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Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions. [ABSTRACT FROM AUTHOR]

The tensile failure of complex branching cracks will cause irreversible damage to the structures. In this paper, the effects of the crack orientations, the local branch lengths, and the different load forms on the crack propagation pattern and the bearing capacity of structures are investigated based on the phase field method and experimental research method. The results show that the bearing capacity and propagation patterns of the specimens change significantly with the change of the orientation of the Y-shaped crack under tensile load. The local branch length of the crack only affects the sequence in which the regional crack reaches the final failure and the bearing capacity of the specimen. Under the dynamic impact load, the bifurcation phenomenon occurs, and with the increase of the dynamic impact load, the number of bifurcations increases. The crack propagation pattern in the numerical simulation is completely consistent with the experiment, and the difference in the maximum reaction force is 2.5%. [ABSTRACT FROM AUTHOR]

The ability to recognize different driving styles of surrounding vehicles is crucial to determine the safest and most efficient driving decisions, prevent accidents, and analyze the causes of traffic accidents. Understanding if the surrounding vehicle is aggressive or cautious can greatly assist in the decision making of vehicles in terms of whether and when it is appropriate to make particular maneuvers. A driver's driving style usually changes with the environment, which brings great challenges to the current research. To this end, a dynamic driving-style analysis framework, in which drivers' interactions with other vehicles are considered, is proposed in this article. First, by analyzing common traffic scenarios, five surrounding vehicles are selected as the environmental vehicles to be considered. Time headway (THW) and time to collision (TTC) that can consider the relative speed and position with the ego vehicle are selected as the clustering indicators. Then, a Bayesian nonparametric learning method based on a hierarchical Dirichlet-process hidden semi-Markov model (HDP-HSMM) is introduced to extract primitive driving patterns from time-series driving data without prior knowledge of the number of these patterns. Then the driving pattern is scored according to the risk degree. A driver's aggressiveness is scored and drivers are divided into different styles based on the frequency distribution of driving patterns. The effectiveness of the proposed method is demonstrated on a real-world vehicle trajectory data set where results show that driving pattern switches and more complex driving behaviors can be better captured and understood semantically. [ABSTRACT FROM AUTHOR]

The experimental study presented in this paper aimed to investigate the effects of bolt number and their pattern on the failure time of minimally fire-protected glulam beam bolted connections. Four full-size glulam beam-end connection configurations reinforced perpendicular to wood grain with self-tapping screws (STS) were experimentally tested under exposure to elevated temperatures that followed the CAN/ULC S101 (Standard methods of fire endurance tests of building construction and materials. Underwriters Laboratories of Canada, Fifth edition, Ottawa, Canada, 2019) standard fire time–temperature curve. All metal connecting components (i.e., bolt heads and nuts and steel plate edges) were fire protected using wood plugs and strips, respectively. Throughout the duration of fire tests, all specimens were subjected to monotonic load causing bending moment equivalent to the maximum moment design capacity of the weakest unreinforced connection configuration. Fire test results show that the failure time of all four proposed wood–steel–wood connection configurations surpassed the 45-min mark, which is the minimum prescribed fire resistance rating for timber connections in applicable codes, with the two configurations that employed six bolts sustained the applied load for more than 60 min in standard fire conditions. Increasing the number of bolts from four to six enhanced the failure time of the proposed connection configurations with time increments more than those due to changing the bolt pattern. Most importantly, test results confirm that reinforcing the connections with STS fully curtailed wood splitting and averted row shear failure that is frequently encountered in similar but unreinforced connection configurations. [ABSTRACT FROM AUTHOR]

Number sense is fundamental to the development of numerical problem‐solving skills. In early childhood, children establish associations between non‐symbolic (e.g., a set of dots) and symbolic (e.g., Arabic numerals) representations of quantity. The developmental estrangement theory proposes that the relationship between non‐symbolic and symbolic representations of quantity evolves with age, with increased dissociation across development. Consistent with this theory, recent research suggests that cross‐format neural representational similarity (NRS) between non‐symbolic and symbolic quantities is correlated with arithmetic fluency in children but not in adolescents. However, it is not known if short‐term training (STT) can induce similar changes as long‐term development. In this study, children aged 7–10 years underwent a theoretically motivated 4‐week number sense training. Using multivariate neural pattern analysis, we investigated whether short‐term learning could modify the relation between cross‐format NRS and arithmetic skills. Our results revealed a significant correlation between cross‐format NRS and arithmetic fluency in distributed brain regions, including the parietal and prefrontal cortices, prior to training. However, this association was no longer observed after training, and multivariate predictive models confirmed these findings. Our findings provide evidence that intensive STT during early childhood can promote behavioral improvements and neural plasticity that resemble and recapitulate long‐term neurodevelopmental changes that occur from childhood to adolescence. More generally, our study contributes to our understanding of the malleability of number sense and highlights the potential for targeted interventions to shape neurodevelopmental trajectories in early childhood. Research Highlights: We tested the hypothesis that short‐term number sense training induces the dissociation of symbolic numbers from non‐symbolic representations of quantity in children.We leveraged a theoretically motivated intervention and multivariate pattern analysis to determine training‐induced neurocognitive changes in the relation between number sense and arithmetic problem‐solving skills.Neural representational similarity between non‐symbolic and symbolic quantity representations was correlated with arithmetic skills before training but not after training.Short‐term training recapitulates long‐term neurodevelopmental changes associated with numerical problem‐solving from childhood to adolescence. [ABSTRACT FROM AUTHOR]

