The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset tabulates the Cut Bank population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Cut Bank across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Cut Bank was 3,017, a 0.43% decrease year-by-year from 2022. Previously, in 2022, Cut Bank population was 3,030, a decline of 0.69% compared to a population of 3,051 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cut Bank decreased by 73. In this period, the peak population was 3,161 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Cut Bank is shown in this column.
• Year on Year Change: This column displays the change in Cut Bank population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cut Bank Population by Year. You can refer the same here

The benchmark interest rate in Indonesia was last recorded at 5 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset Introduction The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset. Synchronization of the two modalities The PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps). Data storage DVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s. Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing). Data captures directory organization (radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z) The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures. dataset_dir///--/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad Identifiers

stage [train, test]. room: [conference_room, foyer, open_space]. subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test). gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right']. distance: 'xxx', '100', '150', '200', '250', '300', '350', '400', '450'. Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture. The test data captures contain both subjects that appear in the train data as well as previously unseen subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new unseen open space. Files List radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z This is the actual archive bundle with the data captures (recordings). rad_file_parser_2.py Parser for individual .rad files, which contain capture data. loader.py A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then request from it a training set, a validation set and a test set as torch.Datasets to work with.
aircraft_marshalling_signals_howto.ipynb Jupyter notebook for exemplary basic use of loader.py Contact For further information or questions try contacting first M. Sifalakis or F. Corradi.

Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).

The dataset consists of seven folders. Each folder represents one milling run. In each milling run the depth of cut was set to 3 mm. A folder contains a maximum of three json files. The number of files depends on the time needed for each run which is a function of milling tool diameter and feed rate. Files in each folder were numerated in sequence. For example, folder “run1” contains the files “run1_1” and “run1_2” with the last number indicating the order in which the files were generated. The frequency of recording datapoints was set to 500 Hz. During each milling run the milling tool moved along the longitudinal side and then was moved back alongside the workpiece. This way machining started always on the same side of the workpiece. Table 1 provides an overview of the milling runs. Run 1 to 4 were performed with a HSS tool with a diameter of 10 mm. The tool in use was an end mill (HSS-E-SPM HPC 10 mm) developed by Hoffmann Group. During the first three runs with this end mill no tool breakage occurred. However, in run 4 the tool broke. Runs 5 and 6 were performed by milling with an end mill of the same tool series (HSS-E-SPM HPC 8 mm) that just differs in tool diameter. In contrast to this run 7 was performed by using a solid carbid tool (Solid carbide roughing end mill HPC 8 mm). Cutting with SC tools provides much higher productivity with the downside being higher tool price. In our case the SC end mill performed cuts with a feed rate of 1150 mm/min compared to 191 mm/min achieved by a HSS end mill of the same diameter. Tool breakages were recorded on all runs with end mills of diameter 8 mm. Table 1. overview of the data folders folder name | number of json files | tool diameter | tool breakage | tool type run 1 2 10 mm No HSS run 2 2 10 mm No HSS run 3 2 10 mm No HSS run 4 2 10 mm Yes HSS run 5 2 8 mm Yes HSS run 6 3 8 mm Yes HSS run 7 1 8 mm Yes SC Each json file consists of a header and a payload. The header lists all parameters that were recorded such as position, motor torque and motor current of each of a maximum of five axes of a milling machine. However, the machine used in our experiments is a 3-axis machining center which leaves the payload of 2 possible additional axes to be empty. In the payload the sequential data for each parameter can be found. A list of recorded signals can be found in Table 2. Table 2. recorded signals during milling Signal index in payload | Signal name | Signal Address |Type 13-18 VelocityFeedForward VEL_FFW|1 double 19-24 Power POWER|1 string 25-30 CountourDeviation CONT_DEV|1 double 38-43 TorqueFeedForward TORQUE_FFW|1 double 44-49 Encoder1Position ENC1_POS|1 double 56-61 Load LOAD|1 double 68-73 Torque TORQUE|1 double 68-91 Current CURRENT|1 double 1 represents x-axis, 2 represents y-axis, 3 represents z-axis and 6 represents spindle-axis. Note that our milling center has 3 axis and therefore values for axes 4 and 5 are null.

