ABSTRACT The format of the efficient frontier is an important measure of technical efficiency; additionally, it determines the type of return to scale verified by the model. The classical Data Envelopment Analysis (DEA) model, CCR (Charnes et al., 1978), assumes constant returns to scale; conversely, the BCC (Banker et al., 1984) model presents a concave downward efficient frontier that presumes variable returns to scale. This study examines how different returns to scale can be revealed in DEA, considering the possibility of the existence of a concave upward efficient frontier. This kind of frontier, not yet explored by the DEA literature, can also represent viable production, seeing that an increase of the inputs causes an increase of the outputs. Considering this, a concave upward efficient frontier presents a variable return to scale, but with different characteristics from those of the concave downward BCC efficient frontier. This proposal is important because it considers the possibility of an efficient frontier that represents different samples of decision-making units (DMUs). An upward curve would better represent DMUs of smaller production scales that have increased marginal productivity but cannot act as efficiently as larger scale units.

The global smart forklift scale market is experiencing robust growth, driven by increasing demand for efficient warehouse management and inventory control across diverse industries. The market, currently valued at approximately $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This expansion is fueled by several key factors, including the rising adoption of automation and digitization in logistics and warehousing, the growing need for real-time data on material handling operations, and stringent regulatory compliance requirements for accurate weight measurements. The preference for advanced features like wireless connectivity, data integration with warehouse management systems (WMS), and improved accuracy compared to traditional methods is significantly driving market growth. Furthermore, the increasing focus on optimizing supply chain efficiency and reducing operational costs is bolstering the adoption of smart forklift scales across various sectors including manufacturing, construction, and e-commerce fulfillment centers. The market segmentation reveals significant opportunities across different types of scales (hydraulic forklift scales, load cell forklift scales, and others) and applications (warehousing, logistics, manufacturing, and construction). While warehousing and logistics currently dominate market share, the manufacturing and construction sectors are witnessing rising adoption rates driven by enhanced productivity and safety demands. Leading players like RAVAS, Mettler Toledo, and Avery Weigh-Tronix are actively shaping the market landscape through continuous product innovation, strategic partnerships, and expansion into emerging markets. Geographical analysis suggests strong growth potential in developing economies of Asia-Pacific and other regions, mirroring the expansion of industrial activity and improved infrastructure. However, factors like the high initial investment cost of smart forklift scales and the need for skilled personnel for implementation and maintenance could pose certain restraints to market growth in the coming years.

U.S. Industrial Scale Market size was valued at USD 618.10 Million in 2024 and is projected to reach USD 953.78 Million by 2032, growing at a CAGR of 5.67% from 2025 to 2032.Industrial scales are nothing but a heavy-duty weighing equipment capable of withstanding large-scale operations in manufacturing, logistics, agriculture, and recycling industries. Industrial scales offer accurate measurements for a variety of applications, such as inventory management, quality control, and process automation. They are constructed to be durable and accurate, with advanced load cells and digital interfaces to provide reliable performance in demanding industrial environments. Industrial scales play a significant role in ensuring continued compliance with industry regulations and general operational efficiency.The market for Industrial Scale in the U.S. is categorized into seven types: Bench Scales, Floor Scales, Crane Scales, Pallet Scales, Checkweighers, Weighbridges (Truck Scales), and Other Types. Bench scales dominate the U.S. industrial scale market due to their versatility, precision, compact design, and advanced integration capabilities, making them ideal for varied industrial applications.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset is the most current national compilation of catchment scale land use data for Australia (CLUM), as at March 2014. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). Catchment scale land use data was produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. The date of mapping (1997 to 2012) and scale of mapping (1:25 000 to 1:250 000) vary, reflecting the source data capture date and scale. This information is provided in a supporting polygon dataset.

The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management (ALUM) Classification version 7, a three-tiered hierarchical structure. There are five primary classes, identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Primary and secondary levels relate to the principal land use. Tertiary classes may include additional information on commodity groups, specific commodities, land management practices or vegetation information. The primary, secondary and tertiary codes work together to provide increasing levels of detail about the land use. Land may be subject to a number of concurrent land uses. For example, while the main management objective of a multiple-use production forest may be timber production, it may also provide conservation, recreation, grazing and water catchment land uses. In these cases, production forestry is commonly identified in the ALUM code as the prime land use.