The task of accurately predicting scientific impact and ranking the researcher based on impact has emerged as a crucial research challenge, captivating the interest of scholars across diverse domains. This task holds immense importance in enhancing research efficiency, aiding decision-making processes, and facilitating scientific evaluations. For this, the scientific community has put forth a wide array of parameters to identify the most influential researchers. These include citation count, total publication count, hybrid methodologies, the h-index, and also its extended or modified versions. But still, there is a lack of consensus on a single optimal parameter for identifying the most influential author. In this study, we introduce a novel index derived from learning hidden patterns within the mathematics field dataset, comprising data from 1050 researchers evenly split between awardees and non-awardees. Initially, we ranked selected parameters by assessing their values for individual researchers, identifying the top five parameters that most frequently placed awardees within the top 100 records. Additionally, we employed deep learning techniques to identify the top five influential parameters from the initially selected set. Subsequently, we evaluated the disjointness between the results produced by these parameters. To further refine our analysis, we assessed seven different statistical models for combining the top disjoint pair to retain the maximum properties of both parameters. The study's findings revealed that the gf and k indices exhibited a 0.96 percent disjointness ratio, establishing them as the highest disjoint pair. Moreover, the geometric mean demonstrated a 0.87 percent average impact in retaining the properties of the top disjoint pair, surpassing the other seven models. As a result of this study, we propose a new index obtained by taking the geometric mean of the top disjoint pair which increase the result by 12% as compared to existing best performing individual index performance. [ABSTRACT FROM AUTHOR]

Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk‐Of‐Non‐Adaptedness, redundancy analysis with and without structure correction and LFMM2) using an extensive set of simulated data sets that vary demography, adaptive architecture and the number and spatial patterns of adaptive environments. For each data set, we train models using either all, adaptive or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,849,600 of such evaluations, we find that (1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), (2) adaptive marker sets provide minimal performance advantages, (3) performance within the species range is variable across gardens and declines when offset models are trained using additional non‐adaptive environments and (4) despite (1) performance declines more rapidly in globally novel climates (i.e. a climate without an analogue within the species range) for metapopulations with greater LA than lesser LA. We discuss the implications of these results for management, assisted gene flow and assisted migration. [ABSTRACT FROM AUTHOR]

Copyright of Education Policy Analysis Archives / Archivos Analíticos de Políticas Educativas / Arquivos Analíticos de Políticas Educativas is the property of Educational Policy Analysis Archives & Education Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to develop a detection method based on morphological features of spike–wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects. [ABSTRACT FROM AUTHOR]

Researchers agree that both domain‐general skills and domain‐specific skills contribute to mathematics knowledge, but questions arise as to which skills can and should be trained to improve children's learning outcomes. In this article, we synthesize research on training three domain‐general constructs in early childhood (patterning skills, working memory, and spatial reasoning) and their causal links to mathematics knowledge. The results are clear: Practice with these domain‐general tasks is unlikely to transfer to higher scores on measures of mathematics knowledge, especially for preschool‐aged children. Based on this evidence, we argue against using isolated domain‐general training to enhance math knowledge in early childhood. We offer recommendations for researchers and practitioners to optimize best practices in this area. [ABSTRACT FROM AUTHOR]

Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch‐powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real‐time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real‐space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652–2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694–704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690–1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX. [ABSTRACT FROM AUTHOR]

ALK detection was performed on 2813 EGFR-unmutated NSCLC cases by simultaneous use of immunohistochemistry (VENTANA® anti-ALK D5F3, Roche Molecular Systems, Inc., Rotkreuz, Switzerland) and fluorescence in situ hybridization with the ALK break apart and the ALK/EML4 fusion probe (ZytoVision, Bremerhaven, Germany). A total of 33 cases were positive discordant (FISH-positive, IHC-negative) and 17 cases were negative discordant (FISH-negative, IHC-positive). This study's aim was to reevaluate the methods used and compare discordant samples to positive concordant samples in order to ellucidate the differences. FISH signal variants were examined and compared. Positive discordant cases featured one pattern of ALK rearrangement in 41.4%, two patterns in 48.3%, and three patterns in 10.3% of analysed samples, with a higher variability of detected patterns and a higher number of ALK copy gains. Positive concordant cases displayed one pattern of rearrangement in 82%, two patterns in 17.8%, and three patterns in 0.6% of analysed samples. The association between number of patterns and concordance/discordance was statistically significant (p < 0.05). Eleven positive discordant and two negative concordant cases underwent NGS analysis, which resulted in identification of ALK fusion in one positive discordant and two negative discordant cases. Positive protein expression regardless of FISH result correlated more with a positive NGS result compared to samples with a positive FISH result with negative protein expression. FISH analysis was able to detect atypical or heterogenous patterns of rearrangement in a proportion of cases with negative protein expression, which may be associated with more extensive genetic alterations rather than true ALK rearrangement. [ABSTRACT FROM AUTHOR]

Due to mutual interference between various components, the transitional motion of fluid particles in the flow field of pump jet propulsion becomes more complex and variable. Additionally, the vortex structure at the wake can undergo severe contraction and deformation. To further improve the accuracy of predicting the hydrodynamic performance of pump jet propulsion, this paper proposes a time-varying, space-varying, and induced velocity-varying free wake numerical calculation model based on potential flow theory. A set of hydrodynamic performance prediction methods that consider the interference of the free wake model is also established. Furthermore, a velocity smoothing model based on time reversal is constructed based on the distribution pattern of numerical singularity points during the induced velocity calculation process, which enhances the robustness of the calculation program. By comparing and analyzing the numerical calculation results with experimental results, including pressure, circulation distribution, and hydrodynamic performance, the accuracy of the free wake model proposed in this paper is verified. The results also demonstrate that the free wake model proposed in this paper can effectively improve the prediction accuracy of the hydrodynamic performance of the pump jet propulsion. [ABSTRACT FROM AUTHOR]

