Scale Distribution Research Articles

The Effect of Nomophobıa on Anxıety and Qualıty of Lıfe in Physıotherapy and Rehabılıtatıon Department Students

Objective. While smartphones provide convenience to individuals, they also cause many problems. One of these problems is smartphone addiction, known as nomophobia. The aim of our study is to investigate the impact of nomophobia on anxiety and quality of life among students in the Department of Physiotherapy and Rehabilitation.. Methods. The study included 211 students enrolled at Sakarya University of Applied Sciences, Department of Physiotherapy and Rehabilitation. Participants completed a data collection form to provide their demographic characteristics. They also completed the Turkish Nomophobia Scale to assess nomophobia levels, the Beck Anxiety Scale to evaluate anxiety levels, and the SF-36 Quality of Life Scale to measure their quality of life levels. Results. In our study, the nomophobia scale score was found to be 77.28. The distribution of the nomophobia scale according to the cut-off points is 30% mild, 45% moderate, and 25% extreme. We identified a statistically significant relationship between nomophobia and anxiety (p < 0.05). Additionally, there was a statistically significant association between nomophobia and quality of life parameters, excluding vitality/energy, mental health, and social functioning (p < 0.05). However, no statistically significant relationship was found between anxiety and quality of life. Conclusion. The anxiety levels of nomophobic individuals increase in parallel with their nomophobia, leading to a decrease in their quality of life. Necessary information should be provided and awareness should be raised in order to prevent nomophobia, which is especially common in the young population.

Measurements of jet cross-section ratios in 13 TeV proton-proton collisions with ATLAS

Measurements of jet cross-section ratios between inclusive bins of jet multiplicity are performed in 140 fb−1 of proton-proton collisions with s=13 TeV center-of-mass energy, recorded with the ATLAS detector at CERN’s Large Hadron Collider. These ratios are constructed from double-differential cross-section measurements that are made in bins of jet multiplicity and other observables that are sensitive the energy scale and angular distribution of radiation due to the strong interaction in the final state. Additionally, the scalar sum of the two leading jets’ transverse momenta is measured triple differentially, in bins of the third jet’s transverse momentum and of jet multiplicity. These measurements are unfolded to account for acceptance and detector-related effects. The measured distributions are used to construct ratios of the inclusive jet-multiplicity bins, which have been shown to be sensitive to the strong coupling αS while being less sensitive than other observables to systematic uncertainties and parton distribution functions. The measured distributions are compared with state-of-the-art QCD calculations, including next-to-next-to-leading-order predictions for two- and three-jet events. These predictions are generally found to model the data well and perform best in bins with a modest requirement on the third jet’s transverse momentum. Significant differences between data and Monte Carlo predictions are observed in events with large rapidity gaps and invariant masses of the leading jet pair. Studies leading to reduced jet energy scale uncertainties significantly improve the precision of this work and are documented herein. © 2024 CERN, for the ATLAS Collaboration 2024 CERN

Validation of the Warwick-Edinburgh Mental Well-Being Scale for the Mental Health Surveillance (MHS) of German adults

BackgroundMental health encompasses more than just the absence of mental disorders. Thus, a Mental Health Surveillance (MHS) and reporting system for Germany should monitor mental well-being in addition to psychopathology to capture a more complete picture of population mental health. The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) is an internationally established inventory for the integrated assessment of different aspects of mental well-being (i.e., hedonic and eudaimonic) in population samples that has not yet been validated for Germany.MethodsUsing data from a cross-sectional online survey of a convenience sample of N = 1.048 adults aged 18–79 years (51% female) living in Germany, the factorial structure, measurement invariance (age, sex) and psychometric properties of the WEMWBS in its long (14 items) and short (7 items) versions were analyzed. Additionally, correlations to relevant factors (e.g., health-related quality of life, psychological distress) were investigated as indicators of criterion validity.ResultsMeans of model fit indices did not confirm a unidimensional factor structure for either version. The three-factor-correlative models showed moderate to good fit while the bifactor model with one general mental well-being factor and three grouping factors fitted the data best. The full range of possible responses was used for all items, and the distribution of both scales was approximately normal. Moreover, the results revealed measurement invariance across sex and age groups. Initial evidence of criterion validity was obtained. Internal consistencies were α = 0.95 and α = 0.89, respectively. Average mental well-being was comparable to that of other European countries at 3.74 for the long version and 3.84 for the short version. While there were no differences by sex, comparisons between age groups revealed higher mental well-being among the older age groups.ConclusionsBoth versions of the WEMWBS showed sound psychometric characteristics in the present German sample. The findings indicate that the instrument is suitable for measuring mental well-being at the population level due to its distributional properties. These results are promising, suggesting that the scale is suitable for use in a national MHS that aims to capture positive mental health in the population as a foundation for prevention and promotion efforts within public mental health.

