typically considered as being strongly localized, the critical current does not decay as a function of the magnetic field. Here we go beyond this approach and inspect a topological JJ in the tunneling regime featuring extended edge states. We consider the possibility that the two electrons of a CP propagate and explore the junction independently over length scales comparable with the superconducting coherence length. As a consequence of the spatial extension, we obtain a decaying pattern with different possible periods. In particular, we show that, if crossed Andreev reflections (CARs) are dominant and the edge states overlap, the resulting interference pattern features oscillations whose periodicity approaches $2\phi_0$.

Sensors and actuators are fundamental units in Cyber-Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), that are patterns that describe abstract conceptual entities. For concreteness, we study them here in the context of autonomous cars. An autonomous car is a complex system because, in ad-dition to its own complex design, it interacts with other vehicles and with the surrounding in-frastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber-Physical System, where some of its func-tions are performed by Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appro-priate physical actions. From AEPs we can derive concrete patterns, a structure combining re-lated AEPs is an Entity Solution Frame (ESF). Both sensors and actuators are susceptible to mali-cious attacks due to the large attack surface of the system where they are used. Our work is in-tended to make autonomous cars more secure, which also increases their safety. Our final objec-tive is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups to-gether and organizes related security patterns. This article is the first stage of a secure unit that can be used to design not only secure autonomous cars but also any system where sensors and actuators are used. This paper concentrates on AEPs and ESFs for sensors and actuators; that is, on the functional aspects of these devices.

Cancer survival continues to improve in high-income countries, partly explained by advances in screening and treatment. Previous studies have mainly examined the relationship between individual dietary components and cancer prognosis in tumours with good therapeutic response (breast, colon and prostate cancers). The aim of this review was to assess qualitatively (and quantitatively where appropriate) the associations of dietary patterns and cancer prognosis from published prospective cohort studies, as well as the effect of diet interventions by means of randomized controlled trials (RCT). A systematic search was conducted in PubMed, and a total of 35 prospective cohort studies and 14 RCT published between 2011 and 2021 were selected. Better overall diet quality was associated with improved survival among breast and colorectal cancer survivors; adherence to the Mediterranean diet was associated to lower risk of mortality in colorectal and prostate cancer survivors. A meta-analysis using a random-effects model showed that higher versus lower diet quality was associated with a 23% reduction in overall mortality in breast cancer survivors. There was evidence that dietary interventions, generally combined with physical activity, improved overall quality of life, though most studies were in breast cancer survivors. Further cohort and intervention studies in other cancers are needed to make more specific recommendations.

B chromosomes (Bs) or supernumerary chromosomes are extra chromosomes in the species karyotype that can vary in their copy number. Bs are widespread. Usually, the Bs of specimens collected from natural populations are involved in studies. We applied another approach analyzing the Bs in animals of laboratory cultures. In this study, three species of the Macrostomum genus that underwent a recent whole-genome duplication (WGD) were involved. In laboratory lines of M. lignano and M. janickei, the frequency of Bs was less than 1%, while in the laboratory culture of M. mirumnovem, it was nearer 30%. Their number in specimens of the culture varied from 1 to 14. Mosaicism on Bs was discovered in parts of these animals. We analyzed the distribution of Bs among the worms of the laboratory cultures during long-term cultivation, the transmission rates of Bs in the progeny obtained from crosses of worms with different numbers of Bs, and from self-fertilized isolated worms. The DNA content of the Bs in M. mirumnovem was analyzed with the chromosomal in situ suppression (CISS) hybridization of microdissected DNA probes derived from A chromosomes (As). Bs mainly consisted of repetitive DNA. The cytogenetic analysis also revealed the divergence and high variation in large metacentric chromosomes (LMs) containing numerous regions enriched for repeats. The possible mechanisms of the appearance and evolution of Bs and LMs in species of the Macrostomum genus were also discussed.

The study on the temporal duration of optically observed spiral patterns in disc galaxies is based on the finite reservoir of hydrogen available with no replenishment following the initial formation of these well-isolated galaxies. The results reveal a gradual decline in observable spiral arms regardless of any spiral arm formation mechanism. This reduction occurs after half-loop rotations (as defined in the paper) as hydrogen reserves deplete, hindering new star formation. Ultimately, spiral patterns may possibly become optically invisible at normal conditions after a full-loop rotation (as defined in the paper) due to hydrogen depletion, even if the formation mechanism remains active. This supports the idea of short-lived spiral patterns compared to the lifespan of the spiral galaxies. The spiral arm formation may occur once in a galaxy's life based on the limited availability of hydrogens. A concise, universally applicable mechanism for spiral arm formation across various disc galaxies should exist which provides a single mathematical formula to describe the broad range of morphologies of the spiral arms with few adjustable parameters. Some exceptions may be explained within this framework. Furthermore, the observed data on spiral pattern and galactic bar rotation speeds in the Milky Way require rigorous verification, particularly concerning the winding problem.

In the context of the emergence and development of varieties of English around the globe, this paper focuses on Kuwait, where the use of English has greatly increased since the mid-20th century. Drawing on the principles of nativisation and indigenisation, I investigate substrate- and superstrate-derived discourse-pragmatic markers (DPMs) in Kuwaiti English: the Arabic loanwords alhamdulillah and ya'ni and their English equivalents (e.g. I mean and thankfully). Data are drawn from the Kuwaiti English National Corpus, a spoken corpus of almost 800,000 words containing interviews, samples of situated language use, and mediated data (podcasts, TV, radio and YouTube). Keyness and concordance analyses were performed to identify the frequency and functions of the DPMs in Kuwaiti English compared to reference corpora of British and American English. The analysis also takes into account contextual and sociolinguistic information such as the speakers’ social identity. Results show that the Arabic-derived DPMs are abundant in both formal and informal settings, indicating that they have become an integral part of this variety of English. Their English equivalents perform similar roles, but are vastly less common (with the exception of I mean for ya'ni). Both the Arabic- and English-based markers show somewhat narrowed discourse-pragmatic functions and are more likely to be placed in clause-initial position than in ‘native’ contexts. Their use appears to be linked to the social identity of the speakers.

Healthy eating behaviour of women is critical not only for their health but also for their children's health and well-being. The present study examined the dietary pattern, nutrition knowledge and attitudes of working women in Western Province, Sri Lanka. In addition, identify the factors associated with Dietary Diversity Score (DDS). A cross-sectional study was conducted among 300 working women, aged 20-60 years, in Western Province, Sri Lanka. The data on so-cio-demography, dietary patterns, and nutrition-related knowledge and attitudes were collected. Overall, 38% of the women were overweight and 13% were obese. The median frequency of intake of chicken, fish, eggs, milk and milk products, green leafy vegetables and fruits were 2, 5, 2, 9, 5 and 10 respectively, per week. A large majority of the women (70%) had tea/coffee with sugar and snacks (60%) at least four times a week. Only a third of the women met the minimum DDS, while more than half of the women had good nutrition-related knowledge and attitudes. Multiple regression analysis showed that age and household income were significantly independently related to DDS, while attitudes were negatively associated. While there was a trend, the association of nutrition-related knowledge with DDS was not statistically significant (p=0.057). The overall F ratio (8.46) was highly significant (p=0.000) and the adjusted R2 was 0.093. The results demonstrated that a significant proportion of working women have good basic nutrition-related knowledge and attitudes, while two-thirds of them do not meet the minimum DDS. Furthermore, age, family income and knowledge were positively associated with DDS, while attitudes were negatively associated. Before designing any intervention, further research is needed using a qualitative approach to understand how nutrition knowledge and eating behaviour are related in this population group.