The operational scales of catchment scale mapping vary according to the intensity of land use activities and landscape context. Scales range from 1:10 000 and 1:25 000 for irrigated and peri-urban areas, to 1:100 000 for broadacre cropping regions and 1:250 000 for the semi-arid and arid pastoral zone. The date of mapping generally reflects the intensity of land use. The most current mapping occurs in intensive agricultural areas; older mapping generally occurs in the semi-arid and pastoral zones.The primary classes of land use in the ALUM Classification are:

Conservation and natural environments-land used primarily for conservation purposes, based on maintaining the essentially natural ecosystems present;

Production from relatively natural environments-land used mainly for primary production with limited change to the native vegetation;

Production from dryland agriculture and plantations-land used mainly for primary production based on dryland farming systems;

Production from irrigated agriculture and plantations-land used mostly for primary production based on irrigated farming;

Intensive uses-land subject to extensive modification, generally in association with closer residential settlement, commercial or industrial uses;

Water-water features (water is regarded as an essential aspect of the classification, even though it is primarily a land cover type, not a land use).

The following areas have been updated since the November 2012 release: the entire state of Victoria; Queensland natural resource management regions Border Rivers-Maranoa, Condamine, South East Queensland (part), and South West Queensland.

Purpose

Land use information is critical to developing sustainable long-term solutions for natural resource management, and is used to underpin investment decisions. Users include local government, catchment authorities, emergency services, quarantine and pest management authorities, industry and community groups. Landscape processes involving soils and water generally operate at catchment scale. Land use information at catchment scale therefore has an important role to play in developing effective solutions to Australia's natural resource management issues.

Dataset History

Lineage:

ABARES has produced this raster dataset from vector catchment scale land use data provided by state and territory agencies, as follows: Land Use: New South Wales (2009); Land Use Mapping of the Northern Territory 2008 (LUMP 2008); Land use mapping - Queensland current (January 2014); Land Use South Australia 2008; Tasmanian Summer 2009/2010 Land Use; Victorian Land Use Information System (VLUIS) 2010 version 4; Land Use in Western Australia, Version 5, (1997); and, Land Use in Western Australia, v7 (2008). Links to land use mapping datasets and metadata are available at the ACLUMP data download page at http://www.daff.gov.au/abares/aclump/pages/land-use/data-download.aspx State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in the document 'Guidelines for land use mapping in Australia: principles, procedures and definitions, Edition 4'. Specifically, the attributes adhere to the ALUM classification, version 7. For Victoria, ABARES converted the VLUIS vector data to the ALUM classification, based on an agreed method using Valuer General Victoria land use codes, land cover and land tenure information. This method has been updated since the previous release. All contributing polygon datasets were gridded by ABARES on the ALUM code and mosaiced to minimise resampling errors. NODATA voids in Sydney, Adelaide and parts of the Australian Capital Territory were filled with Australian Bureau of Statistics Mesh blocks land use attributes with modifications based on: 1:250 000 scale topographic data for built up areas from GEODATA TOPO 250K Series 3 (Geoscience Australia 2006); land tenure data from Tenure of Australia's Forests (ABARES 2008); and, native and plantation forest data from Forests of Australia (ABARES 2008). All other NODATA voids were filled using data from Land Use of Australia, Version 4, 2005/2006 (ABARES 2010).

Land use mapped should be regarded as a REPRESENTATION of land use only. The CLUM data shows a single dominant land use for each area mapped, even if multiple land uses occur within that area. The CLUM data is produced from datasets compiled for various dates from 1997 to 2012. The CLUM data is produced from datasets compiled at various scales from 1:25 000 to 1:2 500 000

Dataset Citation

Australian Bureau of Agricultural and Resource Economics and Sciences (2014) Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/6f72f73c-8a61-4ae9-b8b5-3f67ec918826.

Humans have elevated global extinction rates and thus lowered global-scale species richness. However, there is no a priori reason to expect that losses of global species richness should always, or even often, trickle down to losses of species richness at regional and local scales, even though this relationship is often assumed. Here, we show that scale can modulate our estimates of species richness change through time in the face of anthropogenic pressures, but not in a unidirectional way. Instead, the magnitude of species richness change through time can increase, decrease, reverse, or be unimodal across spatial scales. Using several case studies, we show different forms of scale-dependent richness change through time in the face of anthropogenic pressures. For example, Central American corals show a homogenization pattern, where small scale richness is largely unchanged through time, while larger scale richness change is highly negative. Alternatively, birds in North America showed a differentiation effect, where species richness was again largely unchanged through time at small scales, but was more positive at larger scales. Finally, we collated data from a heterogeneous set of studies of different taxa measured through time from sites ranging from small plots to entire continents, and found highly variable patterns that nevertheless imply complex scale-dependence in several taxa. In summary, understanding how biodiversity is changing in the Anthropocene requires an explicit recognition of the influence of spatial scale, and we conclude with some recommendations for how to better incorporate scale into our estimates of change.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

