Width Scaling Research Articles

Ontogenetic scaling of disc width with total length in west African batoids

AbstractMorphological scaling describes changes in the size or shape of one morphological character (e.g. mass, length, width, area etc.) as another increases in size. Understanding how morphological characters scale with body size can shed light on how natural selection influences morphology, and the nature of ecomorphological relationships through ontogeny. Batoids (Elasmobranchii: Batoidea) are a highly specialised lineage of cartilaginous fishes displaying extreme dorsoventral flattening. Despite this, little is known about morphological scaling in batoids compared to sharks. In this study we test the relationship between disc width and total length in five batoid species (Torpedo torpedo, Mobula tarapacana, Fontitrygon margarita, Raja parva, Rhinobatos irvinei) representing four orders that differ in both ecology and morphology, measured from artisanal fisheries in Western and Central Ghana. Whilst a lack of existing ecological data presents some limitations, our results are broadly consistent with ecomorphological theory previously applied to sharks. Moreover, we find that for some lineages (including some myliobatiform taxa) total length may represent a valid proxy for estimating overall body size. This finding has applications for body size and shape estimation in partially processed batoids obtained from fishing camps, and extinct taxa known only from incomplete or fragmented remains.

Reliability and Construct Validity of the Work Rehabilitation Questionnaire Domains in Patients with Persistent Low Back Pain

Abstract Purpose The Work Rehabilitation Questionnaire (WORQ) assesses patient functioning, including psychological, physical, and cognitive limitations. This study evaluates the WORQ domains in individuals with persistent low back pain (LBP), focusing on reliability and construct validity. Methods Individuals aged 18–65 with LBP completed WORQ and the workability index single item. A subgroup undertook sit-to-stand and 6-min walking tests and re-evaluated WORQ after 14 days. Reliability was assessed through internal consistency (McDonald’s omega and Cronbach’s alpha), test–retest reliability, and smallest detectable change. Construct validity was analyzed via Spearman’s rank correlation and known group validity, with physical functioning also examined against sit-to-stand and 6-min walk test results for sensitivity/specificity. Floor and ceiling effects were assessed through classical and scale width methods. Results Of 425 participants, 149 completed physical tests, and 102 re-assessed WORQ. McDonald’s omega and Cronbach’s alpha indicated high internal consistency (0.92–0.96) with strong test–retest reliability (intraclass-correlation coefficients: 0.74–0.82). The smallest detectable change ranged from 4.62 to 7.82. Predictions from 7 out of 8 hypotheses were confirmed. Notable differences in domain scores were observed based on disability level and sick leave status, with varied diagnostic performance in physical functioning items. Potential floor effects were noted using the scale width method. Conclusions The WORQ demonstrated good reliability and satisfactory validity in assessing work-related functioning in individuals with persistent LBP. These findings support its use as a comprehensive tool for evaluating psychological, physical, and cognitive limitations. However, varied diagnostic performance in physical functioning items and potential floor effects suggest cautious interpretation in diverse clinical settings.

Manipulating density pedestal structure to improve core–edge integration towards low collisionality

Abstract DIII-D experiments have achieved promising core–edge integrated plasma scenarios which combine a high-temperature low-collisionality pedestal (pedestal top temperature Te ,ped > 0.8 keV and collisionality ν*ped < 1) with a partially detached divertor by leveraging the benefits of a low-density-gradient pedestal in a closed divertor. It is found that with a closed divertor and high heating power, strong gas puffing to achieve detachment moves the peak density gradient outward with respect to the maximum gradient of electron temperature and reduces the density gradient at the pedestal region, which correlates with shallow pedestal fuelling due to the closed divertor geometry. In high-current plasmas in particular, the pedestal top density is found to change little with gas puffing while the separatrix, density increases to allow access for divertor detachment. The separation between density and temperature pedestals results in a high- ηe well above the electron-temperature-gradient stability threshold. Electron turbulence is found to be enhanced in the pedestal and correlated with high ηe resulting from the pedestal shift. The pedestal is wider than the EPED scaling. A revised empirical width scaling is derived based on the combination of EPED scaling with ηe and highlights the important role of additional turbulence on the pedestal structure. The wide temperature pedestal facilitates the achievement of a high-temperature, low-collisionality pedestal and high global performance. Simultaneously, the outward shift of the density pedestal facilitates access to detached divertor conditions with low temperature and heat flux towards the target plate. This approach may be promising for closing the core–edge integration gap for future fusion reactors, which may have a weak-gradient density pedestal due to the highly opaque boundary plasmas.