The impact of cyclic heat stress (CHS) and lighting events on pig feeding behavior (FB) was investigated. The FB of 90 gilts was recorded in real time under two ambient temperatures (AT): thermoneutrality (TN, 22 °C) or CHS (22°/35 °C). The day was divided into four periods: PI (06-08 h); PII (08-18 h); PIII (18-20 h); and PIV (20-06 h). Automatic and Intelligent Precision Feeders recorded each feed event for each pig. An estimated meal criterion (49 minutes) was used to calculate the FB variables. Feed behavior in both ATs followed a circadian pattern. The CHS reduced the feed intake by 6.9%. Pigs prioritized feed intake during the coolest hours of the day; however, nocturnal cooling did not allow the pigs to compensate for the reduced meal size due to CHS. The highest meal size and most of the meals were observed during the lighting-on period. Pigs reduced their interval between meals during periods II and III. The lighting program increased the meal size when the lights were switched on and reduced the meal size when the lights were switched off. Thus, the dynamics of the FB were largely influenced by AT, while the meal size was affected by the lighting program.

In every agricultural setting, weed seeds can be found in every cubic centimeter of soil. Weed seeds, as a valuable trait underlying the fate of weed populations, exhibit differing levels of seed dormancy, ensuring their survival under uncertain conditions. Seed dormancy is considered as an innate mechanism that constrains germination under suitable conditions that would otherwise stimulate germination of non-dormant seeds. This work provides new insight into changes in germination patterns along the dormant to nondormancy continuum in seeds with physiological dormancy. Notable findings are: (1) germination synchrony can act as a new parameter that quantitatively describes dormancy patterns and subsequently weed population dynamics, (2) germination synchrony is dynamic, suggesting that the more dormancy decreases, the more synchrony is obtainable, (3) after-ripening and stratification can function as a synchronizing agent that regulates germination behavior. Brassica napus showed the most synchronous germination with the value of 3.14, while lower level of germination asynchrony was for Sinapis arvensis, with the asynchrony value of 2.25. After-ripening and stratification can act as a synchronizing factor through decreasing asynchrony level and increasing synchrony. Weed establish a firm relationship between dormancy cycling and germination synchrony patterns, ensuring their survival and reproductive strategies. By germinating in synchrony, which is accompanied by cycling mechanisms, weeds have more opportunities to persist. The synchrony model used in the present study predicts germination behavior and synchrony along the dormant to nondormancy continuum in weed seeds with physiological dormancy, suggesting a useful method for quantification of germination strategies and weed population dynamics.

Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and Object Detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for the sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.

As Christopher Alexander discovered, all space or matter – either organic or inorganic – has some degree of order in it according to its structure and arrangement. The order refers to a kind of structural character, called living structure, which is defined as a mathematical structure that consists of numerous substructures with an inherent hierarchy. Across the hierarchy, there are far more small substructures than large ones, while on each level of the hierarchy the substructures are more or less similar in size. In this paper we develop a new approach to representing geographic space as a hierarchy of recursively defined subspaces for computing the degree of order. A geographic space is first represented as a hierarchy of recursively defined subspaces, and all the subspaces are then topologically represented as a network for computing the degree of order of the geographic space, as well as that of its subspaces. Unlike conventional geographic representations, which are mechanical in nature, this new geographic representation is organic, conceived, and developed under the third view of space; that is, space is neither lifeless nor neutral, but a living structure capable of being more living or less living. Thus, the order can also be referred to as life, beauty, coherence, or harmony. We applied the new representation to three urban environments, 253 patterns, and 35 black-white strips to verify it and to demonstrate advantages of the new approach and the new kind of order. We further discuss the implications of the approach and the order on geographic information science and sustainable urban planning.

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff to patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of sensor data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different sensors by relabeling to the sensors with less samples resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority class are used to measure signal pattern to find matching sensors to relabel. By matching sensors between classes, two variation of relabeling are implemented specifically full and partial matching. The performance is evaluated using real world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the sensor data with our proposed relabeling method for data augmentation, we achieved higher minority class F1-score as compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected sensor data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

Obstructive sleep apnea (OSA) is multi-faceted world-wide distributed disorder exerting deep effects on the sleeping brain. In the latest years strong efforts have been dedicated to find novel measures assessing the real impact and severity of the pathology, traditionally trivialized by the simplistic apnea/hypopnea index. Due to the unavoidable connection between OSA and sleep we reviewed the key aspects linking the breathing disorder with sleep pathophysiology, focuings on the role of cyclic alternating pattern (CAP). Sleep structure, reflecting the degree of apnea-induced sleep instability, may provide topical informations to stratify OSA severity and foresee some of its dangerous consequences such as excessive daytime sleepiness and cognitive deterioration.

Background: Streptococcus agalactiae or Group B Streptococcal colonization of the gastrointestinal and genital tracts of pregnant women usually remains asymptomatic; even if it is the critical determinant of infection in neonates and young infants. It causes early and late onset of invasive Group B Streptococcus (GBS) disease manifesting as septicemia, meningitis and pneumonia. Now it is recognized as an important cause of maternal and neonatal morbidity and mortality in many parts of the world including Ethiopia where the magnitude of the problem has been little studied. The aim of this study was to assess the prevalence of GBS colonization, to identify associated risk factors and antimicrobial susceptibility pattern among pregnant women at selected health facilities of Wolaita Sodo Town, Southern Ethiopia. Methodology: A health facility based cross-sectional study design was conducted at WSUCSH & Wolaita Sodo Health Center from June to August, 2022. A total of 279 pregnant women who were in ANC follow up with at 35-37 weeks of gestation were included. For GBS isolation, recto-vaginal swabs were inoculated in 1ml Todd-Hewitt broth medium supplemented with 10μg/ml colistin and 15μg/ml nalidixic acid and followed by identification of isolates based on colonial morphology, gram stains, catalase reaction and CAMP tests. Antimicrobial susceptibility testing was performed using modified Kirby–Bauer disk diffusion method. All collected data were organized in Epi info 4.6.0.2, then transfer tabulated using SPSS version 20. Logistic regression analysis was used to see the association between variables. Finally, the p-value < 0.05 were considered statistically significant. Results: In present study, 279 pregnant mothers included and their age was between 15 to 38 years with a mean of 26.5 ± 4.5 years. Of all participants, the highest 120 (43.01%) were housewives. The overall carriage rate of GBS was 67(24.0%). GBS colonization showed a statistically significant association with college and above levels of maternal education [AOR= 6.610, 95% CI (1.724 - 25.349), P=0.01]. High susceptibility of GBS isolate was seen to Penicillin G & Chloramphenicol (92.5%) for each, Ampicillin, and Ceftriaxone (89.6%) each, following Vancomycin (74.62%), and Erythromycin (77%). Relatively, GBS showed high resistance to Tetracycline (88 %). Conclusion: In this study, the overall prevalence of GBS colonization was 24.0%. College and above educational level was statistically significant with GBS colonization. This study used to give attention to the management of pregnant women by making GBS culture one of the routine diagnoses during ANC follow-up and to prevent infection by early detection.

In the current manuscript, a novel software application for Rapid Damage Assessment of RC buildings subjected to earthquake excitation is presented based on Artificial Neural Networks. The software integrates the use of a novel deep learning methodology for Rapid Damage Assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post- earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for Rapid Visual Inspections of buildings prior to or after a strong seismic event or as a component of Building Information Modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of Pattern Recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events

In offshore drilling, accidents such as gas invasion, overflow, and kicks are inevitable, and they can escalate into blowouts and other catastrophic events that result in casualties and substantial economic losses. Therefore, maintaining drilling safety requires accurate monitoring of gas invasion and overflow. The majority of overflow monitoring methods currently utilized at drilling sites are threshold-based. However, monitoring parameters acquired during actual drilling operations frequently contain noise signals, making it difficult for threshold-based methods to strike a balance between improving accuracy and minimizing false positives. In this paper, Pattern Recognition-based Kick Detection (PRKD) is proposed as a novel method for diagnosing overflow in offshore drilling. This method utilizes the overflow evolution process by integrating multi-phase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. The PRKD effectively detects and monitors gas intrusion and overflow based on single parameters by analyzing the shape and wave characteristics of the curves. The case analysis demonstrates that the proposed method for monitoring drilling overflow achieves high precision while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, the PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations.