The livestock weighing scale market is experiencing robust growth, driven by increasing demand for efficient livestock management and improved traceability in the agricultural sector. The market, valued at approximately $1.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated market value exceeding $2.8 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of precision livestock farming techniques, stricter regulations regarding animal welfare and food safety, and the growing need for data-driven decision-making in livestock operations. Technological advancements, such as the integration of smart sensors and IoT capabilities in weighing scales, further contribute to market growth by providing real-time data on animal weight, health, and feed efficiency. Leading manufacturers like Avery Weigh-Tronix, Prime Scales, and others are actively investing in research and development to enhance the accuracy, durability, and functionality of their livestock weighing scales, catering to the diverse needs of various livestock farming operations, from small-scale farms to large-scale industrial facilities. Market restraints include the high initial investment cost associated with advanced weighing systems and the need for skilled personnel to operate and maintain these sophisticated technologies. However, the long-term benefits of improved livestock management and enhanced productivity outweigh these initial challenges, driving market adoption. Segmentation within the market includes different types of scales (e.g., platform scales, electronic scales, animal identification systems), livestock types (e.g., cattle, swine, poultry), and geographic regions. The market is geographically diverse, with significant growth anticipated across regions like North America, Europe, and Asia-Pacific, driven by varying levels of agricultural development and technological adoption.

Animal Scale Market Outlook

The global animal scale market size was valued at USD 3.2 billion in 2023 and is projected to reach USD 4.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.0% during the forecast period from 2024 to 2032. Growth in this market is driven by increasing attention to animal health and the rising need for precise weight measurement in various animal husbandry and veterinary applications.

The demand for animal scales is primarily driven by the growing awareness about animal health and welfare. As owners and caretakers of livestock, pets, and exotic animals become more concerned with the health and well-being of their animals, the need for accurate weight measurement tools has surged. These tools are essential for monitoring growth, administering medication, and ensuring proper nutrition, which are crucial for maintaining the overall health of animals.

Another significant growth factor is the expansion of the veterinary services market. With the increasing number of veterinary clinics and hospitals, there is a greater need for reliable and accurate weighing solutions. These scales not only help in regular health check-ups but also play a pivotal role in diagnosing and treating various medical conditions. The growing trend of pet ownership, particularly in urban areas, further fuels the demand for animal scales in veterinary clinics and hospitals.

The advancements in scale technology, including digital and smart scales, have also contributed to the market growth. These advanced scales offer features such as wireless connectivity, data storage, and integration with health management software, making them more efficient and user-friendly. The adoption of these technologically advanced scales is particularly evident in research laboratories and high-end veterinary clinics, which require precise and sophisticated equipment for their operations.

Regionally, North America holds a significant share of the animal scale market due to the high adoption rate of advanced veterinary technologies and the presence of a large number of veterinary clinics and hospitals. Europe follows closely, driven by stringent animal welfare regulations and a well-established veterinary infrastructure. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rising awareness about animal health, increasing disposable income, and the growing pet population.

Product Type Analysis

Animal scales are available in various types, including portable animal scales, platform animal scales, bench animal scales, and others. Portable animal scales are particularly popular due to their convenience and ease of use. These scales can be easily transported and set up, making them ideal for use in various settings, including farms, veterinary clinics, and zoos. The portability factor is a significant advantage for veterinarians and animal caretakers who need to weigh animals in different locations without the hassle of moving heavy equipment.

Platform animal scales are widely used in farms and research laboratories due to their robustness and ability to weigh larger and heavier animals. These scales typically feature a large, flat surface that can accommodate animals of various sizes, making them suitable for livestock and larger exotic animals. The durability and high weight capacity of platform scales make them a preferred choice for applications that require frequent and heavy-duty weighing.

Bench animal scales are commonly used in veterinary clinics and zoos for weighing smaller animals such as pets and smaller exotic species. These scales are designed to be compact and offer precise measurements, making them ideal for environments where space is limited. Bench scales often come with features like digital displays and tare functions, which enhance their usability and accuracy in clinical settings.

Other types of animal scales include custom scales designed for specific applications or animal types. These scales may incorporate unique features or designs to meet the particular needs of certain animals or operational environments. For example, scales designed for aquatic animals may include waterproof components, while scales for birds may feature perches or other specialized weighing platforms. The diversity in product types ensures that there is a suitable weighing solution for virtually any animal and application.

Report Scope

The Global Truck Scale Market size is expected to reach $2.76 billion by 2032, rising at a market growth of 4.9% CAGR during the forecast period.