Do marine food subsidies predict large scale distribution of scavenging seabirds within the Bay of Biscay?

Omnivorous and opportunistic species have the potential to exploit human food subsidies. In marine ecosystem, understanding interaction between seabirds and fishery activities, particularly their scavenging behaviour on discards is crucial nowadays, in the context of EU landing obligation. The Bay of Biscay is one of the major fishing zones in Europe and a central area for the wintering of a high number of seabird species. During spring and autumn, oceanographic surveys were conducted by the R/V Thalassa with the aim to evaluate fishing resources along transects over the continental shelf, thereby providing an opportunity to study the distribution and scavenging behaviour of seabirds in this area. We investigated the influence of fishery activities (i.e. distribution of professional fishing vessels, quantity of discards) at large scale, and oceanographic conditions on the distribution of scavenging seabirds along the continental shelf. On average, large gulls (Larus spp) and northern gannets accounted for 70 % of the seabirds scavenging on discards. We compared model predictions using oceanographic variables with those using both oceanographic variables and fishery activities, in separate for spring and autumn and group of seabirds (i.e. large gulls and northern gannets). Results suggested that the distribution of scavenging seabirds was better predicted by oceanographic variables than by fishery activities. The predicted numbers of scavenging seabirds were higher in autumn than in spring, probably due to the annual cycle of seabirds (i.e. breeding versus wintering). At large scale, oceanographic variables were better predictors of suitable habitat. However, a closer evaluation of prediction differences between the two models highlighted high anomaly values for large gulls in autumn along the coast, that may indicate over predictions. These results have provided new knowledge on the ecology of scavenging seabirds, in an area with high fishery activity, especially in the context of landing obligation.

Allergic contact dermatitis of the scalp: a review of an underdiagnosed entity.

It is hypothesized that scalp allergic contact dermatitis (ACD) in women is commonly mistaken for other disorders due to overlapping symptoms and unique clinical presentations. This study reviews the potential underdiagnosis and misdiagnosis of scalp ACD and explores ways to improve diagnostic accuracy. This study conducted an extensive literature review to identify diagnostic challenges, common misdiagnoses, and diagnostic approaches for scalp ACD, focusing on standard versus targeted patch testing techniques. Scalp ACD, often misdiagnosed as seborrheic dermatitis due to similar symptoms, has atypical presentations such as hair thinning, hair loss, and erythematous lesions affecting neighboring regions. Trichoscopy can help distinguish scalp ACD, identifying its patchy distribution of thin white scales, in contrast to the yellow scaling of seborrheic dermatitis. Standardized patch testing further contributes to diagnostic errors, with a study reporting 83% of patients who tested negative with standardized patch tests were positive when using their personal products. Individualized patch testing is more effective in identifying causative allergens and accurately diagnosing scalp ACD. It is a retrospective review. Several factors contribute to scalp ACD's misdiagnosis for conditions such as seborrheic dermatitis. The significant discrepancy in ACD detection rates between personalized and standardized patch tests in women emphasizes the importance of using patient-specific products in diagnostic testing. Incorporating scalp ACD more readily into one's differential, employing individualized patch testing with trichoscopy, and accounting for neighboring symptomatic areas are all crucial elements in improving diagnostic accuracy for scalp ACD in women.

Unraveling urban dynamics: Scaling laws of human mobility across the cityscape divide

The scaling laws of human mobility are one of the key theories of human geography. They also have important policy implications for urban-rural integration and regional economic growth. Although scaling laws of human mobility have been widely discussed, the allometric growth between urban and rural areas is overlooked. This study aims to fill the gap by investigating scaling laws and power-law distribution across urban and rural areas during urban growth with individual people's trajectory information of 13 million mobile phone users. The analysis identified a turning point at a population of 1.8 million in the Greater Bay Area, where the growth rate of aggregate travel demand stabilizes. The results show that human mobility exhibits segmented allometric growth, with superlinear growth transitioning to sublinear beyond this turning point. The disparities of travel behaviour patterns between urban and rural populations increase as cities grow. These findings would enhance the existing knowledge of the allometric scaling of human mobility.