Land use and land cover (LULC) datasets for Jinan in 1992, 1998, 2002, 2006, 2011, 2017, and 2022 were developed from Landsat images using the Random Forest (RF) classification approach. The relationships between social-economic, political factors and time-series LULC data were exam-ined for the periods between 1992 and 2022. The results showed the effectiveness of using the RF classification method for LULC classification with time series of Landsat images. Combined with driving forces analysis, our research can effectively explain the detailed LULC change tra-jectories corresponding to different stages and give new insights into Jinan LULC change pat-terns. The results show a significant increase in impervious surface which opposite change to bare land which experienced a huge decline declined by 95%, due to urbanization and rapid in-crease of population. The driving forces behind these changes are related to population growth, economic development, and climate change. Moreover, the present research employed Principal Components Analysis (PCA) methodology in order to understand the relative significance of disparate driving factors. The analysis results prove that the economy (population, GDP) and climate change were the primary factors that have an obvious impact on land use/land cover changes and that the driving factors for impervious surface, bare land, woodland, farmland, and water were distinct. Government policies also have a substantial impact on LULC change as well, such as the Construction of Harmonious Jinan (COHJ). The results were helpful for better understanding the mechanisms of LULC change and can provide useful knowledge for effective land resource management and planning.

With the rapid development of network science, Turing pattern on complex networks has attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator-prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing pattern is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing pattern. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing pattern is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing pattern, which is not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing pattern and spatiotemporal pattern. We believe that these findings contribute to a better understanding of self-organization on complex networks.

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 HS were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE in the antero-posterior direction at τ 4 and τ 5, and MSE in the ML direction at τ 4 outperformed the other entropy measures in terms of discriminative ability and characterized the gait of swPD with 79%, 82%, and 78% probabilities, respectively, and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can be considered as a marker of gait variability and complexity in swPD.

Most research findings in Ghana and all over the globe lay much emphasis on the technical knowledge of plastics; especially giving preference to the biodegradable over the synthetic/Single-Use plastics. Questionnaires were administered to about 200 respondents in the Ho Municipality out of which 163 responses were filled and returned. Results showed that of all the variables tested only Legislative Framework has a meaningful impact on the behaviour of Ho indigenes. Awareness level: (Synthetic Plastics and Biodegradable Plastics) as well as Attitudinal Change do not influence people’s behaviour when using plastics. Findings from this study are further discussed and recommendations were proffered to instil eco-friendly behaviour among people.

The term `motor control´ encompasses a wide range of mechanisms thought to be implicated in the organization of movements. Their study, like that of any other scientific field, must use specific notions to get to grips with them. This review is therefore organized along a series of notions that are frequently used in motor control, and we will discuss them with particular emphasis on neurological conditions, which may disrupt normal motor functioning. We will start with a short description of the roles of space and time, in which movements take place. Subsequently we will deal with kinematics and kinetics (dynamics) of movements. Then, we will list the inputs to motoneurons (MNs), which, in different forms, convey signals from the central nervous system (CNS) to skeletal muscles and muscle spindles, including central pattern generators (CPGs), sensory inputs and supraspinal descending fiber systems. Some helpers in movement organization will be introduced, such as internal models and neuronal network models. A relatively brief section on sensory-motor learning will follow because learning abilities are required to endow the sensory-motor system with flexibility and adaptability. The major emphasis is placed on neurological processes, which result from genetic modifications, autoimmune diseases, neurodegenerative diseases, infections in the nervous system as well as vascular and traumatic lesions. These neurological diseases lead to a variety of impairments and symptoms, particularly movement derangements including cerebellar ataxia, spasticity, and (other) movement disorders such as Parkinson’s syndromes. The conclusion is that there is no unique top controller that controls ´motor control`.

The prevalence of obesity in Korean adults was 56.1% in 2019. This study aimed to find obesity phenotypes from the KNHANES(2015–2019) and determine the risk factors associated with each type. In 15,179 participants, three obesity phenotypes, high BMI (short heavy obesity, n=1,685), high waist circumference (WC) (tall abdominal obesity, n=549), and BMI/WC combination (heavy abdominal obesity, n=3,538), were compared to non-OB (n=9,425). Marital status, lower income and education levels were highly associated with abdominal obesity. Insufficient exercise or skipping breakfast were associated with the WC or BMI/WC obesity type. The BMI/WC risk was 3.25 times higher in males with high TG and 4.6–4.8 times higher in females with high FAS or TG, and the risk increased to 7-8 times higher in three combinations (HYP+HDLc+FBS) compared to the control. The K-Score to test balanced diets was lower in BMI/WC than in others. Low intakes of FI-4(fruits) in men and FI-2(meat), FI-3 (vegetables), and FI-5 (milk/milk products) in women with BMI/WC and WC types, but BMI type was not associated with diets. Compared to BMI, the BMI/WC or WC obesity type was an indicator of metabolically unhealthy obesity associated with obesogenic environments including food/diets pattern and unhealthy behaviors.

In Poland, the mountainous and hilly regions until recently were territories with unbalanced and poor diet quality, especially in early spring. Wild edible plants were or still are influential in supplementing a human diet. One of the earliest wild edible plants to appear in the growing season, characteristic of a broad swath of Europe from Ukraine to Germany, remains wild (bear) garlic (Allium ursinum L.). This paper assesses the impact of the vegetation site and preservation methods on selected parameters of the chemical composition of bear garlic. In addition, the connection between its genome pattern and chemical composition was assessed. The plants were collected in three locations in the Carpathian foothills, and the reference sample was plants collected in the lowlands. The site from which the raw material was harvested has a statistically significant effect on the chemical composition of the preserved leaves of bear garlic. The profiles of the analysed components differed in the material from a different site. Bear garlic leaves from mountainous areas had more analyzed components than those obtained from the lowland sites. Moreover, the material from the lowlands had a different genetic pattern from the leaves from the mountainous areas..

Knowledge on the health of banana trees is critical for farmers to profit from banana cultivation. Fusarium wilt and banana blood diseases (BDB), two significant diseases infecting banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have successfully caused a decline in crop yield as they destroy the trees, starting sequentially from the pseudostem to the fruits. The entire distribution of BDB and Fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using an unmanned aerial vehicle (UAV), combined with the reliable data field of infected trees. Vegetation and soil indices derived from a multispectral aerial photograph, such as normalized difference vegetation index, modified chlorophyll absorption ratio index, normalized difference water index (NDWI) and soil pH, may have to be relied on to explain the precise location of these two diseases. In this study, a random forest algorithm was used to handle a large dataset consisting of two models: the banana diseases multispectral model and the banana diseases spectral model. The results show that the soil indices, soil pH and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BDB is more extensive than that affected by Fusarium, if the variation of planted banana cultivars is not considered.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predict eventual BCR with an AUC of 0.97 and classifying cancer at an accuracy of 89.9%. This study showcases the application for a radiomics-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Nowadays, brain signal processing is performed rapidly in various brain-computer interface (BCI) applications. Most researchers focus on developing new methods for the future or improving the basic implemented models to identify the optimum standalone feature set. Our research focuses on four ideas. One of them introduces future communication models, and the others are for improving old models or methods. These are: 1) new communication imagery model instead of speech imager using the mental task: Due to speech imagery is very difficult, and it is impossible to imagine sound for all of the characters in all of the languages. Our research introduces a new mental task model for all languages that call Lip-sync imagery. This model can use for all characters in all languages. This paper implemented two lip-sync for two sounds, characters or letters. 2) New combination Signals: Selecting an inopportune frequency domain can lead to inefficient feature extraction. Therefore, domain selection is so important for processing. This combination of limited frequency ranges proposes a preliminary for creating Fragmentary Continuous frequency. For the first model, two s intervals of 4 Hz as filter banks were examined and tested. The primary purpose is to identify the combination of filter banks with 4Hz (scale of each filter bank) from the 4Hz to 40Hz frequency domain as new combination signals (8Hz) to obtain well and efficient features using increasing distinctive patterns and decreasing similar patterns of brain activities.3) new supplement bond graph classifier for SVM classifier: When SVM linear uses in very noisy, the performance is decreased. But we introduce a new bond graph linear classifier to supplement SVM linear in noisy data. 4) a deep formula recognition model: it converts the data of the first layer into a formula model (formula extraction model). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. The output of the last layer is the coefficients selected by different functions in different layers. Finally, the classifier extracts the root interval of the formulas, and the diagnosis does based on the root interval. For all of the ideas achieved the results of implementing methods. The results are between 55% to 98%. Less result is 55% for the deep detection formula, and the highest result is 98% for new combination signals.