The role of substrata width and millimeter scale surface micro-structure in the attachment of juvenile mussels

The global mussel aquaculture industry is constrained by the unreliable supply of wild seed mussels and high losses of seed mussels shortly after they are seeded onto coastal farms. The success in the collection of the settling larvae of mussel in the wild and the subsequent on-growing of early seed mussels in aquaculture are largely determined by the settlement and attachment behaviour of the larval and juvenile mussels, which is strongly influenced by the physical structure of their attachment substrata, especially filamentous substrata. This study aimed to identify an ideal set of morphological characteristics of filamentous substrata that would promote the improved attachment of early mussels and contribute to reducing the shortage of seed mussels in the New Zealand green-lipped mussel (Perna canaliculus) aquaculture industry. Artificial filamentous substrata with varying branch width and textured surfaces were tested for their ability to promote the attachment of wild juvenile mussels. The attachment of early juvenile mussels to 1.6 mm wide filamentous substrate was six times higher than their wider counterparts (i.e., 3.7, 5.6, 7.4, and 9.5 mm). There was also a higher proportion of small-size mussels (<0.99 mm shell length) on the 1.6 mm substrata. Furthermore, the filamentous substrata with regularly ridged surface contours (1 mm spacings) tended to attract more juvenile mussels. However, this preference was not consistent for small-size mussels on filaments of thinner width (i.e., 0.4, 0.8, 1.6, and 3.7 mm). Overall, the dimensions of filamentous substrates identified through this study will be valuable for improving the design of artificial substrata used in mussel aquaculture for improving its effectiveness for the attachment of seed mussels.

Questioning the appropriate angle of view to represent the visual perception of streets: possible answers from a preliminary study

Despite the rising prevalence of streetscape images as key material in architectural and urban research, their selection of angle of view (AoV) still mostly relies on arbitrary choice. An AoV determines the trade-off between the visible range and level of detail that the image can convey within its limited frame and, thereby, the overall visual information and perception about the street. Given the need for an empirically supported guidance to such previously overlooked dilemma, this study questioned and tentatively drew which AoV would be appropriate in its representation. On-site street surveys comparing the street’s in-situ view and perception with its pictures taken at five different AoVs were conducted for 120 respondents. Results suggest that the appropriate AoV may not be fixed, but differ according to the spatial scale (width and height) of the street and its buildings. A set of practical guidelines readily applicable for AoV selection is further provided.

Study on debris flow discharge characteristics of check dam spillway

After a check dam is filled with sediment, the debris flow will flow out from the spillway. Insufficient discharge capacity of the spillway can lead to debris flow scouring the dam's shoulder and foundation. Therefore, it is necessary to study the discharge capacity of the spillway. It is of great importance for further optimizing the structural design of the check dam. This article analyzes discharge characteristics of debris flow of a spillway through a series of flume experiments. According to the motion states, it can be divided into five stages: contact stage of the spillway, flushing and climbing stage, increasing and reflux stage, stable discharge stage, and final discharge stage. We analyzed the influencing factors of the discharge capacity, including the type of spillway, bulk density of debris flow, scale of debris flow, flume gradient, and effective opening width of the spillway. Furthermore, we derived a calculation formula of discharge for the spillway based on Bernoulli's principle. Finally, we considered the influence of the ratio of flow area and Froude number to the discharge coefficient and established a calculation method of discharge. Consequently, we compared the calculated results with the measured results and found them to be in good agreement. Our research results provide a quantitative calculation method for the design of spillways and energy dissipation and erosion prevention projects under check dams, which is beneficial for improving engineering value and for better serving disaster prevention and reduction efforts.

Scale Characteristics of Six Fish Species of the Genus Cyprinion (Teleostei: Cypriniformes): A Microscopic Analysis.

This study investigated the morphological characteristics of scales in six Cyprinion species, using light and scanning electron microscopy focusing on key features such as scale type, key scales, lateral line scales, radius/radii, rostral margin, focus, circuli, lepidonts, tubercles, and scale indices. The research analyzed the scales using ultramicroscopy and light microscopy imaging, categorizing them based on size classes and body regions. The morphological variations in scale characteristics were examined across different species, regions, and size classes. Notable findings included the tetra-sectioned form of scales, representing a unique characteristic of the Cyprinion genus. Morphological changes in scale features were observed with fish growth, particularly in the overall shape, focus shape, and size. Quantitative analysis revealed variations in average relative scale length and width among different species, regions, and size classes. The study utilized canonical discriminant analysis for multivariate assessment, classifying the species into distinct groups based on morphometric indices. The findings contribute to the understanding of scale morphology in Cyprinion species and exploring morphological variation between the examined species.