Carbon Sequestration Capacity after Ecological Restoration of Open-Pit Mines: A Case Study in Yangtze River Basin, Jurong City, Jiangsu Province

Open-pit mining seriously damages the original vegetation community and soil layer and disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental awareness and the influence of related policies, the ecological restoration of open-pit mines has been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and small scale of single mines. However, the carbon sequestration capability of mines after ecological restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current status were determined through field investigation, the methods for sampling and data collection for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting of three aspects and nine evaluation indexes was established by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following: the establishment of the judgment matrix, calculation of the index weight, determination of the membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation results of the restoration mines were determined with the ‘excellent, good, normal and poor’ grade classification according to the evaluation standards for each index proposed considering the data of the field investigation and laboratory tests. The results indicated that (1) the evaluation results of the mines’ carbon sequestration capacity were of excellent and good grade at a proportion of 62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547, indicating the stability of the system is the main factor in the carbon sequestration capacity of the mines and the sustainability of the vegetation community and the stability of soil fixation on the slope. The proposed evaluation system effectively evaluates the short-term carbon sequestration capability of the restoration mining system according to the visual effects and the laboratory testing results, objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative analysis. The evaluation method is relatively applicable and reliable for restoration mines and can provide a reference for similar ecological restoration engineering.

Estimating Non-Stationary Extreme-Value Probability Distribution Shifts and Their Parameters Under Climate Change Using L-Moments and L-Moment Ratio Diagrams: A Case Study of Hydrologic Drought in the Goat River Near Creston, British Columbia

Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs (τ2-Precipitation and τ2-Discharge: R2 = 0.675, p-values < 0.001; τ3-Precipitation and τ3-Discharge: R2 = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming α = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future.

Exploring the relationship of colour categorization with pork colour standards under different subjective and objective conditions

The impact of using incorrect lighting while subjectively scoring pork colour with subjective standards (Japanese, Canadian, and Kodak grey) was explored. Lightness was more important than a good colour match between standards and meat. Subjective and image-based automated scoring with Canadian standards were correlated at 0.71–0.77 (P < 0.001) with significant differences in scale distribution (D = 0.14–0.46; P < 0.002), primarily with moderately dark meat. Automated scoring on full colour and greyscale images were strongly related (r = 0.83, P < 0.001) and showed matching score distributions when whole scores were used. Tracking automated colour categorization during blooming showed very good potential for reliable categorization after 1 min exposure to air for most meat colours, indicating that reliable automated on-line sorting of pork for colour is easily within reach.

Studying the Impacts of Meteorological Factors on Distribution of Cloud Horizontal Scales Based on Active Satellite

AbstractAs a significant macrophysical property, cloud horizontal scales play a role in cloud radiation, precipitation and vertical cloud overlap. Until now, however, the mechanisms behind the variations in cloud scale distribution have received far less attention. This study utilizes active satellite data from 2007 to 2016 to investigate the spatiotemporal distribution of cloud horizontal scales, and explains the variations through two meteorological factors: wind shear and atmospheric stability. Cloud scales exhibit a distinct power‐law behavior when scale break is not considered, and the power‐law exponent β is a characteristic measure of cloud scale distribution. A smaller power‐law exponent β indicates a higher frequency of large clouds. During boreal summer season, the amount of large clouds is extremely large south of the 40°S but rather small between 10°S and 20°S. As wind shear decreases or atmospheric stability increases, more large clouds occur globally. The underlying mechanisms might be associated with cloud entrainment which can be promoted by wind shear but inhibited by atmospheric stability. However, our analysis of the impacts of these two factors on cloud scale distribution across different regions and heights reveals that both wind shear and atmospheric stability play dual roles on the values of the exponent β. The potential physical mechanisms, including the effects of precipitation, are further discussed. It is observed that precipitation also exerts a dual impact on the values of the exponent β. These findings underscore the significance of considering the impacts of meteorological factors on cloud scale distribution in numerical weather prediction models.

Computer Augmented Reality Model-Based Interactive Teaching System for Chinese Line Drawing CAD

To achieve automatic target region detection, an interactive image segmentation technique has been always used as preferences. However, when it comes to the implementation of computer aided design, there is a problem with the structured method. The technique of stroke contour extraction based on boundary detection is used in this investigation. The high-frequency lifting filter improves the original image, and the grey scale and spatial distribution of the pixels are used to determine how the pixels are distributed on the pen track. The segmentation of mural lines and strokes is achieved well in a limited space. To ascertain each pixel's grey value distribution, a non-realistic rendering technique is suggested. Draw in accordance with the boundary tangential flow and each pixel's category. In conclusion, the two approaches can cover more application scenarios and yield good results in the extraction of creative materials from Chinese classical mural painting while maintaining the entire shape of the original strokes in the painting.