The field of nephrology has recently directed a considerable amount of attention towards the stimulator of interferon genes (STING) molecule since it appears to be a potent driver of chronic kidney disease (CKD). STING and its activator, the cyclic GMP-AMP synthase (cGAS) are among the most relevant inducers that promote the expression of type I interferons (IFN-Is). These cytokines have been long recognized as part of the mechanism used by the innate immune system to battle viral infections; however, their involvement in sterile inflammation remains unclear. Mounting evidence pointing to the involvement of the IFN-I pathway in sterile kidney inflammation provides potential insights into the complex interplay between the innate immune system and damage to the most sensitive segment of the nephron, the glomerulus. The STING pathway is often cited as the cause of renal disease originating from non-infection related caused by the induction of IFN-I expression. However, other receptors have been implicated in this process, mainly by recognizing host-derived nucleic acids. This review explores the main endogenous inducers of IFN-I in glomerular cells, discusses what exposure to the main autocrine and paracrine cytokines has on these cells, and identifies the pathways that are implicated in the development of glomerular damage.

This systematic review provides a comprehensive neuropsychological analysis of psychotic spectrum disorders, including schizophrenia, bipolar disorder, and depression. It explores cognitive impairments in memory, attention, and executive function, and the relation between brain abnormalities and these deficits. The paper emphasizes the use of neuropsychological tests in diagnosis and early psychosis management, while also discussing interventions, the impact of dementia on psychosis, and the neurobiological patterns associated with these conditions. Additionally, it underlines the importance of childhood trauma in the development of psychosis and examines the effects of substance abuse and brain injury. Neuropsychological evaluations and targeted therapies are highlighted as crucial for the understanding, diagnosis, and treatment of psychotic disorders. The research further includes evidence of neurocognitive dysfunction in individuals with a high risk of psychosis and emphasizes the advanced brain aging in schizophrenia and bipolar disorder. The necessity of comprehensive clinical evaluations and targeted therapies for cognitive impairments is underscored throughout the research. The potential effects of dementia on individuals with psychosis, neurobiological patterns, the role of childhood trauma, the impact of psychosis in dementia patients, and proposed treatments are also explored in depth. Overall, the paper underscores the integral role neuropsychological knowledge plays in understanding, diagnosing, managing, and treating psychotic spectrum disorders.

Considering the current needs of industry 4.0, especially in the condition of systems linked to artificial intelligence, this multidisciplinary concept work and difficult to execute, proposes the main idea of recognizing patterns using mathematical concepts of simple access and logical resolution of great potential. The objective of this work is to demonstrate a process for pattern recognition by applying the Fibonacci concept to vibration signals of hybrid dynamical systems. For this to be possible, it was necessary to use the concepts of preprocessing in a vector for pattern recognition, together with the concepts of the golden rule of Fibonacci, finally, all applied in the theorem of Jeffcott Rotors. The general result, from a technical point of view, demonstrates positivity and relationship of patterns, because, considering the aspect of the location between the values of the experimental and numerical result, both signs have similarities.

The research proposes a self-directed paradigm with self-design using the existing technology with LMS modules, discussions, and self-tests. The research establishes its criteria for assurance of teaching and learning known as 7x2C for the compliance of content, and appearance along with other criteria for the analysis. The above technology of web and LMS is tested for a business course that is technology-oriented in creating an experiential learning system for Covid-19 awareness. Similarly, among other ideas, an environment for educating learners about diabetes and obesity has been designed. The project is known as Sunchoke with its theme of Grow, Eat, and Heal. The educators can use their technology and apply self-directed pedagogy to transform this approach to their customized ways by using their content and rubrics.

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and finally identified six main ridership patterns. We then developed a new built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis result revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type station, and centrality was only associated with employment-oriented hybrid type station. These results could inform the coordinated development of rail transit and land use, and the renewal and enhancement of the built environment in the pedestrian catchment area surrounding metro stations.

A stable match, an intercuspated maxilla-mandible relation is the universally accepted goal for dental restorative interventions, yet the underlying sensorimotor mechanisms to achieve this goal in the natural jaw closure remains to be elucidated. Dentists have intuitively regarded peripheral sensory innervation from tooth contacts as a significant control element for jaw muscle actions, both in the state of health and in cases of pathological dysfunction. However, therapeutical interventions for reshaping the dental arch anatomy and jaw relations have not always been successful. Repetitive masticatory chewing cycles, swallowing, talking, as well as volitional jaw-opening and -closing involve stereotyped neural commands from the central nervous system that are modulated in reaction to the sensory afferent feedback from the peripheral neurons and their sensor organs. The aim of this article is to describe, on a fraction of a second time-scale, the minuscule tilts and kinematic events of mandible during the making of a stable match with maxilla. Evidence of the importance of tooth contacts as the sensorimotor motivation of each of the successive kinematic steps of the “mandible-meets-maxilla-narrative” are discussed.

The accuracy of estimating changes in terrestrial water storage (TWS) using Gravity Recovery and Climate Experiment (GRACE) level-2 products is limited by the leakage effect resulting from post-processing and the weaker signal magnitude in adjacent areas. TWS anomaly in Dnieper River basin, where characteristic with medium scale and adjacent weak TWS anomaly, is estimate in this work. Two categories of leakage error repaired method (including forward modeling, Data-driven, single and multiple scaling factor) are employed. Root mean square error (RMSE) and Nash-Sutcliffe Efficiency (NSE) are used to evaluate the efficiency of methods. Compare to independent methods, TWS anomaly inverted by multiple scaling factor depending on CPC Soil Moisture model is more accurate in terms with RMSE 2.32 and NSE 0.88, which scaling factors corresponding to trend term 1.02, annual term 1.04, and semi-annual term 1.14. Further, comprehensive climate insights behind of the TWS anomaly were confirmed. The temperate continental climate of this River basin is shown, according to the variation of TWS anomaly in spatial domain. Snowmelt is significant role in TWS anomaly of Dnieper River basin, which is accord with the precipitation record and the 14 years temperature spatial distribution of February. Overall, we compare TWS anomaly recovered by single and multiple scaling method, the leakage signal originates mainly from semi-annual term of the TWS anomaly.

This paper presents four key results in the philosophy of language. Firstly, it distinguishes between partial and consistent assertion of a sentence, and introduces the concept of an equivocal sentence, which is both partially asserted and partially denied. Secondly, it proposes a novel definition of truth, stating that a true sentence is one that is consistently asserted. This definition avoids the Liar paradox, does not restrict classical logic, and applies to declarative sentences in the language used by any particular person. Thirdly, the paper introduces an epistemic model of language, called assertional language, which formalizes the definition of truth. Finally, it provides an argument for the falsity of so-called Liar sentences. The proposed definition of truth can be viewed as a formal account of the correspondence theory. The epistemic model is a powerful concept that allows for combining different languages in a meaningful way. This model is uniquely capable of reflecting on epistemic inconsistencies, such as logical paradoxes, in a consistent manner and without resorting to non-classical solutions.

This article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it can’t get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like), solving a given cognitive problem. DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, it is shown that DBC-1 can solve the following cognitive problems: “similarity indexing between seemingly different but inherently identical objects” and “recognizing regions of an image separated by a complex boundary.” DBC-2 can solve the following cognitive problem: “pattern recognition when the relevant information is insufficient.” Smart manufacturing-based systems (digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and, thereby, can enhance its biologicalization.