Explaining neural scaling laws

The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets. We also observe several empirical relationships between datasets and scaling exponents under modifications of task and architecture aspect ratio. Our work provides a taxonomy for classifying different scaling regimes, underscores that there can be different mechanisms driving improvements in loss, and lends insight into the microscopic origin and relationships between scaling exponents.

Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification

Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with the potential to reduce the clinical burden and improve patient care. However, further research is required before they can be used in clinical settings, particularly regarding the impact of the number of training samples and model parameters on their testing error. To address this, we present a comprehensive study of the empirical scaling behaviour of ConvNets for EEG pathology classification. We analysed the testing error with increasing the training samples and model size for four different ConvNet architectures. The focus of our experiments is width scaling, and we have increased the number of parameters to up to 1.8 million. Our evaluation was based on two publicly available datasets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus, which together contain 10,707 training samples. The results show that the testing error follows a saturating power-law with both model and dataset size. This pattern is consistent across different datasets and ConvNet architectures. Furthermore, empirically observed accuracies saturate at 85%–87%, which may be due to an imperfect inter-rater agreement on the clinical labels. The empirical scaling behaviour of the test performance with dataset and model size has significant implications for deep EEG pathology classification research and practice. Our findings highlight the potential of deep ConvNets for high-performance EEG pathology classification, and the identified scaling relationships provide valuable recommendations for the advancement of automated EEG diagnostics.

The age and growth information of a ctenoid scale fossil from the Upper Cretaceous Nenjiang Formation in Songliao Basin, China.

The study of morphological characteristics and growth information in fish scales is a crucial component of modern fishery biological research, while it has been less studied in fossil materials. This paper presents a detailed morphological description and growth analysis of a fossil ctenoid scale obtained from the Upper Cretaceous Campanian lacustrine deposits in northeastern China. The morphological features of this fossil scale are well-preserved and consistent with the structures found in ctenoid scales of extant fish species and display prominent ring ornamentation radiating outward from the central focus, with grooves intersecting the rings. A comparative analysis of the morphological characteristics between the fossil ctenoid scale and those well-studied extant fish Mugilidae allows us to explore the applicability of modern fishery biological research methods to the field of fossil scales. The scale length, scale width, the vertical distance from the focus to the apex of the scale, and the total number of radii have been measured. The age of the fish that possessed this ctenoid scale has been estimated by carefully counting the annuli, suggesting an age equal to or more than seven years. The distribution of growth rings on the scale potentially reflects the warm paleoclimatic condition and fish-friendly paleoenvironment prevalent during that period. This paper, moreover, serves as a notable application of fishery biological methods in the examination of fossil materials.

Early Blight and Late Blight Disease Detection in Potato Using Efficientnetb0

Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being the main producers. Numerous leaf diseases harm the crop during its production. A typical Indian farmer lacks the tools necessary to detect Leaf Disease before damage is done. On a dataset of potato leaf images retrieved from Kaggle, we employed the EfficientNetB0 of Deep Learning to address this problem. This model uses width scaling and resolution scaling apart from depth scaling to perform the classification. Our work mainly focuses on the diseases Early Blight and Late Blight, two serious potato diseases. Early blight Spots start off as tiny, dry, dark, and papery specks that develop into brown to black, circular to oval-shaped regions. Veins that round the spots frequently give them an angular appearance. Late blight syntoms appear as small, light to dark green and round to irregularly shaped. Water-soaked patches are the first signs of late blight. The Data Collection has 2152 pictures in total, 2000 of which are diseased and 152 of which are healthy. The deep learning model provides a testing accuracy of 99.05%, which is higher than several widely used techniques available to provide farmers with knowledge about correct diseases well in time.

3D Point Cloud and GIS Approach to Assess Street Physical Attributes

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces.

Impact of Gate Oxide Thickness on Flicker Noise (1/f) in PDSOI n-channel FETs

This work reports the impact of gate oxide thickness on flicker noise (1/f) in 45-nm RFSOI NFET devices. In addition, the effect of finger width scaling on 1/f noise parameters is studied in linear region. The dominant source of 1/f noise is also analyzed. It is observed that thin oxide devices show carrier number fluctuation; however, for thick oxide devices, correlated number-mobility govern the noise. Extracted trap densities using 1/f noise show higher volume trap densities in thin oxide devices. Moreover, trap distribution behavior is analyzed using frequency exponent. Further, GLOBALFOUNDRIES PDK is utilized to model 1/f noise behavior of the devices.