Key Highlights:

The North America market dominated Global Truck Scale Market in 2024, accounting for a 35.35% revenue share in 2024. The U.S. mar

The evergreen and deciduous broadleaved mixed forests (EDBMFs) belong to one of the ecosystems most sensitive to environmental change, however, little is known about the environmental determinants for their plant diversity and forest structure at different habitat types and spatial scales. Here, we used data from a 15-ha (300×500 m) forest dynamic plot (FDP) of an old-growth EDBMF to examine the patterns and determinants of the three community features (stem abundance, rarefied species richness and basal area) in three habitat types (ridge, hillside and foothill) and at three spatial scales (20×20 m, 50×50 m and 100×100 m). We found that the three community features significantly changed with habitat type, but only one of them (rarefied richness) changed with scale. Among spatial scales, the principle environmental factors that widely affected community features were pH, SOM and TP, while these effects only taken place at certain habitat. Variations in the three community features explained by soil conditions were generally greater than those explained by topographical conditions. With changes in habitat type, the proportion of variations explained by environmental conditions was 31-53%, 8-25%, and 18-26% for abundance, rarefied richness and basal area (BA), respectively. With increasing spatial scale, the variations explained by environmental conditions were 44-75% for abundance, 28-95% for rarefied richness, and 18-86% for BA. Our study demonstrated that environmental factors had great impacts on the plant diversity and forest structure in the EDBMFs, especially the soil factors such as pH. In addition, the importance of the environmental determinants on these community features was highly related to the spatial scale.

A Large-Scale Dataset for Segmentation and Classification

Authors: O. Ulucan, D. Karakaya, M. Turkan Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir, Turkey Corresponding author: M. Turkan Contact Information: mehmet.turkan@ieu.edu.tr

• Paper : A Large-Scale Dataset for Fish Segmentation and Classification

General Introduction

This dataset contains 9 different seafood types collected from a supermarket in Izmir, Turkey for a university-industry collaboration project at Izmir University of Economics, and this work was published in ASYU 2020. The dataset includes gilt head bream, red sea bream, sea bass, red mullet, horse mackerel, black sea sprat, striped red mullet, trout, shrimp image samples.

If you use this dataset in your work, please consider to cite:

@inproceedings{ulucan2020large, title={A Large-Scale Dataset for Fish Segmentation and Classification}, author={Ulucan, Oguzhan and Karakaya, Diclehan and Turkan, Mehmet}, booktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)}, pages={1--5}, year={2020}, organization={IEEE} }

• O.Ulucan, D.Karakaya, and M.Turkan.(2020) A large-scale dataset for fish segmentation and classification. In Conf. Innovations Intell. Syst. Appli. (ASYU)

Purpose of the work

This dataset was collected in order to carry out segmentation, feature extraction, and classification tasks and compare the common segmentation, feature extraction, and classification algorithms (Semantic Segmentation, Convolutional Neural Networks, Bag of Features). All of the experiment results prove the usability of our dataset for purposes mentioned above.

Data Gathering Equipment and Data Augmentation

Images were collected via 2 different cameras, Kodak Easyshare Z650 and Samsung ST60. Therefore, the resolution of the images are 2832 x 2128, 1024 x 768, respectively.

Before the segmentation, feature extraction, and classification process, the dataset was resized to 590 x 445 by preserving the aspect ratio. After resizing the images, all labels in the dataset were augmented (by flipping and rotating).

At the end of the augmentation process, the number of total images for each class became 2000; 1000 for the RGB fish images and 1000 for their pair-wise ground truth labels.

Description of the dataset

The dataset contains 9 different seafood types. For each class, there are 1000 augmented images and their pair-wise augmented ground truths. Each class can be found in the "Fish_Dataset" file with their ground truth labels. All images for each class are ordered from "00000.png" to "01000.png".

For example, if you want to access the ground truth images of the shrimp in the dataset, the order should be followed is "Fish->Shrimp->Shrimp GT".

The global benchtop scale market is experiencing robust growth, driven by increasing automation in various industries and the rising demand for precise weighing solutions. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This growth is fueled by several key factors, including the expanding pharmaceutical and packaging industries, which rely heavily on accurate weighing for quality control and efficient production processes. Advancements in sensor technology, leading to more accurate and durable scales, further contribute to market expansion. The diverse applications of benchtop scales across sectors like research and development, food processing, and industrial manufacturing also contribute to this positive market outlook. Different types of benchtop scales, such as compact scales, precision scales, analytical scales, and semi-micro balances, cater to the varying needs of these industries, creating further market segmentation and opportunities. However, certain restraints might impede the market's growth trajectory. These include the high initial investment costs associated with advanced benchtop scales, particularly in smaller businesses or developing economies. Furthermore, the market faces challenges from counterfeit and low-quality products that can compromise accuracy and reliability. Nevertheless, the ongoing technological advancements and increasing regulatory requirements for accurate measurement in various industries are expected to mitigate these restraints and propel market growth in the long term. The market is geographically diverse, with North America and Europe currently holding significant market shares, but the Asia-Pacific region is anticipated to demonstrate strong growth in the coming years due to rapid industrialization and economic expansion in countries like China and India. This comprehensive report delves into the multi-million dollar benchtop scale market, providing an in-depth analysis of its current state, future trajectory, and key players. With a projected market value exceeding $2 billion by 2028, this report is essential for businesses seeking to understand and capitalize on this thriving sector. Keywords: benchtop scale market, precision scale, analytical balance, compact scale, semi-micro balance, weighing scale, laboratory scale, pharmaceutical scale, industrial scale, scale manufacturers.