Recanalization poses challenge in coil embolization for cerebral aneurysms. Establishing predictive models for post-embolization recanalization is important for clinical decision-making. However, conventional statistical and machine learning (ML) models may overlook critical parameters during the initial selection process. In this study, we automated the identification of significant hemodynamic parameters using a PointNet-based deep neural network (DNN), leveraging their inherent 3D features. Further feature analysis was conducted utilizing saliency mapping, an explainable artificial intelligence (XAI) technique. The study encompassed the analysis of velocity, pressure, and wall shear stress (WSS) in both pre- and post-coiling models derived from computational fluid dynamic (CFD) simulations for 59 aneurysms. Velocity was identified as the most pivotal parameter, supported by the lowest P-value from statistical analysis and the highest area under the curves (AUROC)/precision-recall curves (AUPRC) values from DNN model. Moreover, visual XAI analysis revealed robust injection flow zones with notable impingement points in pre-coiling models, as well as pronounced interplay between flow dynamics and the coiling plane were important 3D features in identifying the recanalized aneurysms. The combination of DNN and XAI was found to be an accurate and explainable approach not only at predicting post-embolization recanalization but also at discovering unknown features in the future.

Many studies have focused on the impacts of rainfall duration and intensity while overlooking the role of rainfall patterns on intensive tillage erosion in hilly agricultural landscapes. The objective of this study was to determine the combined effects of rainfall patterns and intensive tillage erosion on surface runoff and soil loss on sloping farmland in the purple-soil area of China. Five simulated rainfall patterns (constant, rising, falling, rising-falling, and falling-rising) with the same total precipitation were designed, and the intensive tillage erosion treatment (IT) and no-tillage treatment (NT) were subjected to simulated rainfall using rectangular steel tanks (2 m × 5 m) with a slope of 15°. To analyse the differences in the hydrodynamic characteristics induced by tillage erosion, we calculated the flow velocity (V), Reynolds number (Re), Froude number (Fr), and Darcy-Weisbach resistance coefficient (f). The results indicate that significant differences in surface runoff and soil loss were found among different rainfall patterns and stages (P < 0.05). The falling pattern and falling-rising pattern had faster runoff-initiating times and larger sediment yields than those of the other rainfall patterns. f varied from 0.30 to 9.05 for the IT and 0.48 to 11.57 for the NT and exhibited an approximately inverse trend to V and Fr over the course of the rainfall events. Compared with the NT, the mean sediment yield rates from the IT increased the dynamic range of 8.34%–16.21% among different rainfall patterns. The net contributions of the IT ranged from 2.77% to 46.39% on surface runoff and 10.14%–78.95% on soil loss on sloping farmland. Surface runoff and soil loss were positively correlated with rainfall intensity, V, and Fr but negatively correlated with f irrespective of tillage intensive (P < 0.05). For varying-intensity rainfall patterns, soil and water loss fluctuated during rainfall events, suggesting that the changes in rainfall intensity and tillage intensity would result in drastic variations in soil hydrological characteristics and sediment transport mechanisms. The time sequences of rainfall intensity in each rainfall pattern significantly affected surface runoff, soil erosion, and their contribution rates to total soil and water loss. Moreover, tillage erosion effects on soil and water loss were closely related to rainfall patterns in hilly agricultural landscapes. Our study not only sheds light on the mechanism of tillage erosion and rainfall erosion but also provides useful insights for developing tillage-water erosion prediction models to evaluate soil and water loss on cultivated hillslopes.

The popularity of online retail is followed by decreasing transactions at shopping centres. Leisure and companionship trips to restaurants and cafés are replacing the frequency of intra-urban shopping trips. Several studies demonstrated a notable trend in urban commercial areas, wherein functionality has shifted from shopping centres to primarily focused on restaurants and cafes. Leisure and companionship places in the urban south have greater diversity than formal restaurants and cafés. This article employs a comprehensive framework to examine the categorization of restaurants and cafés in the urban south, referred to as food services. This study aims to investigate the spatial pattern of food service in Surabaya and ascertain whether there is a concentration of all food service variants around existing shopping centres. This research employs the POI of food service units gathered through web scraping from Google Maps. Applying the k-means cluster analysis demonstrates that the spatial pattern of food services is categorized into several distinct clusters. This article discloses five types of urban spaces characterized by varying compositions of food service variants within them. These spaces include the city centre, sub-city centre, areas along main roads, residential neighbourhoods, and peri-urban areas.

Any malocclusion can be associated with various face development patterns, including hyper, neutral and hypodivergent growth patterns. We measured face height ratios, angle formed by Y axis to SN and growth patterns in various malocclusions in a group of patients from Timis county. The pretreatment digital cephalograms of 350 participants were split into three groups based on Angle's classification of dentoalveolar malocclusion. Every cephalogram was digitally traced and analyzed using a composite cephalometric method analysis utilizing the WebCeph imaging cephalometric program, which included two linear and one angular. In general, patients from Timis county have a hypodivergent development pattern. Hypodivergent development pattern predominated in group I, II, and III. Group I hyperdivergent 5.14%, normodivergent 16.01%, hypodivergent 78.85%. Group II hyperdivergent 2.78%, normodivergent 14.44%, hypodivergent 82.78%. Group III hyperdivergent 10.53%, normodivergent 10.53%, hypodivergent 78.94%. In all three groups, the value of the angle formed by Y axis to SN is within normal limits group I 65.4 ±4.34°, group II 63.87± 3.86° and group III 64.66 ±5.28°. A strong negative correlation was found between angle: Y axis to SN and facial height ratio (r= -0,766; p &lt;0.001). In all kinds of malocclusion, patients from Timis county show primarily a hypodivergent development pattern.

Mountain ecosystems are crucial for global biodiversity conservation. However, their landscape features are constantly changing owing to urban expansion. Understanding the relationships between biotic communities and landscape features is essential for biodiversity conservation. This study aimed to examine the effect of land-cover type on avian communities in Lishui, a mountainous urban area in eastern China. Avian surveys were conducted using a total of 168 line transects across different land-cover types once per season from December 2019 to January 2021. We assessed the diversity of bird communities by calculating various metrics at both taxonomic and functional levels. Among the land-cover types measured, woodland, built-up land, cultivated land, and water bodies significantly influenced bird community diversity and composition. Species richness, species abundance, and functional richness were negatively correlated with the proportion of woodland but positively correlated with the proportion of non-natural land cover, such as built-up and cultivated land. In contrast, functional evenness was positively correlated with the proportion of woodland and grassland but negatively correlated with the proportion of non-natural land cover. Land-cover type also exhibited significant correlations with avian functional characteristics such as diet, foraging strata, and body mass, thereby influencing the overall community structure. Our results indicated that mountainous landscape patterns substantially affect avian communities. Different land-cover types possess varying resource endowments that affect the distribution of avian species. Therefore, urban landscape planning in mountainous areas should carefully consider the various functions provided to organisms by different types of land cover to promote biodiversity.

With the development of free trade port in Hainan Island, the construction of tourist roads around the island is also in full swing. Under the weather conditions of strong typhoon and rainstorm in Hainan, the highway cutting slope built on the coastal weak sandy terraces has strong sand loss and is easy to be scour by rainfall. MICP green spray irrigation solidification technology is used to strengthen the sandy cutting, and pore water pressure monitoring is carried out on the slope model during MICP solidification and rainfall scour. Combined with the model pore water pressure and flow slip failure pattern, dynamic analysis was carried out. The results show that MICP sprinkler irrigation technology can solidified the surface of the slope model in a short time, and the cementation depth of the model reaches 4cm after three sets of rotation reinforcement. The surface reinforcement effect is good, and the sand samples are closely connected. Under the erosion effect of simulated rainfall intensity, the sand loss of the slope is weakened, and no sand binding damage occurs, and the integrity is enhanced. Due to the cementation between sand grains, most of the rainfall was converted into runoff, and the slope slid after 150s. When the slope began to slip, the leading edge of the slope model lost sand and unloaded, and the failure mode was graded creep slip failure. Finally, the slope was divided into several blocks due to the continuous expansion of cracks after the slope failure. The erosion stability of the sandy slope under heavy rains is optimized and the sand loss is prevented effectively. In this study, a new method of MICP remediation techniques is proposed, which provides a new test basis for the application of MICP technology in practical engineering.