The theory of limits of permutations leads to limit objects called permutons, which are certain Borel measures on the unit square. We prove that permutons avoiding a given permutation of order k$$ k $$ have a particularly simple structure. Namely, almost every fiber of the disintegration of the permuton (say, along the x‐axis) consists only of atoms, at most (k−1)$$ \left(k-1\right) $$ many, and this bound is sharp. We use this to give a simple proof of the "permutation removal lemma." [ABSTRACT FROM AUTHOR]

Highlights Executive function (EF) skills are consistently associated with global mathematics assessments. However, less is known about which specific mathematics skills invoke EF in early childhood. We adapted batteries of EF, numerical, and patterning tasks to be conducted via synchronous video conferencing with typically developing 4‐year‐old children (N = 115, 56.6% female, 85.2% White, non‐Hispanic, 7.8% Multiracial, 0.9% Hispanic, 0.9% Black, 1.7% American Indian/Alaska Native, 2.6% Asian and 0.9% missing) from primarily upper middle class households (maternal education range: some college to graduate or professional degree, mode = graduate or professional degree) in the United States. We found feasibility of remote adaptation varied greatly by task. Just over half of enrolled children (57.02%) completed all tasks and there were higher rates of missingness compared to an in‐person sample with a similar age range collected in schools. Nonetheless, working memory predicted various mathematics skills, including patterning, verbal counting, and number comparison, controlling for age and maternal education. Relations between cognitive flexibility and patterning varied across model specifications. Overall, these findings provide insight into the costs and benefits of remotely administered direct assessments of EF and mathematics in early childhood and add to the growing body of literature suggesting working memory plays an important role in several foundational early mathematics skills. Suggestions are offered for remote assessment with preschoolers. Executive function and mathematics tasks were adapted for remote use and associations among skill sets were examined. Remotely administered data collection sessions had higher rates of missingness than comparable in‐person studies. Findings replicate and extend prior work on the associations between working memory and patterning, verbal counting, and number comparison in a remote setting. [ABSTRACT FROM AUTHOR]

Background: To master the secondary–tertiary transition into fields of science, technology, engineering, and mathematics (STEM), academic self-beliefs play a pivotal role, especially those related to learning mathematics. The framework of expectancy-value theory has been used widely in primary and secondary education and partly in tertiary education to assess the self-beliefs of students in terms of expectancy of success and perceived value of mathematics. Based on this framework, we measured how the intrinsic value, the attainment value, the utility value, and the cost of learning mathematics as well as the expectancy of success when learning mathematics developed during the secondary–tertiary transition of students into STEM fields. Data were collected in a quantitative repeated-measures questionnaire study with two measurement points (measurement point 1: n = 710, measurement point 2: n = 487, listwise: n = 409). We conducted a latent profile analysis to identify the prevalent patterns of mathematics self-beliefs, called profiles, at each of the two measurement points. We studied the relation of these profiles to prior education, achievement at school, and achievement at university. By performing a latent transition analysis, we determined the probabilities of transitioning from the initial profiles to the posterior profiles. Results: Our analysis revealed four distinct prevalent profiles at each measurement point, ranging from highly favorable (i.e., high expectancy, high value, low cost) to highly unfavorable with respect to learning mathematics. The profiles with favorable manifestations remained stable over time, while those with undesirable manifestations deteriorated further. We observed a sharp increase in cost across all profiles. Prior achievement correlated strongly with profile membership. Conclusions: The expenditure of time and energy increased sharply during the secondary–tertiary transition, independently of the students' initial motivational patterns. The perceived utility of mathematics for potential future careers was shown to be a significant source of motivation. The role of mathematics in future careers should thus be made visible in university teaching. Keeping the detrimental development of initially undesirable motivational profiles in mind, university teachers should create ample opportunities for students to gain a sense of accomplishment. [ABSTRACT FROM AUTHOR]

Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern's types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level, but the core is the proposed three combination methods in fusion decision methods. In this method, the dataset is composed of four main Persian geometric pattern types: Tond dah, Kond tablghenas, Hashtva 4 lengeh, and HashtvatablKond. So, the model will be trained by extracting global and local features of the images separately. Random forest, as the prevalent machine learning algorithm, is proposed for training data and predicting the class of input images. Finally, the probability of prediction for random forest classifiers is fused by the Decision Templates (DT) combiner, Naïve Bayes (NB) combiner, and Dempster–Shafer combination methods. In comparison with the individual classifier accuracy results of 80% and 85% for global and local features respectively, our proposed approach achieves an improved accuracy of 90%, 88%, and 90% in three fusion decision methods including DT combiner, NB combiner, and Dempster–Shafer combination methods. [ABSTRACT FROM AUTHOR]

To examine the level of number line estimation (NLE) in Chinese children with respect to representations of both numerical (Arabic numerals) and nonnumerical symbols (dots), a total of 192 Chinese preschoolers aged between 4 and 5 years participated in four different NLE tasks. These tasks were paired to evaluate the accuracy and patterns of children's estimations in both numerical and non-numerical symbol contexts. Our findings indicate that, for Chinese preschoolers, relatively precise numerical symbol representations begin to emerge as early as 4 years of age. The accuracy of number line estimates for both 4- and 5-year-old children gradually increases in tasks involving both numerical and non-numerical symbols. Additionally, the development and patterns observed in the number line estimates of 4- and 5-year-old Chinese preschoolers are similar in both numerical symbol and non-numerical symbol tasks. These results indicate that the initiation of relatively precise numerical symbol representation and the turning point in the developmental trajectory, where the relatively precise representation for numerical symbols surpasses that of non-numerical ones, occur earlier in Chinese children than in their Western counterparts. [ABSTRACT FROM AUTHOR]