The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.

Discrimination based on skin color, referred to as colorism, has been documented as a con- siderable problem in social science research. Most of this research relies on Likert-type ratings of skin color. For example, the widely used “Massey Martin Scale” (MMS) requires coders to rate subjects on a scale from 1-10, based on the similarity between the subject’s skin tone and ten shades of skin color on a palette. Some scholars have raised questions about measurement error in Likert-type skin color scales. It’s been shown, for example, that Black and White coders apply the MMS differently. We hypothesize that the coding of a person’s skin color will vary depending on the race of persons previously coded. To test this hypothesis, we conducted an experiment using a convenience sample of Mturk workers as coders. We find that the MMS is vulnerable to spillover effects: a person’s skin is coded as “darker,” on average, if he is ob- served following a sequence of White persons than if he is observed following a sequence of Black persons. We also replicate previous work showing that Black and White coders use the scale differently. Finally, having coders cross-reference the palette at the time of coding, rather than recalling the palette from memory, fails to mitigate either race-of-coder or spillover effects. We provide suggestive evidence that use of a pairwise-comparisons approach may overcome some of the issues associated with Likert-type ratings of skin color.

Social structure can emerge from hierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual’s social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat’s grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks. Methods Empirical analyses We analyzed existing published data to estimate how often common vampire bats switched roosts, clusters, and partners. To estimate individual rates of roost-switching, we used 1,336 observations of 81 free-ranging bats of both sexes (38 males and 43 females) that were observed >25 times across 11 tree roosts along the Rio Corobici in Guanacaste, Costa Rica (1, 2). We also made grooming networks using 1,761 grooming interactions among 29 of these bats (3). To estimate individual rates of cluster switching and partner switching, we used 4,092 observations of clusters (defined as bats roosting in the same corner of a flight cage) and 22,836 observations of grooming from 31 vampire bats of both sexes (5 males and 26 females) at a captive colony in Panama (4). Individuals in both studies were identified visually using unique combinations of distinctive wing bands. To estimate roost-switching rates, we only used observations of the same bat or roost on consecutive days, because roost switching would be underestimated when a bat moved away and then returned to the same roost between observations (see supplement in associated paper for details). To calculate cluster-switching and partner-switching rates, we counted consecutive observations of the same bat where a switch occurred, then divided that count by the total time elapsed between those observations (see supplement for details). We only considered consecutive cluster-switching and partner-switching observations that occurred within a sampled hour. To calculate within-cluster partner-switching rates, we did not count partner switches and the associated time lapse that occurred due to partner switches between clusters. To create co-roosting and co-clustering networks, we defined edge weights in the co-roosting and co-clustering networks as the ‘simple ratio index’ of association (5–7). To create grooming networks, we defined edge weights as total minutes of grooming. To assess within-bat correlations between movement types, we used a linear model to test if cluster-switching rates predicted within-cluster partner-switching rates. To determine how well roost, cluster, and partner-switching rates predict the overdispersed counts of the number of bats groomed (outdegree centrality), we fit a quasi-Poisson generalized linear mixed-effects model with each of the three rates as single predictors, and bat as random intercept. We used nonparametric bootstrapping to create a 95% confidence interval (CI) around the standardized coefficient (b). Agent-based model We created a model using NetLogo 6.2.0 and used it to simulate movements of virtual vampire bats that lacked preferences for roosts, clusters, or partners. Each of 11 roosts contained 4 locations for potential clusters. We randomly assigned each virtual bat to a starting roost and cluster location. For each spatial scale, each bat had a switching propensity randomly sampled with replacement from empirical estimates of the probabilities of movement. Switching probabilities at every scale were conditional on the time since the last switch (see supplement). We initially ran all the simulations with populations of 200 virtual bats, the approximate number of bats encountered and banded by Wilkinson along the Rio Corobici between 1978 and 1983 (1, 2). To explore how our results would change with fewer bats and limited partner choice, we later ran the simulation with 100 virtual bats to explore how our results would change with fewer bats, leading to smaller group sizes and limited partner choice (2.3 bats per cluster, or an average of 1.3 partners per cluster). To isolate the effects of movement, we fixed the probability of grooming per minute for all virtual bats at 1.8% (the mean probability that a captive vampire bat groomed another bat during the sampled hours from empirical observations of captive vampire bats (4)). We included a synchronous 200-minute foraging period where bats left all roosts to forage outside the roosts. The simulations recorded observations of behaviors every minute for 15 days. When in a roost, virtual bats randomly decided every minute whether to groom a partner and whether they would switch partners based on an increasing probability related to the time since last switch at that scale. The decision was solely determined by the groomer initiating the exchange; the receiver did not decide whether to accept grooming. Each bat could only groom one partner at any particular minute, but multiple bats could groom the same bat during that minute. Virtual bats decided whether to switch clusters within their roost once every hour. Additionally, they decided whether to switch roosts once per day after returning from foraging. If a bat changed its partner as a result of cluster or roost switching, we did not count this event as partner switching. Similarly, if a bat changed clusters due to roost switching, we did not count this event as cluster switching. We took this approach to test the effects of a bat’s decisions at each scale rather than the effect of what it experiences. Although we measured within-roost cluster switching and within-cluster partner switching, for brevity, these are simply referred to as ‘cluster switching’ and ‘partner switching.’ Simulations using agent-based model We ran five types of simulation, each 100 times, and we ran those five simulation types across two different population sizes, once for 100 bats and again for 200 bats. Each of the five simulation types had switching propensities that were either fixed or individually-variable and either correlated or uncorrelated. In simulation 1, virtual bats were assigned a random propensity of roost, cluster, and partner switching; these propensities were uncorrelated within each individual bat because they were drawn independently from empirical distributions. The resulting standardized coefficients of the switching rates from this simulation measured how well each movement type predicted grooming outdegree when controlling for the other movement types. In simulations 2-4, one type of movement varied among bats while the two others were fixed (to the mean observed from the empirical data). In simulation 2, only partner-switching propensity varied across individuals. In simulation 3, only cluster-switching propensity varied across individuals. In simulation 4, only roost-switching propensity varied across individuals. Using simulations 2-4, we estimated the reference effects, defined as the median standardized coefficients of the switching rate when switching propensity was not variable between bats. The reference effects measure how well one movement type predicts grooming outdegree when it lacks individual variation in switching propensity. We estimated the isolated effects of individual variation in each switching propensity, defined as the difference between the standardized coefficient of the switching rate when only it was variable between bats and the reference effect. The isolated effect measures how well individual variation in only one movement propensity predicts grooming outdegree while accounting for the reference effect. Simulation 5 was similar to simulation 1 except that the three switching propensities were positively correlated, such that virtual bats that moved most frequently at one scale also moved most frequently at other scales (see supplement). By comparing the results of simulations 1 and 5, we could therefore assess the effect of switching propensities being correlated (simulation 5) or uncorrelated (simulation 1). In sum, our model allowed us to ‘switch on and off’ the existence of realistic individual variation in movement at each spatial scale to isolate the social consequences for the individuals, while eliminating the confounding effects of social and spatial preferences found in real vampire bats. By adding or removing individual variation in movement at each scale or across all scales, and by making these movements correlated or not across scales, these simulations allowed us to isolate the causal effects of individually variable movement on grooming network centrality. Note that a bat’s assigned probability of switching (its switching propensity) is not the same as the number of times it actually switched during the simulation (its switching rate). When switching propensity was fixed, all bats with the same time since last switch also had the same probability of switching at that time step. However, as the model was randomized, the realized number of switching events

The global market for digital luggage scales for travel, currently valued at $165 million in 2025, is projected to experience steady growth, driven by the increasing frequency of air travel and stricter airline baggage regulations. The Compound Annual Growth Rate (CAGR) of 3.7% over the forecast period (2025-2033) indicates a consistent expansion of this market. Key drivers include the convenience and accuracy offered by digital scales compared to traditional analog scales, eliminating the guesswork and potential for overweight baggage fees. Growing e-commerce sales channels are also contributing to market expansion, providing consumers with easy access to a wider range of products and brands. Segment-wise, online sales are expected to show faster growth than offline sales due to the ease of online purchasing and wider product selection. Within product types, hook-type scales maintain a larger market share due to their simplicity and affordability; however, strap-type scales are gaining popularity due to their versatility and suitability for various luggage types. Competitive landscape analysis shows a diverse range of established and emerging players, including Camry, Etekcity, and others, constantly innovating to enhance product features and improve user experience. This competition fosters innovation and price optimization, benefiting the consumer. The geographical distribution of the market reveals significant regional variations. North America and Europe are currently leading the market, driven by high disposable incomes and a strong preference for convenient travel solutions. However, the Asia-Pacific region is poised for significant growth, fueled by rising middle-class incomes and increased air travel in countries like China and India. Market restraints include the relatively low cost of traditional luggage scales and potential consumer reluctance to adopt new technology. However, ongoing product innovation, such as the integration of smart features and Bluetooth connectivity, is likely to overcome this inertia, further boosting market expansion. The continued focus on enhancing user experience through improved design, durability, and accuracy will further propel market growth throughout the forecast period.