The goal of this paper is to fabricate innovative diaphragm headphones using graphene oxide paper (GOP) and GOP/epoxy nanocomposites (GOPC). Initially, graphene oxide suspension is fabricated, and the vacuum filtration method is adopted to make GOP. Then vacuum bag molding is used to fabricate GOPC from GOP. Hot pressing and associated molds are adopted to fabricate line-indented (GOPC-L) or curve-indented patterns (GOPC-C) on the GOPC. The performance of one kind of GOP and three kinds of GOPC diaphragm headphones is analyzed based on their sound pressure level (SPL) curves achieved by the Soundcheck measurement system. There are four important processing parameters that will influence the performance of the diaphragm, including material type GOP versus GOPC, indented pattern type, sonication time on suspension, and graphene weight fraction in suspension. Compliances of various diaphragms are measured by the Klippel LPM laser measurement system. The results indicate that effects of sonication time and graphene weight fraction on SPL of GOP and GOPC headphones are in reverse, and this is associated with their difference on compliance (modulus), mass, damping ratio, and microstructure uniformity. Either GOPC-L or GOPC-C seems to improve the microstructure of the GOPC, and leads to better SPL performance. The correlation between the previous four factors and SPLs of four kinds of diaphragm headphones is proposed by using scanning electron microscope (SEM) to examine the microstructure of these diaphragms.

This work proposes a methodological approach applied to ephemeral gravel-bed streams to verify the change in the magnitude and frequency of hydrological events affecting the morphological dynamics and sediment budget in this type of channel. For the case study the Azohía Rambla, located in southeastern Spain, was chosen, emphasizing the research on two reference riverbed sections (RCRs): an upper one, with a predominance of erosion, and a middle one, where processes of incision, transport, and deposition converge. First, this approach focuses on relationships between peak discharges and sediment budgets during the period 2018 to 2022. For this purpose, water level measurements from pressure sensors, a One-Dimensional Hydrodynamic model, and findings from comparative analyses of high-resolution differential digital elevation models (HRDEM of Difference - HRDoD) based on SfM-MVS and LiDAR datasets were used. In a second phase the GeoWEPP model was applied to the period 1996-2022 in order to simulate runoff and sediment yield at the event scale for the watersheds draining into both RCRs. During the calibration phase a sensitivity analysis was carried out to detect the most influential parameters in the model and to confirm its capacity to simulate peak flow and sediment delivery in the area described above. Values of NS (Nash-Sutcliffe efficiency) and PBIAS (percent bias) equal to 0.86 and 7.81%, respectively, were found in the calibration period, while these indices were 0.81 and -4.1% in the validation period. Finally, different event class patterns (ECPs) were established for the monitoring period (2018-2022), according to flow stage and morphological channel adjustments (overtopping, bankfull and sub-bankfull, and half-sub-bankfull), and then retrospectively extrapolated to stages of the prior simulated period (1996-2018) from their typical sequences (PECPs). The results revealed a significant increase in the number of events and PECPs leading to lower bed incision rates and higher vertical accretion, which denotes a progressive increase in bed armoring and bank erosion processes.

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system's response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing impressive performance. These important results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.

Background: Poisoning is a challenging medical condition facing health care providers, as it usually results in serious outcomes in terms of morbidities or mortalities. Poisoning is a challenging medical condition facing health care providers, as it usually results in serious outcomes in terms of morbidities or mortalities. The lack of or inadequate information about the circumstances, substances and populations at risk are considered barriers to the actual prevention of poisoning and effective intervention strategies in Saudi Arabia. The aim of the current study was to describe the pattern and determinants of chemical and drug overdose poisoning in Makkah.Subjects and methods: All reports of chemical and drug overdose poisoning delivered to the Environmental Health and Occupational Safety Department in Makkah over two years (2018-2019) were reviewed.Results: The total number of cases reported in 2018 was 209 cases, while it reached 42 cases in 2019. The overwhelming majority of the cases were Saudi (91.6%), with a slight predominance of males (57.4%) over females (42.6%). Generally, almost two-thirds of the cases occurred in children below 13 years (61.4%), most of the poisoning cases resulted from drug overdose (63.3%), while 34.7% were chemical poisoning. The most common drug poisoning was attributed to analgesics followed by antiepileptics, antihypertensive drugs, antipsychotics and antimicrobials, while cleaning and detergent agents were the most commonly poisoned chemicals. The majority of these agents were taken by the oral route. Children's chemical poisoning was significantly higher than that in older age groups (p<0.05). All intentional poisoning occurred in adults; the great majority of them (88.9%) used drug overdosing rather than chemical substances.Conclusion and recommendations: The current study results are consistent with most of the previous studies carried out in different regions of Saudi Arabia. Further in-depth studies are needed to elaborate other social and demographic factors associated with chemical and drug overdose poisoning. Public health plans, policies and legislations should be implemented to reduce these factors. Intensive supervision of children is needed. Suicidal poisoning patients should seek psychiatric help to limit the possibility of attempting suicide again in the future. Furthermore, efforts are needed to plan and implement health education programs about the factors proven in the current study to be significantly related to chemical and drug overdose poisoning.

The main objective of this study is to conduct a bibliometric analysis of scholarly publications of Authorship Pattern. The present study covers 1723 research papers published in the area of authorship pattern and indexed in Scopus database from the year 2013 to 2022. These research publications considered for the present study have been analysed based on their year wise growth, pattern of authorship, times citations, type of publication, most productive publication source as well as countries and institutions. The study shows the positive growth of the literatures with collaborative authorship pattern and good citation status. This original research paper will be helpful to the researchers of library and information science, especially who are working in the area of bibliometrics studies.

In general sound waves propagate radially outwards from a point source. These waves will continue in the same direction, decreasing in intensity, unless a boundary condition is met. To arrive at a universal understanding of the relation between frequency and wave propagation within spatial boundaries, an analysis and comparison is made between the results of Chladni plate experiments and Chladni plate simulations. We consider a finite range of frequencies and plate geometries and present a model that has been shown to successfully recreate the nodal line patterns to a first approximation. We discuss the benefits of such a model and the future work necessary to develop the model to its full predictive capacity.

Fermatean fuzzy sets (FFSs) are a versatile tool for handling uncertain problems and have shown great effectiveness in practical applications. However, many existing Fermatean fuzzy similarity measures exhibit counter-intuitive situations, making it challenging to accurately measure the similarity or difference between FFSs. To address this issue, several similarity and distance measures for FFSs are proposed, inspired by the Tanimoto similarity measure. The properties of the proposed measures are also explored, along with several comparative examples with existing measures for FFSs, which illustrate their superior performance in processing fuzzy information from FFSs. The proposed measures have effectively overcome the counter-intuitive challenges posed by many existing measures and significantly outperforms existing measures in differentiating different FFSs. Furthermore, we demonstrate the practical applicability of the proposed measures in solving pattern recognition, medical diagnosis and multiple-attribute decision-making problems.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

Background: The AWaRe tool was set up by the World Health Organization (WHO) to promote the rational use of antimicrobials. Indeed, this tool classifies antibiotics into four groups, Access, Watch, Reserve and not-recommended antibiotics. In Republic Democratic of Congo, data on antibiotic dispensing (prescribing) by health professionals according to the AWaRe classification are scarce. In this research work, we aimed to explore antibiotic dispensing pattern from health professionals according to the WHO AWaRe classification to strengthen the national antimicrobial resistance plan. Methods: For this purpose, a survey was conducted from July to December 2022 in the district of Tshangu in Kinshasa. From randomly selected drugstores, drug-sellers were interviewed and randomly selected customers attending those drugstores were included in the study for medical prescriptions collection. The prescribed antibiotics were classified into the Access, Watch, Reserve and not recommended antibiotics group and by antibiotics number by prescription among pharmacies surveyed. Results: Of 400 medical prescriptions collected, 301 (75.25%) contained antibiotics. Of 301 prescriptions containing antibiotics, 164 (164/301; 54.5%) contained one antibiotic, 117 (117/301; 38.9%) two antibiotics, 15 (15/301; 5%) three antibiotics and 5 (5/301; 1.6%) 4 antibiotics. Out of the total of 463 antibiotics prescribed, 169 (169/463; 36.5%) were from the Access group, 200 (200/463; 43.2%) from the Watch group and 94 (94/463; 20.3%) from not recommended antibiotics group, respectively. Based on the anatomical, therapeutic and chemical (ATC) classification, it can be seen that third generation cephalosporins contained 34.33% of the prescribed antibiotics, followed by penicillins 17.17%, macrolides 7.63%, aminoglycosides 7. 36% and Imidazoles 7.36%, thus accounting approximately for 74% of the classes of antibiotics prescribed. Of the 463 antibiotics prescribed, the most frequently prescribed antibiotics were Ceftriaxone (21.38%), Amoxicillin (11.01%), Gentamycin (5.61%), Amoxicillin-clavulanic acid (5 .61%), Azithromycin (4.97%) and Metronidazole (4.75%), thus accounting for approximately 54% of all antibiotics prescribed. Conclusion: These results highlight the importance of strict implementation of the national plan to combat antimicrobial resistance and the need to train health workers in the correct application of the WHO AWaRe classification.