This study provides an in-depth analysis of the frequency of extreme streamflow in Italy, adopting the innovative perspective of the theory of records, and focusing on record-breaking floods. (i.e., annual maximum series, AMS) observed in Italy between 1911 and 2020. Our research employs an extensive dataset of 522 annual maximum series (AMS) of streamflow observed across Italy between 1911 and 2020. We consider three time intervals (1911–2020, 1911–1970, and 1971–2020), and we define pooling-groups of AMSs based on (a) hydrological (e.g., catchment size, mean annual precipitation, etc.) and (b) spatial proximities of the gauged sites. First, within each group and for each time period, we compute the regional average number of record-breaking events (NRbins). Second, with a series of resampling experiments that preserve the spatial correlation among the AMSs, we test the hypothesis that NRbins result from a group of stationary sequences. Our results show spatially coherent patterns of an increasing number of record-breaking floods in central and in northeastern Italy over the last 50 years. In the same time interval, significant deviations in the regional number of record-breaking events from what would be expected for stationary flood sequences seem to be more common in drier climates or at higher altitudes, while the catchment size does not seem to be a meaningful descriptor. [ABSTRACT FROM AUTHOR]

This paper develops a new method for analyzing the relationship between a set of points and another single point, the latter of which we call a reference point. This relationship has been discussed in various academic fields, such as geography, criminology, and epidemiology. Analytical methods, however, have not yet been fully developed, which has motivated this paper. Our method reveals how the number of points varies by the distance from a reference point and by direction. It visualizes the spatial pattern of points in relation to a reference point, describes the point pattern using mathematical models, and statistically evaluates the difference between two sets of points. We applied the proposed method to analyze the spatial pattern of the climbers of Mt. Azuma, Japan. The result gave us useful and interesting findings, indicating the method's soundness. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ingenius, Revista Ciencia y Tecnología is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: As a new type of spinning machine, the jet spinning machine absorbs the carding system of the rotating cup spinning series and the nozzle part of the jet spinning. This paper aims to intends to introduce the double carding structure currently studied by the rotating cup spinning into the jet spinning machine, and analyze the influence of the nozzle characteristic number on the flow field in the double carding structure to verify the advantages of the double carding structure. Design/methodology/approach: The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field. The influence of the nozzle characteristic number on the flow pattern in the four models is compared. The advantages and disadvantages of a conventional single comb and a double comb with a bypass channel on the longer side of the transport channel as an additional air supply channel are also evaluated. Findings: At present, the double comb technology of rotary cup spinning is being studied at home and abroad to improve the spinning quality and improve the difficult problem of mixed yarn with large difference in processing fiber properties. At present, the jet spinning machine combines the advantages of rotary cup spinning and jet spinning, absorbing the comb system of rotary cup spinning series and the nozzle part of jet spinning. Therefore, it is found that the introduction of the double-split structure into the wool jet spinning has research value to improve the spinning quality. Originality/value: The purpose of this paper is to refer to the previous research on the double comb structure in rotary spinning, and to apply the double comb structure in the new jet spinning machine to improve the spinning quality. The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field. [ABSTRACT FROM AUTHOR]

This paper aims at expanding theories of metaphorical reasoning to other tropes. Asking why figurative language tends to fall into a limited number of patterns, it first examines approaches that offer an answer – ranging from antique rhetoric theory, Hans Blumenberg, Harald Weinrich, Donald Davidson, and Roman Jakobson to George Lakoff and Mark Johnson. By then turning to Giambattista Vico, it puts forth the argument that a limited set of pre-structured ways of embodied reasoning is hard-wired in and enacted by different aspects of children's ways of playing – and provides the foundation for structures of thinking in the adult life. [ABSTRACT FROM AUTHOR]

In this paper, the structural performances of steel plate shear walls employing web plates with rectangular, triangular (zigzag), trapezoidal, and sinusoidal corrugation patterns under monotonic loading are investigated. Several significant structural response parameters including buckling stability, stiffness, strength, serviceability, and energy dissipation capability are considered in this research endeavor. The results of this study provide crucial insights into the behavior of corrugated-web steel shear walls with varying infill plate shapes and thicknesses. It is shown that corrugated-web shear walls predominantly undergo local and/or interactive buckling modes, while flat-web systems exhibit more of a global buckling behavior. Additionally, corrugated infill plates substantially enhance the overall capacity of the system, with the rectangular pattern exhibiting the highest strength, surpassing the flat-web panels by approximately 11 times for a 2-mm web-plate thickness. Assessment of the energy absorption capacity reveals the superior performance of the rectangular pattern for web thicknesses between 2.0 and 3.0 mm. From the load–displacement responses, it is found that increasing of the web thickness significantly enhances the strength and stiffness performances and the base shear capacity is improved by 42% while increasing the thickness from 1.5 to 3.0 mm. These results demonstrate the importance of proper design and detailing in ensuring the desirable and high performance of corrugated-web steel shear walls as promising lateral force-resisting systems. [ABSTRACT FROM AUTHOR]