The Tennessee Self Concept Scale (TSCS) is a 120 item true-false questionnaire designed to assess self esteem in adults. Developed by Fitts (1965), the scale assesses seven different dimensions of self esteem as well as a subtle measure of self criticism derived from test-taking response style. The seven areas of self esteem are: Identity, Behavior, Self-satisfaction, Physical self, Moral-ethical self, Personal self, Family self, and Social self. A Total Self Esteem Score can also be computed from all the scales. The primary advantage that the TSCS offers over other available self esteem measures is its assessment of a number of different dimensions of the self, including for the present study Social Self and Family Self. The Self criticism scale is also a nicely subtle index of the respondent's tendency to select negative statements to negate positive ones about the self. Research with the TSCS shows that low self esteem is correlated with depression and other types of psychological problems (Fitts, 1965). The 10 scales provided on the CAPS datafile are described below: SELFCRIT The Self Criticism Score: This scale is composed of 10 items. These are all mildly derogatory statements that most people admit as being true for them. Individuals who deny most of these statements are being defensive and making a deliberate effort to present a favorable picture of themselves. High scores generally indicate a normal, healthy openness and capacity for self-criticism, although scores above the 99th pe rcentile may indicate pathology. Low scores indicate defensiveness and suggest that total positive scores are probably artificially elevated by this defensiveness. TOTPOS The Total Positive Score: This is seen as the most important score from the TSC. It reflects overall level of self esteem. Persons with high scores like themselves, feel they have value and worth, have confidence in themselves, and act accordingly. People with low scores are doubtful about their own worth and feel anxious, depressed and unhappy.IDENTITY The Identity Score: These are 'what I am' items. Here the person is describing his/her basic identity.SELFSATI The S elf Satisfaction Score: On these items, the person describes how he/she feels about the self he/she perceives. This score reflects the level of self satisfaction or self acceptance. BEHAVIOR The Behavior Score: This score measures the individual's perception of his own behavior or the way he functions.PHYSSELF The Physical Self Score: This is the individual's view of his body, his state of health, his physical appearance, skills, and sexuality.MORLSELF The Moral Ethical Self Score: This describes the self from a moral-ethical frame of reference -- moral worth, relationship to God, feelings of being a 'good' or 'bad' person.PERSSELF The Personal S elf Score: This reflects the individual's sense of personal worth, feeling of adequacy as a person and evaluation of his personality apart from his body or relationships with others. FAMISELF The Family Self Score: This reflects the respondent's feelings of adequacy, worth, and value as a family member. It refers to the individual's perception of self in reference to his closest and most immediate circle of associates.SOCISELF The Social Self Score: This is another self as perceived in relation to others category, but pertains to others in a more general way. It reflects the person's sense of adequacy and worth in his social interaction with others in general.< /p>See codebook for additional information

The raw data of manuscript "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors"

The North America Truck Scale Market would witness market growth of 4.3% CAGR during the forecast period (2025-2032). The US market dominated the North America Truck Scale Market by Country in 2024, and would continue to be a dominant market till 2032; thereby, achieving a market value of $622 mill

Functional trait-based approaches are increasingly used for studying the processes underlying community assembly. The relative influence of different assembly rules might depend on the spatial scale of analysis, the environmental context and the type of functional traits considered. By using a functional trait-based approach, we aim to disentangle the relative role of environmental filtering and interspecific competition on the structure of European ant communities according to the spatial scale and the type of trait considered. We used a large database on ant species composition that encompasses 361 ant communities distributed across the five biogeographic regions of Europe; these communities were composed of 155 ant species, which were characterized by 6 functional traits. We then analysed the relationship between functional divergence and co-occurrence between species pairs across different spatial scales (European, biogeographic region and local) and considering different types of traits (ecological tolerance and niche traits). Three different patterns emerged: negative, positive and non-significant regression coefficients suggest that environmental filtering, competition and neutrality are at work, respectively. We found that environmental filtering is important for structuring European ant communities at large spatial scales, particularly at the scale of Europe and most biogeographic regions. Competition could play a certain role at intermediate spatial scales where temperatures are more favourable for ant productivity (i.e. the Mediterranean region), while neutrality might be especially relevant in spatially discontinuous regions (i.e. the Alpine region). We found that no ecological mechanism (environmental filtering or competition) prevails at the local scale. The type of trait is especially important when looking for different assembly rules, and multi-trait grouping works well for traits associated with environmental responses (tolerance traits), but not for traits related to resource exploitation (niche traits). The spatial scale of analysis, the environmental context and the chosen traits merit special attention in trait-based analyses of community assembly mechanisms.