New Evolutionary Features of the Urban Scale Distribution in China: Based on the Seventh Census Data

New Evolutionary Features of the Urban Scale Distribution in China: Based on the Seventh Census Data

Integrated textural and geochemical analysis of igneous zircon by atom probe tomography

Mechanisms relating to growth and/or compositional modification of zircon occur at the atomic scale. For felsic igneous systems, processes responsible for growth patterns in zircon have previously remained elusive as the volume of material needed to analyze these compositional features using traditional in-situ methods is considerably larger than the typical sub-micron scale distribution of trace elements. To illuminate some of these driving forces, we characterize and quantify minor and trace element concentrations in igneous zircon grains by combining methods of cathodoluminescence (CL) imaging, electron microprobe microanalysis (EMPA) elemental maps for Hf, Y, Yb and U or Th, and atom probe tomography (APT). We focus on igneous zircon from the Chon Aike Silicic Large Igneous Province (Patagonia) that provide novel insights into (1) dissolution and re-crystallization during crustal anatexis, (2) crystallization to produce oscillatory zonation patterns that are typical of igneous zircons, and (3) the incorporation of trace element impurities (e.g., P, Be, and Al) at the nanoscale. Significantly, these APT volumes provide nanoscale sampling of boundaries between oscillatory growth zones in an igneous zircon to reveal compositional zoning of Y and, to a lesser extent P, which appear as high-angle, planar features. These concentration boundaries measured on the order of 10 to 12 nm are difficult to reconcile with proposed mechanisms for generating fine-scaled oscillations. Lastly, we fit diffusional profiles to measured Y concentrations to provide an estimate on the maximum timescales of zircon growth prior to eruption, as a function of the temperature at which diffusion occurred. When combined with known pressure-temperature-time paths for the magmatic system considered, these extremely short diffusion profiles that are resolvable by APT provide a powerful method to constrain timescales of crystal growth.

A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering

Reliability analysis plays an important role in the risk management of geotechnical engineering. For the random field-based method, it is expected that the uncertainty characterization of geo-material parameters and the realization of random field can be integrated effectively. Moreover, as the increase in measured data size is generally difficult in the field investigation of geotechnical engineering due to limitation of budget and time etc., the statistical uncertainty resulting from sparse data should be paid great attention. Therefore, taking the determination of hyper-parameters for Bayesian-based conditional random field as the breakthrough, this study proposed a reliability analysis framework to achieve the expectation above. In this proposed reliability analysis framework, the present characterization method of statistical uncertainty is improved by setting the lognormal distribution as the prior distribution of scale of fluctuation (SOF). Subsequently, the performance of statistical uncertainty characterization method is tested by a set of unconfined compressive strength (UCS) database about rocks. Then, a case study about the stability analysis of slope is employed to demonstrate the beneficial effect of the proposed reliability analysis framework. It is found that the uncertainty in both the realization of random field and the reliability analysis results can be significantly mitigated by the proposed reliability analysis framework.

Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework

Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework

Change point detection via Gaussian mixture model

Change point detection aims to find abrupt changes in time series data. These changes denote substantial modifications to the process; these can be modeled as a change in the distribution (in location, scale, or trend). Traditional changepoint detection methods often rely on a cost function to assess if a change occurred in a series. Here, change point detection is investigated in a mixture-model-based clustering framework and a novel change point detection algorithm is developed using a finite mixture of regressions with concomitant variables. Through the introduction of a label correction mechanism, the unstructured clustering-based labels are treated as ordered and distinct segment labels. This approach can detect change points in both univariate and multivariate time series, and different kinds of change can be captured using a parsimonious family of models. Performance is illustrated on both simulated and real data. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 197-219.