Background: Ear and tail lesions are prevalent indicators of impaired welfare observed in pig production with different multifactorial causes. Understanding the progression of ear and tail lesions over time is important to implement preventative strategies on commercial pig farms. Therefore, this case study aimed to provide a detailed account of patterns of ear and tail lesions in pigs on a single commercial farm during the grower-finisher period. Case presentation: A total of 1,676 12-week old pigs (n = 773 females and n = 903 males, all tail docked) were followed from arrival to the grower facilities until transferred to the finisher stage on a commercial pig farm in Ireland. Pigs were individually weighed and inspected for the severity of fresh ear and tail lesions (score 0–4) on transfer to the first grower (24.9 ± 5.33 kg, 12 weeks of age, n = 1,676 pigs), second grower (33.3 ± 7.04 kg, 14 weeks of age, n = 1,641 pigs), and finisher stage (60.2 ± 7.74 kg, 18 weeks of age, n = 1,626 pigs). Due to the low number of pigs with high scores, ear lesions were classified as no (score 0), mild (score 1), moderate (score 2) and severe (score ≥ 3) and tail lesions were classified as no (score 0), mild (score 1), and moderate-to-severe (score ≥ 2). Ear lesions were more prevalent than tail lesions at each inspection. There were approx. 19% of pigs with ear lesions at all three inspections but no pigs presented with tail lesions at all three inspections. When considering the specific severity categories, we observed 32 different ear lesion score combinations and 15 different tail lesion score combinations across the three inspections. Conclusion: The high number of observed patterns of ear and tail lesions suggest large individual variability in lesion progression. Ear lesions were more of an issue than tail lesions and little is known about this health and welfare problem indicating that further research into causes and management strategies is needed. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the effect of sustained reading on the temporal changes in the wavefront error in the presbyopic eye. The wavefront aberration of the eyes was measured using an IRX3 Shack–Hartmann aberrometer before and after (immediately, 5 min, and 10 min after) a reading task. Temporal changes in C 2 0 , C 4 0 , and C 3 − 1 coefficient values of the eyes were plotted, showing a predominant number of V-shaped patterns (for C 4 0 and C 3 − 1 ) and inverse V-shaped patterns (for C 2 0 ) among the study group, and the percentages (between 27 and 73%) were reported. The median of the total RMS of aberrations and the RMS of HOA (higher-order aberrations), which included comatic (3rd order) and spherical-like aberrations (4th and 6th order), increased immediately after finishing the near-vision reading task and then decreased. The median of RMS of comatic aberrations had a similar pattern of variations, while the median of RMS of spherical-like aberrations displayed an opposite pattern. Simulating the aberration changes due to lens decentration caused by relaxed zonules during 4 D accommodation in an eye model demonstrated that the expected range of changes for the vertical coma and spherical aberrations are in the order of 0.001 and 0.01 μm, respectively, which could justify why the observed changes were not statistically significant. The observed dynamic changes in HOA might be linked to the biomechanical characteristics and alterations in the displacement of the crystalline lens following prolonged near-vision tasks in presbyopic people. Although some predominant patterns under some conditions were shown, they exhibit considerable inter-subject and inter-ocular variability. This might be due to slight misalignments while fixating on the internal extended object in the aberrometer. [ABSTRACT FROM AUTHOR]

Background: Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient's evolution from multivariate sequential patterns. Methods: In this paper, we propose a method to extract a new type of patterns in the clinical domain called Jumping Diagnostic Odds Ratio Sequential Patterns (JDORSP). The aim of this method is to employ the odds ratio to identify a concise set of sequential patterns that represent a patient's state with a statistically significant protection factor (i.e., a pattern associated with patients that survive) and those extensions whose evolution suddenly changes the patient's clinical state, thus making the sequential patterns a statistically significant risk factor (i.e., a pattern associated with patients that do not survive), or vice versa. Results: The results of our experiments highlight that our method reduces the number of sequential patterns obtained with state-of-the-art pattern reduction methods by over 95%. Only by achieving this drastic reduction can medical experts carry out a comprehensive clinical evaluation of the patterns that might be considered medical knowledge regarding the temporal evolution of the patients. We have evaluated the surprisingness and relevance of the sequential patterns with clinicians, and the most interesting fact is the high surprisingness of the extensions of the patterns that become a protection factor, that is, the patients that recover after several days of being at high risk of dying. Conclusions: Our proposed method with which to extract JDORSP generates a set of interpretable multivariate sequential patterns with new knowledge regarding the temporal evolution of the patients. The number of patterns is greatly reduced when compared to those generated by other methods and measures of interest. An additional advantage of this method is that it does not require any parameters or thresholds, and that the reduced number of patterns allows a manual evaluation. [ABSTRACT FROM AUTHOR]

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset. [ABSTRACT FROM AUTHOR]

The article criticizes the mathematical explanatory videos of YouTuber Daniel Jung and states that they have significant didactic and mathematical deficiencies. The videos present mathematics as a collection of rules without explanations and lack logical structure and technical correctness. They reduce mathematics to pattern recognition and routines and neglect authentic applications and modeling. The use of these videos in schools and educational foundations is considered alarming. It is emphasized that the success of such videos should be seen as a warning sign. [Extracted from the article]

Batik is one of Indonesia's most famous and commonly encountered cultural heritage sites. Batik is a type of Indonesian specialty cloth where the pattern is drawn on the cloth using wax. The number of batik pattern varieties produced by Indonesia introduces challenges in recognizing and differentiating between batik patterns. Various previous works have been done to research how to use computer vision to recognize and differentiate between batik patterns. Some of the methods used in previous works involve convolutional neural networks; previous studies use pre-trained models or manually design the model, which may not be suitable for recognizing batik patterns. This study focuses on developing a convolutional neural network model optimized and designed automatically using genetic algorithms using more unique images than previous works. Genetic algorithms have been proven in previous works from other fields to produce better models compared to pre-trained and manually designed models. The experiment results show that a model design using genetic algorithms outperforms pre-trained models by a significant margin. The model automatically achieved an accuracy of 0.8654 with a parameter ∼1% of the VGG-19 model, whereas the VGG-19 model achieved an accuracy of 0.7596. [ABSTRACT FROM AUTHOR]

This article provides an overview of Ibn Arabi's Fihrist, a catalog of his works, and delves into its structure and thematic considerations. The authors emphasize the importance of understanding this text as it forms the foundation for comprehending Ibn Arabi's work. They highlight the thematic connections among the book titles and their association with Quranic verses and Hadith. The article also explores the use of abjad methodology in the Fihrist and its significance in understanding Ibn Arabi's contemplation of the mysteries of Reality. Overall, the text offers insights into the structure and meaning of Ibn Arabi's work. [Extracted from the article]