Number-state reconstruction with a single single-photon avalanche detector

Single-photon avalanche detectors (SPADs) are crucial sensors of light for many fields and applications. However, they are not able to resolve photon number, so typically more complex and more expensive experimental setups or devices must be used to measure the number of photons in a pulse. Here, we present a methodology for performing photon number-state reconstruction with only one SPAD. The methodology, which is cost-effective and easy to implement, uses maximum-likelihood techniques with a detector model whose parameters are measurable. We achieve excellent agreement between known input pulses and their reconstructions for coherent states with up to ≈10 photons and peak input photon rates up to several Mcounts/s. When detector imperfections are small, we maintain good agreement for coherent pulses with peak input photon rates of over 40 Mcounts/s, greater than one photon per detector dead time. For anti-bunched light, the reconstructed and independently measured pulse-averaged values of g(2)(0) are also consistent with one another. Our algorithm is applicable to light pulses whose pulse width and correlation time scales are both at least a few detector dead times. These results, achieved with single commercially available SPADs, provide an inexpensive number-state reconstruction method and expand the capabilities of single-photon detectors.

MACHINE LEARNING FOR AUTOMATIC EXTRACTION OF WATER BODIES USING SENTINEL-2 IMAGERY

Context. Given the aggravation of environmental and water problems, there is a need to improve automated methods for extracting and monitoring water bodies in urban ecosystems. The problem of efficient and automated extraction of water bodies is becoming relevant given the large amount of data obtained from satellite systems. The object of study is water bodies that are automatically extracted from Sentinel-2 optical satellite images using machine learning methods.&#x0D; Objective. The goal of the work is to improve the efficiency of the process of extracting the boundaries of water bodies on digital optical satellite images by using machine learning methods.&#x0D; Method. The paper proposes an automated information technology for delineating the boundaries of water bodies on Sentinel-2 digital optical satellite images. The process includes eight stages, starting with data download and using topographic maps to obtain basic information about the study area. Then, the process involved data pre-processing, which included calibrating the images, removing atmospheric noise, and enhancing contrast. Next, the EfficientNet-B0 architecture is applied to identify water features, facilitating optimal network width scaling, depth, and image resolution. ResNet blocks compress and expand channels. It allows for optimal connectivity of large-scale and multi-channel links across layers. After that, the Regional Proposal Network defines regions of interest (ROI), and ROI alignment ensures data homogeneity. The Fully connected layer helps in segmenting the regions, and the Fully connected network creates binary masks for accurate identification of water bodies. The final step of the method is to analyze spatial and temporal changes in the images to identify differences, changes, and trends that may indicate specific phenomena or events. This approach allows automating and accurately identifying water features on satellite images using machine learning.&#x0D; Results. The implementation of the proposed technology is development through Python software development. An assessment of the technology’s accuracy, conducted through a comparative analysis with existing methods, such as water indices and K-means, confirms a high level of accuracy in the period from 2017 to 2023 (up to 98%). The Kappa coefficient, which considers the degree of consistency between the actual and predicted classification, confirms the stability and reliability of our approach, reaching a value of 0.96.&#x0D; Conclusions. The experiments confirm the effectiveness of the proposed automated information technology and allow us to recommend it for use in studies of changes in coastal areas, decision-making in the field of coastal resource management, and land use. Prospects for further research may include new methods that seasonal changes and provide robustness in the selection and mapping of water surfaces.

A physics-based model for fluvial valley width

Abstract. The width of fluvial valley floors is a key parameter to quantifying the morphology of mountain regions. Valley floor width is relevant to diverse fields including sedimentology, fluvial geomorphology, and archaeology. The width of valleys has been argued to depend on climatic and tectonic conditions, on the hydraulics and hydrology of the river channel that forms the valley, and on sediment supply from valley walls. Here, we derive a physically based model that can be used to predict valley width and test it against three different datasets. The model applies to valleys that are carved by a river migrating laterally across the valley floor. We conceptualize river migration as a Poisson process, in which the river changes its direction stochastically at a mean rate determined by hydraulic boundary conditions. This approach yields a characteristic timescale for the river to cross the valley floor from one wall to the other. The valley width can then be determined by integrating the speed of migration over this timescale. For a laterally unconfined river that is not uplifting, the model predicts that the channel-belt width scales with river flow depth. Channel-belt width corresponds to the maximum width of a fluvial valley. We expand the model to include the effects of uplift and lateral sediment supply from valley walls. Both of these effects lead to a decrease in valley width in comparison to the maximum width. We identify a dimensionless number, termed the mobility–uplift number, which is the ratio between the lateral mobility of the river channel and uplift rate. The model predicts two limits: at high values of the mobility–uplift number, the valley evolves to the channel-belt width, whereas it corresponds to the channel width at low values. Between these limits, valley width is linked to the mobility–uplift number by a logarithmic function. As a consequence of the model, valley width increases with increasing drainage area, with a scaling exponent that typically has a value between 0.4 and 0.5, but can also be lower or higher. We compare the model to three independent datasets of valleys in experimental and natural uplifting landscapes and show that it closely predicts the first-order relationship between valley width and the mobility–uplift number.