Oxidation model of slabs in an industrial-scale walking-beam reheating furnace with mixed loading

This paper develops a comprehensive numerical model of an industrial-scale walking-beam reheating furnace integrated with an improved oxidation model. The model demonstrates good agreement with experimental data, accurately predicting both temperature distribution and the oxidation process. Utilizing this model, the impact of mixed loading and swirl angles on the oxidation process is investigated. The average porosity of the scale, determined experimentally to be 0.25634, is used as a benchmark for the model. Comparisons of results with and without considering porosity highlight its significant impact on scale thickness distribution. This finding is crucial for the descaling process, allowing for a more accurate determination of descaling pressure and the reduction of production costs. Predicting scale distribution based solely on the temperature at a specific time is challenging due to the lag in scale accumulation. The temperature trend over an extended period is more consistent with the scale distribution. For the reheating furnace studied in this paper, the research results reveal that a swirl angle of 25° emerges as the optimal choice with industrial demands. These results underscore the importance of considering porosity and swirl angle effects on scale distribution and oxidation loss rate, offering valuable guidance for industrial processes.

Microstructure evolution in bituminous-coal pyrolysis under in situ and stress-free conditions: a comparative study

A self-made triaxial testing machine with thermal–hydraulic–mechanical–chemical (THMC) coupling and a tubular heating furnace, combined with in situ (IS) micro-computed-tomography technology was utilized in this study. The evolution of pore-fissure (PF) structure parameters (porosity, PF scale distribution, effective PF volume ratio, and permeability) of bituminous coal under stress-free (SF) and IS conditions with temperature was investigated, and then the mechanism of experimental results was analyzed. Results showed that (1) under SF conditions, at 300–550 °C, the coal samples after pyrolysis are dominated by elongated large fissures, with PF structure parameters positively correlating with temperature. After 400 °C, the number of PFs increases, with most PFs having equivalent diameter (R) ≤ 100 μm. (2) Under IS conditions, coal sample fissures are dominated by elongated large fissures at 300–350 °C and by holes at 350–600 °C. (3) Under IS conditions at 300–600 °C, the PF structure parameters of coal samples initially decrease with temperature and subsequently increase. The number of PFs fluctuates within a certain range, and the PF scale distribution dynamically shifts with temperature. (4) After 300 °C, the PF structure parameters of bituminous coal under SF and IS conditions show a bipolar distribution with temperature. Therefore, the weakening effect of stress on the PF structure of coal samples should not be overlooked during IS pyrolysis mining of coal bodies.

Normal-skew mixture-based robust probability hypothesis density filter under unknown statistical properties of noise

In multi-target tracking (MTT), the statistical properties of noise in real-world scenarios are often unknown. When noise modeling does not align with the actual noise, it can lead to inaccurate estimations of the number and locations of targets. To address this issue, we model the noise as a normal-skewed mixture (NSM) distribution (NSMD), comprising a linear combination of a normal distribution (ND) and a Gaussian scale distribution (GSD). By varying the parameters of the NSMD, it can fit arbitrary normal and/or heavy-tailed, and/or skewed nonsmooth noise. To enable the NSMD to adaptively fit noise with unknown statistical properties, we introduce a hierarchical model and a variational Bayesian (VB) approach to adjust the shape of the NSMD. The marginal likelihood function is then derived for filtering updates. Based on the NSMD and the marginal likelihood function, we propose a robust NSM-based probabilistic hypothesis density (NSM-PHD) filter to achieve improved MTT accuracy when noise statistical characteristics are unknown. Simulation results demonstrate that the NSM-PHD effectively enhances MTT accuracy under conditions of unknown noise statistical properties.

Shake‐table tests on steel moment‐resisting frames with detachable yielding zones

AbstractThis article presents an experimental campaign based on shake table tests conducted as part of the European research project FUTURE, funded in the framework of the H2020‐INFRAIA SERA program. Three steel moment‐resisting frame structures equipped with replaceable dissipative elements were tested. The experimental mockups were scaled two thirds from a reference building archetype designed per the latest draft of the upcoming version of Eurocode 8. Each mockup has different bolted end‐plate beam‐to‐column joints, namely reduced beam section (RBS), extended stiffened endplate (ESEP), and Haunched types, designed with European seismic prequalification rules. Additionally, detachable column segments at the base were designed with Reduced Column Sections (RCS) to localize the plastic deformation and enforce the intended global dissipation mechanism. The slab was detailed with reduced areas around the columns to minimize the direct composite action at beam‐to‐column joints. The mockups were subjected to incremental near‐field excitations with combined horizontal and vertical components. In this article, the experimental campaign is first described, providing details on the mockup's geometry, design, scaling, ground excitations, and sensor distribution. Then, the observed global and local responses are discussed. The results demonstrate that the global response of the mockups was significantly influenced by the type of beam‐to‐column joints, which exhibited the expected damage. The RCS configuration in the column bases also showed satisfactory behavior without leading to local or global instabilities throughout the performed tests.