This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare's works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character's dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare's plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare's works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models. [ABSTRACT FROM AUTHOR]

The efficiency of bovine in vitro embryo production can be significantly improved by splitting embryos at different stages. However, the blastocyst quality of in vitro‐produced demi‐embryos remains unexplored. The objective of this research was to compare embryo developmental rates and quality of bovine demi‐embryos produced by two different strategies: (a) embryo bisection (BSEC) and (b) 2‐cell blastomere separation (BSEP). To determine demi‐embryos quality, we evaluated total blastocyst cell number and proportion of SOX2+ cells. Additionally, the expression of SOX2, NANOG, OCT4, CDX2, IFNT, BAX and BCL genes and let‐7a and miRNA‐30c Micro RNAs was analysed. BSEP resulted in improved blastocyst development, higher ICM cells and a significantly higher expression of IFNΤ than demi‐embryos produced by BSEC. Let‐7a, which is associated with low pregnancy establishment was detected in BSEC, while miRNA‐30c expression was observed in all treatments. In conclusion, BSEP of 2‐cell embryos is more efficient to improve in vitro bovine embryo development and to produce good quality demi‐embryos based on ICM cell number and the expression pattern of the genes explored compared to BSEC. [ABSTRACT FROM AUTHOR]

The development of young children's counting has been extensively researched, yet detailed studies that examine development over multiple years are curiously absent from the literature. This research examined 15 preschoolers' (ages 3–4) use of number words as they engaged in two counting tasks, an oral counting task and counting a collection of 31 pennies, at four time points over 2 years. This study highlights patterns in the development of a number word list for the 15 children and presents three cases that illustrate the ways in which development varied for different children over time. Findings indicate that children demonstrated emerging understandings of the structure of the number sequence within the teen numbers and beyond before consistently using the conventional, "correct" sequence. Implications for research and instruction include how assessment of mathematical tasks are conducted and coded to capture children's important understandings beyond accuracy as well as how the development of counting and mathematical practices are viewed. [ABSTRACT FROM AUTHOR]

The object of the study is epidemiological grouping using the SEIR mathematical model on a machine learning-based multilayer network. The problems in this research are related to managing epidemiological data on a large scale to determine disease patterns and identification such as the number of recovered cases, number of infected cases and number of deaths and demographic factors. In the process, traditional methods make it difficult to carry out processes such as determining patterns and identifying diseases. So, it is necessary to use machine learning and the SEIR (Susceptible-Exposed-Infectious-Recovered) mathematical model which can be integrated with multilayer networks to increase accuracy and effectiveness in identifying diseases and determining patterns. The results obtained from this research are a model that can identify and determine patterns of disease spread in largescale epidemiological data. In its application, the SEIR mathematical model combined into a social layer and an environmental layer in a multilayer network. This research is research with a level of novelty in the application of the SEIR mathematical model to multilayer networks and machine learning so that the model formed can be used to view the distribution of epidemiological data for disease-related information. Machine learning aims to process large-scale data in minimal time resulting in clustering and identification of diseases such as flu, Covid-19 and pneumonia. This research has an accuracy of 94 % using the MAPE evaluation technique. It is hoped that the resulting model can be used in the world of health for disease mapping in certain areas as a reference for mitigating the spread of disease. [ABSTRACT FROM AUTHOR]

In this study, a novel time-driven mathematical model for trust is developed considering human–multi-robot performance for a Human–Robot Collaboration (HRC) framework. For this purpose, a model is developed to quantify human performance considering the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload, workloads due to the robots' mistakes, and task complexity. The performance of multi-robot in the HRC setting is modeled based upon the rate of task assignment and completion as well as the mistake probabilities of the individual robots. The human trust in HRC setting with single and multiple robots is modeled over different operation regions, namely unpredictable region, predictable region, dependable region, and faithful region. The relative performance difference between the human operator and the robot is used to analyze the effect on the human operator's trust in robots' operation. The developed model is simulated for a manufacturing workspace scenario considering different task complexities and involving multiple robots to complete shared tasks. The simulation results indicate that for a constant multi-robot performance in operation, the human operator's trust in robots' operation improves whenever the comparative performance of the robots improves with respect to the human operator performance. The impact of robot hypothetical learning capabilities on human trust in the same HRC setting is also analyzed. The results confirm that a hypothetical learning capability allows robots to reduce human workloads, which improves human performance. The simulation result analysis confirms that the human operator's trust in the multi-robot operation increases faster with the improvement of the multi-robot performance when the robots have a hypothetical learning capability. An empirical study was conducted involving a human operator and two collaborator robots with two different performance levels in a software-based HRC setting. The experimental results closely followed the pattern of the developed mathematical models when capturing human trust and performance in terms of human–multi-robot collaboration. [ABSTRACT FROM AUTHOR]

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the "first-infection tree," a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment. [ABSTRACT FROM AUTHOR]

Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption. [ABSTRACT FROM AUTHOR]

In the achromatic White's grid, the grey patches between the grey lines are perceived as darker while the same elements inserted between white lines are perceived lighter than their true measured values. A number of authors attempted to calculate the magnitude and direction of this effect using mathematical models based on multiple spatial filters. This paper uses different mathematical model, based on regression analysis, which has shown itself as an excellent tool for prediction of direction and magnitude of White's effect. The psychophysical visual experiment was conducted on 38 subjects of both genders. The differences in lightness perception ∆L00 were calculated in CIE ∆E_2000 system. This paper determined the functional dependence of White's effect magnitude ∆L00 to the lightness L of rectangular elements in White's achromatic grid. The results gave the square polynomial of very high quality (R² = 0,974). Regression polynomials were also found. The gave numerical values ∆L00 of difference in perception of left and right elements in comparison to their physical values (left R² = 0,943, right R² = 0,938) in dependence to variation of lightness parameter L. Results of the research clearly show the mathematical pattern of White's effect based on the lightness of rectangular elements. [ABSTRACT FROM AUTHOR]