Currently, Pythagorean fuzzy sets (PFSs) have been widely applied in various fields due to their substantial advantages in expressing and dealing with uncertainty. However, measuring the similarity and difference between PFSs effectively remains an unresolved issue. Inspired by Tanimoto similarity, we propose a novel set of similarity and distance measures for PFSs. We delve into the theoretical properties of the proposed measures and compare it with existing PFSs measures. Numerous numerical examples validate their rationality and effectiveness. Furthermore, our experimental findings suggest that in contrast to existing measures, the introduced measures successfully circumvent various counter-intuitive issues encountered by current measures, and yield more pronounced outcomes in the discrimination of different fuzzy sets. This enhances the uniqueness and superiority of our measures. Finally, we developed two decision models based on the proposed measures and validated their applicability in three applications.

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Historically, viral hepatitis is a considerable public health problem in Central Asian countries, which may have worsened after dissolution of the Soviet Union. However, up-to-date seroepidemiological studies are lacking. The aim of the present study was, therefore, to provide current estimates of seroprevalence of viral hepatitis in Kyrgyzstan, one of the economically least developed countries in the region. We conducted a population-based cross-sectional study in 2018 in the capital of Kyrgyzstan, Bishkek (n=1075). Participants, children and adults, were recruited from an outpatient clinic. Data were collected during face-to-face interviews. A blood sample (6 ml) was collected from each participant and tested with ELISA for the presence of serological markers for five viral hepatitides (A, B, C, D and E). Poststratification weighting was performed to obtain nationally representative findings. The overwhelming majority of the study participants were positive for anti-HAV (estimated seroprevalence, 75.3%; 95% confidence interval: 72.5–77.9%). The weighted seroprevalence estimates of HBsAg, anti-HCV and anti-HDV were 2.2% (1.5–3.3%), 3.8% (2.8–5.1%), and 0.40% (0.15–1.01%), respectively. Anti-HEV seropositivity was 3.3% (2.4–4.5%). Of the 33 HBsAg positive participants, five (15%) were anti-HDV positive. Our study confirms that Kyrgyzstan remains a high endemic country for hepatitis virus A and C infections. However, seroprevalences of HBV and HDV were lower than previously reported, and based on these data, the country could potentially be reclassified from high to (lower) intermediate endemicity. The observed anti-HEV seroprevalence resembles the low endemicity pattern characteristic of high-income countries.

The aim of this study was to examine the relationship of the distance between the apex tip of the upper central tooth (U1A) and the anterior nasal spine (ANS) and the nasal floor (NF) with the vertical cephalometric values using cone-beam computerized tomography (CBCT). A total of 122 patients who applied to Department of Orthodontics between January 2011-June 2019 were included. On the CBCT’s, distances between the U1A and the NF and ANS were measured. Statistical significance was considered as p&lt;0.05. Of 122 individuals 73.8% (n=90) were female and 26.2% (n=32) were male with an mean age of 22,8±3,3 years. A statistically significant moderate positive correlation was found between the NF-U1A mean values and the measurements of N-Me, ANS-Me and ANS-Gn, S-Go, and N-ANS measurements (p &lt;0.01). A statistically significant positive correlation was found between the ANS-U1A mean values and Ar-Go-Me, total posterior angles, N-Me, SN/GoGn and Y-axis angle, ANS-Me and ANS-Gn (p &lt;0.01). In this study, we found that the distance of U1A from ANS and NF points was related to orthodontic vertical direction parameters. It was demonstrated that the ANS-U1A and NF-U1A points can serve as reference points for identifying the orthodontic vertical growth pattern on CBCT scans.

With the increase of global human activities, a large number of technogenic magnetic particles (TMPs) generated by industrial activities such as coal combustion, traffic and so on enter sediments on continental shelves. However, there is currently a lack of understanding of the spatiotemporal distribution of TMPs on continental shelves, as well as the sedimentary effects they cause. This review work comprehensively analyzes the source, morphology, composition, and spatiotemporal distribution characteristics of TMPs. Then it elucidates their magnetic contribution to sedimentary environments in continental shelf seas, as well as the influence of heavy metals and active iron. At the same time, the existing problems and future development directions of TMPs on continental shelves are summarized.

Different bone bruise patterns observed on magnetic resonance imaging (MRI) after non-contact anterior cruciate ligament (ACL) rupture and lateral patellar dislocation may indicate different injury mechanism of knee. In order to investigate the typical bone bruising patterns after ACL rupture and patellar dislocation. In this study, 77 ACL rupture and 77 patellar dislocation patients’ knee MR images with bone bruises between August 2020 and March 2022at our institution were selected and analyzed, respectively. Sagittal and transverse planes images were used to determine the bone bruise location in the lateral-medial and superior-inferior directions of the MR images. The presence, location, and intensity of the bone bruises within specific compartments of the femur and tibial after ACL rupture and patellar dislocation were recorded. Relative bone bruise patterns of the ACL rupture and patellar dislocation were classified and analyzed. Results showed that there were 4 kinds of bone bruise (1-,2-,3-,4- bone bruises) patterns after ACL rupture. The most common two patterns after ACL rupture were 3- bone bruises (including lateral side of femur and both lateral and medial side of tibia, LF+BT; both lateral and medial side of femur and lateral side of tibia, BF+LT, medial side of femur and both lateral and medial side of tibia, MF+BT) followed by 4- bone bruises (both lateral and medial side of femur and tibia, BF+BT), 2- bone bruises (LF+LT, lateral side of femur and tibia; MF+LT, medial side of femur and lateral side of tibia; LF+MT, lateral side of femur and medial side of tibia; MF+MT, medial side of femur and tibia; 0+BT, both lateral and medial side of tibia;), and 1- bone bruise (0+LT, only lateral side of tibia). There was only 1 pattern bone bruise (latera femur and medial patella bone bruise) of patellar dislocation, the most common pattern of patellar dislocation was in the inferior medial patella and lateral anterior inferior femur. The results suggest that the pattern of bone bruise after ACL rupture and patellar dislocation is completely different. There were 4 kinds of bone bruise patterns after non-contact ACL rupture while there was only 1 kind of bone bruise pattern after patellar dislocation bone bruise patients which was in the inferior medial patella and lateral anterior inferior femur.

This research delves into the realm of predictive modeling within artificial intelligence (AI) and introduces an innovative ensemble approach designed to enhance prognostic precision. The proposed methodology, characterized by its ebullient nature, leverages the collective wisdom of diverse predictive models, synthesizing a comprehensive and robust framework. The pursuit of parsimony are paramount, underscoring the need for efficient model architectures that balance predictive acuracy and computational efficiency. Our study meticulously explores the amalgamation of diverse algorithms, encapsulating the intrinsic complexity of real-world phenomena. We navigate the intricate landscape of feature selection, algorithmic diversity, and model combination, fostering a holistic understanding of the prognostic process. The ebullient ensemble emerges as a dynamic solution, harnessing the synergy of constituent models while mitigating the risk of overfitting. To validate the efficacy of our approach, extensive experiments are conducted on diverse datasets, spanning domains such as healthcare, finance, and natural language processing. Comparative analyses demonstrate the superior predictive performance of our ensemble model in comparison to individual algorithms and traditional ensemble methods. Additionally, we delve into interpretability aspects, elucidating the underlying mechanisms that contribute to the model's prognostic prowess. Our research advances the frontier of predictive modeling in AI, offering a nuanced and sophisticated paradigm for prognostication. The ebullient ensemble approach stands as a testament to the synergy achievable through the amalgamation of diverse predictive models, providing a pragmatic and parsimonious avenue for enhanced prognostic acuracy in artificial intelligence applications.