Methods Data analyses

Different trait-based approaches have been used to distinguish the stochastic and deterministic (environmental vs. biotic filtering) processes that structure biotic communities. The approach we use can disentangle the role of environmental filtering and competitive exclusion by analysing the relationship between species pair co-occurrence and functional dissimilarity (2). From this analysis, three different patterns might emerge. First, if species with similar functional traits co-occur more often than expected by chance, the relationship between co-occurrence and functional dissimilarity of pairs of species will be significant and negative (i.e. environmental filtering process). Contrary to this, if species with divergent traits co-occur more often than expected at random, the relationship will be significant and positive (i.e. competitive exclusion process). Finally, non-significant relationships between co-occurrence and functional dissimilarity of species pairs are also possible (i.e. neutral theory processes). This would be the case where species co-occur independently of their functional similarity, or alternatively, if environmental filtering and competition exclusion are simultaneously at work with similar contributions. Here, we assume that two species co-occur when they occur spatially in the same community, although they might not share the same foraging time.

The co-occurrence index for each species pair was calculated within each species x site (European and regional scales) and species x bait (local scale) matrix. Data for the co-occurrence analyses consist in binary presence-absence matrices, where each row was a species, each column a site (or a bait), and the entries were presence (1) or absence (0) of a species in a site or a bait. Pairwise co-occurrence was calculated using the Jaccard index of similarity (JIab) for each pair of species in each matrix (47):

JIab=AB÷A+B+AB

where A and B are the number of sites where only species a and species b occur, respectively, and AB the number of sites where species a and b co-occur. The Jaccard similarity index takes values between 0 and 1, where 0 means that the two species are never found in the same site, and in our case, that co-occurrence is null; while 1 indicates that the two species are always together, and in our case, that the co-occurrence is total.

In order to measure functional dissimilarity between species pairs, we computed Gower’s dissimilarity between two species based on each functional trait separately, pooling traits according to whether they are ‘ecological tolerance’ or ‘ecological niche’ traits, and pooling all traits together. We used Gower’s dissimilarity, so that we would be able to deal with quantitative and qualitative traits (48). To compute it, we used a functional matrix where rows were species, columns were traits, and cell values were the trait values. Since Gower's dissimilarity depends on the number of species in the matrix, it was only calculated for each pair of species with data from the largest scale (Europe) where the number of species is highest. For each pair of species, nine functional dissimilarities were calculated: one with all functional traits together; one with only the ecological niche traits; one with the ecological tolerance traits; and one for each of the six traits separately. For these computations we used the ‘vegan’ (49) and ‘cluster’ (50) packages in R software v. 3.2.2 (51).

The relationship between the functional dissimilarity and the co-occurrence index between species pairs was tested by using linear models. Given the large number of zeros in the co-occurrence index and failure to meet the normal assumptions, we carried out the analyses in two steps. First, we transformed the co-occurrence index into a binary variable indicating whether or not there was occurrence of the pair of species in each matrix. We used a generalized linear model with a binomial distribution and a logit link function to perform the analysis (hereafter, binary co-occurrence analysis). In a second step, we applied a general linear model to make the model with the co-occurrence index where the pair of species occur at least once in the matrix (hereafter, co-occurrence strength analysis). In this case, the co-occurrence index was log-transformed to satisfy normality assumptions. We performed 18 analyses at the European scale (nine analyses for binary occurrence matrices and nine for co-occurrence strength matrices, these last nine comprising one analysis with all traits together, two analyses corresponding to each group of traits, and six analyses corresponding to each trait separately), 90 analyses at the biogeographic scale (forty-five for binary occurrence matrices and forty-five for occurrence strength matrices, of which nine analyses corresponded to each of the five biogeographic regions), and 333 analyses at the local scale (117 for binary occurrence matrices and 216 for co-occurrence strength matrices, comprising 37 analyses with all traits together, 37 for each group of traits and 222 for each singular trait). It is worth noting that binary co-occurrence analyses were only performed in locations where more than five pairs of species showed values of co-occurrence=0. Generalized and general linear models were conducted using the ‘stats’ package in R.