Bird monitoring is an important approach to studying the diversity and abundance of birds, especially during migration, as it can provide core data for bird conservation purposes. The previous methods for bird number estimation are largely based on manual counting, which suffers from low throughput and a high error rate. In this study, we aimed to provide an alternative bird-counting method from video datasets by using five available ImageJ methods: Particle Analyzer, Find Maxima, Watershed segmentation, TrackMate, and trainable WEKA segmentation. The numbers of birds and their XY coordinates were extracted from videos to conduct a side-by-side comparison with the manual counting results, and the three important criteria of the sensitivity, precision, and F1 score were calculated for the performance evaluation. From the tests, which we conducted for four different cases with different bird numbers or flying patterns, TrackMate had the best overall performance for counting birds and pinpointing their locations, followed by Particle Analyzer, Find Maxima, WEKA, and lastly, Watershed, which showed low precision in most of the cases. In summary, five ImageJ-based counting methods were compared in this study, and we validated that TrackMate obtains the best performance for bird counting and detection. [ABSTRACT FROM AUTHOR]

The FRC algorithm is a commonly employed technique in LCD displays, aimed at enhancing the data bit depth through pixel dithering. However, it can also give rise to certain display issues such as noise, scrolling stripes, and flickering [1]. To address these problems comprehensively, this paper conducts an analysis from both temporal and spatial dimensions. It reveals that screen noise is closely associated with the uniformity of pixel data distribution throughout the entire FRC cycle time, while rolling stripes are linked to the spatial distribution of pixel data in both row and column directions. Furthermore, flickering arises due to variations in the number of FRC pattern pixels actually displayed per frame. The validity of these theories has been verified on an FPGA platform. [ABSTRACT FROM AUTHOR]

Flooding, a pervasive and severe natural disaster, significantly damages environments and infrastructure and endangers human lives. In affected regions, disruptions to transportation networks often lead to critical shortages of essential supplies, such as food and water. The swift and adaptable delivery of relief goods via vehicle is vital to sustain life and facilitate community recovery. This paper introduces a novel model, the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which focuses on optimizing the speed of route generation and minimizing waiting times for aid delivery in flood conditions. The Genetic Algorithm (GA) is employed because it effectively handles the uncertainties typical of NP-Hard problems. This model features a dual-population strategy: random and enhanced populations, with the latter specifically designed to manage uncertainties through anticipated route performance evaluations, incorporating factors like waiting times and flood risks. The Population Sizing Module (PSM) is implemented to dynamically adjust the population size based on the dispersion of affected nodes, using standard deviation assessments. Introducing the Complete Subtour Order Crossover (CSOX) method improves solution quality and accelerates convergence. The model's efficacy is validated through simulated flood scenarios that emulate various degrees of uncertainty in road conditions, affirming its practicality for real-life rescue operations. Focusing on prioritizing waiting times over travel times in routing decisions has proven effective. The model has been tested using standard CVRP problems with 20 distinct sets, each with varying node numbers and patterns, demonstrating superior performance and efficiency in generating vehicle routing plans compared to the shortest routes, which serve as the benchmark for optimal solutions. The results highlight the model's capability to deliver high-quality solutions more rapidly across all tested scenarios. [ABSTRACT FROM AUTHOR]

To address the effect of a ship's rolling on the fluidization quality of fluidized beds, in this study, a simulation of a rolling fluidized bed with longitudinal internal members added (R-FBLIM) was carried out and compared with that of a rolling fluidized bed without internal members added (R-FBWIM). The transient motion, as well as the behavioral characteristics of the bubbles within the R-FBLIM, was analyzed; the variation patterns of the number of bubbles, as well as the equivalent diameter of the bubbles, were compared for different apparent gas velocities, oscillation periods, and amplitudes; and the mechanism of the action of the longitudinal internal members was investigated. The results show that the structural design of the longitudinal internal members can effectively improve the gas–solid fluidization quality of the rolling fluidized bed. The horizontal support plate and the cap hole structure can effectively break the air bubbles, the cap hole structure promotes the radial mixing of the gas–solid fluid, and the internal and outer rings of the curved surface plate roll in rows, which inhibit the aggregation behavior of the gas–solid fluid to the two sides of the oscillating planes, respectively, by cooperating with the cap hole structure. Compared with R-FBWIM, the gas–solid phase within R-FBLIM is more spatially distributed, with the number of bubbles increasing by about 2–4 times and the mean diameter decreasing by about 50–60%. The number of bubbles increases with the gas velocity but decreases with the rolling amplitude; the mean diameter decreases with the gas velocity but responds less to the rolling amplitude change. [ABSTRACT FROM AUTHOR]

Two endemic Anatolian nemacheilids were studied cytogenetically for the first time. Diploid chromosome numbers, chromosome morphologies in the karyotypes and also C-band and Ag-NOR banding properties were recorded for the two species of Oxynoemacheilus. The diploid chromosome number is 50 and karyotypes contained 34 biarmed and 16 monoarmed chromosomes in Oxynoemacheilus axylos Yoğurtçuoğlu et al. 2022. In O. nasreddini Yoğurtçuoğlu et al. 2021, there are 30 biarmed and 20 monoarmed chromosomes. No heteromorphic sex chromosomes were determined. C-band patterns and the number of Ag-NORs were different between the two studied species. [ABSTRACT FROM AUTHOR]