A drinking strategy aiming to replace a given percentage of the sweat losses incurred during exercise should result in reproducible fluid intake volume and, hence, fluid balance from one exercise session to the other performed under similar scenarios. Whether this may also be the case with ad libitum drinking during exercise is unclear. We characterized the repeatability of ad libitum fluid intake during repeated 1 h exercise sessions and examine its effect over time on fluid balance and selected physiological functions and perceptual sensations. Twelve (3 women) healthy individuals participated in this study. At weekly intervals, they completed 4, 2 x 30 min walking/jogging exercise bouts (55% VO2max, 40°C, 20-30% relative humidity) interspersed by a 3 min recovery period. During exercise, participants replaced water (20°C) ad libitum. There were no significant differences among the 4 exercise sessions for absolute fluid intake volume (~ 1000 mL · h-1), percent body mass loss (~ 0.4%), sweat rate (~ 1300 mL · h-1) and percent of sweat loss replaced by fluid intake (~ 80%). Heart rate, rectal temperature and perceived thirst and heat stress did not differ significantly between the first and fourth exercise session. Perceived exertion was significantly lower during the fourth vs. the first exercise session, but the difference was trivial, &lt; 1 arbitrary unit. In conclusion, ad libitum fluid intake during 4 successive identical 1 h walking/jogging sessions conducted in the heat will result in similar fluid intake volumes and perturbations in fluid balance, heart rate, rectal temperature and perceived thirst, heat stress and exertion.

: Human settlements have historically thrived near rivers for navigation, trade, and availability of water supply and resources. Night light data, representing economic activities provide a novel approach to studying the interactions between human activity and rivers over time. Here, we use the Defense Meteorological Satellite Program (DMSP) stable night light data from 2000-2013 as a proxy for human presence and activities to quantify the statistical relationships between night light presence and intensity in the Indus Basin, Asia. We test how these data are affected by proximity to trunk channels and by channel type (single/multi-thread) in the study area. We find that night light presence is enhanced by 26% within a 0 to 5 km proximity range of the Indus River and its tribu-taries, relative to the basin as a whole. We interpret this to represent increased human presence and activity within this zone. However, the mean intensity is lower near the river and higher away from the river signifying denser settlements, such as towns and cities which are preferentially located away from the Indus and its tributaries. Moreover, the enhancement of lit pixels signifying human presence and activities is increased by 18% near single-thread sections of the Indus River, compared to segments of the Indus displaying multi-thread morphologies. We suggest that this is due to the enhanced stability of single-threaded channels, relative to mobile multi-threaded channel reaches. This study demonstrates how night lights are an important tool to constrain the relationship between human presence and river dynamics in large catchments such as the Indus, and we suggest this data will have an important role in assessing differential flood spatial and social vulnerability at a regional scale.

Arboviruses are infections caused by arboviruses that may be either symptomatic or asymptomatic, according to individuals' resistance and immune response, which dietary patterns can influence. Thus, this study aimed to investigate the possible association between the occurrence of clinical manifestations of arboviruses and the dietary pattern of the population studied. The study was conducted in a rural population in the metropolitan area of Belém, Pará State, Brazil. The sampling consisted of 95 individuals during a febrile outbreak. Two investigations were conducted: clinical-epidemiological-dietary and laboratory investigation based on blood samples taken for arbovirus analysis. Out of the 95 individuals studied, more than half (57.89%) showed clinical manifestations. Following the laboratory serology (HI and IgM ELISA) analyses, viral isolation in C6/36 cells, and viral genome detection (RT-qPCR), the final diagnosis of the cases was 47.37% Oropouche Fever, 2.11% Oropouche Fever and Chikungunya Fever, and 1.05% Mayaro Fever. Regarding dietary pattern, as for the comparison analyses, statistical significance was observed between dietary pattern and classification regarding the presence of symptoms (p<0.05); after the correspondence analysis, symptomatic cases were more associated with a bad dietary pattern, while asymptomatic cases were more associated with a good dietary pattern.

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard including signature-based and machine-learning (ML) based systems. More recently, researchers are considering deep learning (DL) based anomaly detection approaches. Most proposed works in this research field aimed to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, achieving lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns. Based on this we devise an anomaly detection for IDS using a lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSL-KDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have also demonstrated the excellent generalization of the proposed lightweight CNN model, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed version.

Ground penetrating radar (georadar, GPR), widely used in geology, archeology and road construction, is an efficient tool for searching and studying subsurface objects and structures. When planning GPR missions, it is necessary to predict the resolution of the device and the achievable probing depth. The article discusses the methods and results of assessing these characteristics of the Loza-V and Loza-N radars obtained in the course of archaeological and geographical expedition works.

The (numerical) solution of Ordinary Differential Equations (ODEs) problems is of paramount relevance, ODEs system being an ubiquitous mathematical formulation of many physical phenomena (such as those involved in fluid dynamics, chemistry, biology, evolutionary-anthropology, ...): almost every dynamici phenomenon can be modeled by means of an ODEs system. The present paper is the first manifesto of FOODIE, a library aimed to numerically solve ODEs problems by means of a clear, concise and efficient abstract interface. FOODIE, meaning Fortran Object oriented Ordinary Differential Equations integration library, has manifolds aims: to provide a set to built-in numerical schemes that are accurate, robust, validated and efficient and to allow easy application of these schemes to (almost) all ODEs problems by means of an effective Abstract Calculus Pattern. The key idea is to allow the same solver-implementation to be applied to all ODEs problems thus avoiding the re-implementation of the ODEs solver for each different ODEs problem: code re-usability is consequently maximized, FOODIE being a general robust framework. Besides, the same framework also allows rapid development of new ODEs solvers due to the high abstraction level of the library itself. The present paper is the first announcement of FOODIE project: the current implementation is extensively discussed and its capabilities are proved by means of tests and examples.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

Pregestational and gestational diabetes mellitus are relevant complications of pregnancy and antidiabetic drugs are prescribed to obtain glycemic control and improve perinatal outcomes. The objective of this study was to highlight the prescription pattern of antidiabetics before, during and after pregnancy in Italy and to evaluate its appropriateness. A multi-database cross sectional population study using a Common Data Model was performed. In a cohort of about 450,000 women, the prescribing profile of antidiabetics seemed to be in line with the Italian standards for the treatment of GDM which currently do not recommend the use of oral antidiabetics and non-insulin injection, even if a small share of new users (up to 3.8% in the third trimester) used oral antidiabetics. A substantial variability in the prescription pattern was observed among the Italian regions considered: the highest increase was registered in Tuscany (4.2%), Umbria (3.5%) and Emilia-Romagna (2.6%) while the lowest in Lombardy (1.5%), Veneto (1.7%) and Apulia (1.7%). Analysing the prevalence of use of antidiabetics between foreign women we observed that the consumption during pregnancy was higher than Italians (second trimester: 1,8% vs 0,9%, third trimester: 3,6% vs 1,8%).

Anthropogenic chemicals are fundamental to our standard of living in modern society. Unfortunately, some chemicals are persistent and can enter waste streams and ultimately the environment. Commonly used household products including pharmaceuticals and personal care products are an important source of contaminants. The aim of this study was to develop an “Up-the-pipe Solutions” framework to raise awareness about the presence and potential risks of chemicals found in household products to reduce their levels in waste streams or substitute them with less harmful alternatives. This approach based on The Natural Step framework and the concept of essentiality recognises the importance of engaging the community to raise awareness around the consequences of our daily activities and behavioural patterns that can lead to the release of persistent contaminants in main waste streams from the kitchen and bathroom and green wastes we generate.