DeLiVoTr: Deep and light-weight voxel transformer for 3D object detection

The image-based backbone (feature extraction) networks downsample the feature maps not only to increase the receptive field but also to efficiently detect objects of various scales. The existing feature extraction networks in LiDAR-based 3D object detection tasks follow the feature map downsampling similar to image-based feature extraction networks to increase the receptive field. But, such downsampling of LiDAR feature maps in large-scale autonomous driving scenarios hinder the detection of small size objects, such as pedestrians. To solve this issue we design an architecture that not only maintains the same scale of the feature maps but also the receptive field in the feature extraction network to aid for efficient detection of small size objects. We resort to attention mechanism to build sufficient receptive field and we propose a Deep and Light-weight Voxel Transformer (DeLiVoTr) network with voxel intra- and inter-region transformer modules to extract voxel local and global features respectively. We introduce DeLiVoTr block that uses transformations with expand and reduce strategy to vary the width and depth of the network efficiently. This facilitates to learn wider and deeper voxel representations and enables to use not only smaller dimension for attention mechanism but also a light-weight feed-forward network, facilitating the reduction of parameters and operations. In addition to model scaling, we employ layer-level scaling of DeLiVoTr encoder layers for efficient parameter allocation in each encoder layer instead of fixed number of parameters as in existing approaches. Leveraging layer-level depth and width scaling we formulate three variants of DeLiVoTr network. We conduct extensive experiments and analysis on large-scale Waymo and KITTI datasets. Our network surpasses state-of-the-art methods for detection of small objects (pedestrians) with an inference speed of 20.5 FPS.

Bimodal orientation distribution and head-tail asymmetry of a sample of filamentary molecular clouds

ABSTRACT The morphology of molecular clouds is crucial for understanding their origin and evolution. In this work, we investigate the morphology of the filamentary molecular clouds (filaments for short) using a portion of the 12CO(J = 1 − 0) data from the Milky Way Imaging Scroll Painting project. The data cover an area spanning 104.75° &amp;lt; l &amp;lt; 150.25°, |b| &amp;lt; 5.25° in Galactic coordinates, with VLSR ranging from −95 to 25 kms−1. Our primary focus is on the orientation and morphological asymmetry of the filaments. To achieve this, we apply several criteria on the data to create a sample of filaments with well-defined straight shape, and we use elliptical fitting to obtain the orientation of each filament, with an estimated error of ∼1.6° for the orientation. We find that the filament orientation with respect to the Galactic plane exhibits a bimodal distribution, a double-Gaussian fitting of which has two centres located at −38.1° and 42.0°, with 1σ of the two Gaussian functions being 35.4° and 27.4°. We do not find significant correlation between the orientation and other parameters, including the Galactic coordinates, radial velocity, velocity width, and physical scale. A considerable fraction of filaments (≳40 per cent) display head-tail asymmetry, which suggests that mass concentration tends to occur at one end of the filaments.

Backbone search for object detection for applications in intrusion warning systems

&lt;span&gt;In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset. &lt;/span&gt;

Localisation of gamma ray bursts using AstroSat Mass Model

Abstract The Cadmium Zinc Telluride Imager (CZTI) aboard AstroSat has good sensitivity to Gamma Ray Bursts (GRBs), with close to 600 detections including about 50 discoveries undetected by other missions. However, CZTI was not designed to be a GRB monitor and lacks localisation capabilities. We introduce a new method of localising GRBs using ‘shadows’ cast on the CZTI detector plane due to absorption and scattering by satellite components and instruments. Comparing the observed distribution of counts on the detector plane with simulated distributions with the AstroSat Mass Model, we can localise GRBs in the sky. Our localisation uncertainty is defined by a two-component model, with a narrow Gaussian component that has close to 50% probability of containing the source, and the remaining spread over a broader Gaussian component with an 11.3 times higher σ. The width (σ) of the Gaussian components scales inversely with source counts. We test this model by applying the method to GRBs with known positions and find good agreement between the model and observations. This new ability expands the utility of CZTI in the study of GRBs and other rapid high-energy transients.