An assessment of the projected changes in precipitation extremes for the 21st century is presented here for Greece and its individual administrative regions. The analysis relies on an ensemble of high-resolution Regional Climate Model (RCM) simulations following various Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The simulated changes in future annual total precipitation (PRTOT) under the examined scenarios are generally negative but statistically non-robust, except towards the end of the century (2071–2100) over high-altitude mountainous regions in Western Greece, Peloponnese, and Crete under RCP8.5. The pattern of change in the number of very heavy precipitation days (R20) is linked to the respective pattern of the PRTOT change with a statistically robust decrease of up to −5 days per year only over parts of the high-altitude mountainous regions in Western Greece, Peloponnese, and Crete for 2071–2100 under RCP8.5. Contrasting the future tendency for decrease in total precipitation and R20, the changes in the intensity of precipitation extremes show a tendency for intensification. However, these change patterns are non-robust for all periods and scenarios. Statistical significance is indicated for the highest 1-day precipitation amount in a year (Rx1day) for the administrative regions of Thessaly, Central Greece, Ionian Islands, and North Aegean under RCP8.5 in 2071–2100. The changes in the contribution of the wettest day per year to the annual total precipitation (RxTratio) are mainly positive but non-robust for most of Greece and all scenarios in the period 2021–2050, becoming more positive and robust in 2071–2100 for RCP8.5. This work highlights the necessity of taking into consideration high-resolution multi-model RCM estimates in future precipitation extremes with various scenarios, for assessing their potential impact on flood episodes and the strategic planning of structure resilience at national and regional level under the anticipated human-induced future climate change. [ABSTRACT FROM AUTHOR]

Every year, large quantities of dust are transported from North Africa to the Americas, Europe, and West Asia. The purpose of this study is to analyze four intense and pervasive dust storms that entered the Middle East from Northern Africa. Satellite products, ground-based remote sensing measurements, reanalysis data, and the outputs of the Aire Limitée Adaptation dynamique Développement InterNational-Dust (ALADIN-Dust) and the ICOsahedral Nonhydrostatic weather and climate model with Aerosols and Reactive Trace gases (ICON-ART) forecasting models were synergized. The dust storms originated from different source regions located in the north, northeastern, and central parts of the Sahara Desert. The transport height of the main dust plumes was about 3–5 km, triggered by the westerly zonal winds. The presence of a closed low over the Eastern Mediterranean and the penetration of a deep trough into North Africa at 500 hPa were the main synoptic circulation patterns favoring long-range dust transport during the four dust events. A comparison of aerosol optical depth (AOD) outputs from the two models with satellite data revealed that although both models forecasted dust transport from Africa to the Middle East, they considerably underestimated the AOD values, especially near the dust sources. The ICON-ART model performed slightly better than ALADIN in forecasting these dust storms, and for longer forecasting leading time, although the performance of both models decreased, the superiority of the ICON-ART model became more apparent. [ABSTRACT FROM AUTHOR]

The manuscript presents a pilot study of the impact of orthodontic intervention on the brain electrical activity. The orthodontic treatment is a powerful factor of both physiological influence on the jaw system and the surrounding tissues of the head and stress influence. All practically healthy subjects of the same age category (18–25 years) were distributed among three groups based on the method of orthodontic treatment. Group 1 included patients using braces, groups 2 and 3 included patients using aligners in which pressure was applied to 3–5 or 1–2 teeth, respectively. Brain activity electroencephalographic data were collected twice during neurophysiological monitoring: before and after orthodontic correction. The collected data sets included EEG signals from the occipital region of the brain. Numerical processing was performed based on continuous wavelet analysis to estimate the number and duration of oscillatory patterns in narrow frequency bands from 1 to 50 Hz. An assessment of the oscillatory brain activity demonstrated that different grades of correction intensity, regarding the dentition and occlusion, lead to uniform changes in the oscillatory patterns assessed by the electroencephalography in the occipital lobe. Comparison of the number of oscillatory patterns in the groups showed significant changes in the high-frequency ⋃ HF ∋ 16 ; 18 , 20 ; 28 , 32 ; 34 , 42 ; 50 Hz. The number of patterns in the ⋃ HF -band increases when using the most intense bracket devices; while in cases of more gentle correction based on aligner systems, it remains unchanged or even decreases. The independent clustering procedure by assessing changes in oscillatory processes of ⋃ HF -band occurring in a single occipital O1-canal made it possible to divide the data array into three clusters. The clusters of changes in brain activity correspond to clinical groups of patients. Thus, different types of dental exposure lead to significantly different changes in the brain activity of patients. [ABSTRACT FROM AUTHOR]

Mathematics is essential to science in numerous ways, providing a precise language for communication, tools for measurement and analysis, models for prediction and problem-solving, and the ability to identify patterns and verify results. In an activity called the Penny Model, students use pennies to explore the concept of change over time and scientific thinking. They arrange the pennies based on their dirtiness and use graphing skills to analyze the data and make predictions about the age of a penny. The activity demonstrates how math is a crucial tool in science and can enhance students' understanding of scientific concepts. [Extracted from the article]

This qualitative research employed a phenomenological approach with the aim of describing students' learning obstacles in computational thinking related to the lesson on number patterns. This study took place at one of the MTsN schools in Makassar, involving 74 students of grade 8th, out of which 12 students were selected for interviews. The research employed tests, questionnaires, and interview guidelines as research instruments. The results revealed that students faced various learning obstacles, including (a) ontogenical obstacles such as instrumental ontogenical obstacle, psychological ontogenical obstacle, and conceptual ontogenical obstacle; (b) epistemological obstacles, encompassing difficulties in pattern recognition, abstraction, and generalization due to limitations in students' contextual abilities to solve problems related to number patterns; and (c) didactical obstacles, including limitations in the teaching of number pattern lesson. The learning process fails to involve students in the process of abstraction and, resulting in incomplete material presentation. Moreover, it lacks emphasis on students' thinking process during abstraction and generalization process, and places less emphasis on problem decomposition and algoritmic thinking. [ABSTRACT FROM AUTHOR]