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

Looking back 45 years or so, progress against poverty in India has been highly uneven over time and space. It took 20 years for the national poverty rate to fall below—and stay below—its value in the early 1950s. And trend rates of poverty reduction have differed appreciably between states. This research project aimed to understand what influence economy-wide and sectoral factors have played in the evolution of poverty measures for India since the 1950s, and to draw lessons for the future. This database contains detailed statistics on a wide range of topics in India. The data are presented at the state level and at the all-India level separately. The database uses published information to construct comprehensive series in six subject blocks. Period coverage is roughly from 1950 to 1994. The database contains 30 spreadsheets and 89 text files (ASCII) that are grouped into the six subject blocks. The formats and sizes of the 30 spreadsheets vary considerably. The list of variables included: . Expenditures (distribution) . National Accounts . Prices Wages . Population . Rainfall

The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This record contains raw data related to article "Early Recurrence after Upfront Surgery for Pancreatic Ductal Adenocarcinoma" Background: Survival after surgery for pancreatic ductal adenocarcinoma (PDAC) remains poor, due to early recurrence (ER) of the disease. A global definition of ER is lacking and different cut-off values (6, 8, and 12 months) have been adopted. The aims of this study were to define the optimal cut-off for the definition of ER and predictive factors for ER. Methods: Recurrence was recorded for all consecutive patients undergoing upfront surgery for PDAC at our institute between 2010 and 2017. Receiver operating characteristic (ROC) curves were utilized, to estimate the optimal cut-off for the definition of ER as a predictive factor for poor post-progression survival (PPS). To identify predictive factors of ER, univariable and multivariable logistic regression models were used. Results: Three hundred and fifty one cases were retrospectively evaluated. The recurrence rate was 76.9%. ER rates were 29.0%, 37.6%, and 47.6%, when adopting 6, 8, and 12 months as cut-offs, respectively. A significant difference in median PPS was only shown between ER and late recurrence using 12 months as cut-off (p = 0.005). In the multivariate analysis, a pre-operative value of CA 19-9 > 70.5 UI/L (OR 3.10 (1.41-6.81); p = 0.005) and the omission of adjuvant treatment (OR 0.18 (0.08-0.41); p < 0.001) were significant predictive factors of ER. Conclusions: A twelve-months cut-off should be adopted for the definition of ER. Almost 50% of upfront-resected patients presented ER, and it significantly affected the prognosis. A high preoperative value of CA 19-9 and the omission of adjuvant treatment were the only predictive factors for ER.

Background. Electronic Health Records (EHRs) embedded into hospital systems have been reported to have benefits including reductions in patient safety events.

The dataset includes the summarised performance of three key clinical indicators. These nursing indicators are based on clinical quality and patient safety. Data is provided prior and post migration to a fully integrated Electronic Health System (EHS) with monthly numerator and denominators. The data covers the implementation at three hospital sites in Birmingham. Further supporting data can be requested from PIONEER to analyse the impact on patient safety and key outcomes such a mortality, length of stay, escalation to intensive care and readmissions.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems) and this record includes the adoption of the EHR at two new hospital sites, a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: The dataset includes summarised data for performance of three clinical indicators, primarily:

• Late/Missed Antibiotics – this is the prescribed dose administered on time, late or missed by ward area each month.

• Late/Missed Non-Antibiotics – this is the prescribed medication (excluding antibiotics) which have been administered on time, administered late or missed by ward area each month.

• 12 Hour Observations – a measure to assess that a patient has a full set of physiological observations taken every 12 hours. These include temperature, blood pressure, respiratory rate and oxygen saturations. The data within this dataset are only the compliance count, but PIONEER has full results available on request.

Available supplementary data: Matched controls; synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Sediment samples were taken during May 2017 on research vessel (R/V) Meteor (M137) at four stations (74, 128, 243, and 752 m) along the 12º S depth transect traversing the Peruvian OMZ. Samples were retrieved using a TV-guided multicorer (MUC) equipped with seven core liners as described previously. Core liners were 60 cm long with an inner diameter of 10 cm. Filaments of giant sulfur oxidizing bacteria were observable by eye at the sediment surface (dense mat) and inside sediment at the 128 and 243 m stations. Filaments were detected mostly inside sediment at the 74 m station and were not observed at the 752 m station. Retrieved cores were immediately transferred to cold rooms (12ºC) for further processing. At each station, two small push cores (length 20 cm, inner diameter 2.6 cm) were subsampled from one MUC core. One of the replicate sub-cores (hereafter 'spiked core') was amended with unlabeled sulfide: 16 µl saturated sulfide solution (2.42 M stock solution: 250 g Na2S • 9 H2O in 420 ml ultrapure water) was injected into the sediment push core through pre-drilled holes placed at 1 cm depth increments following the principle of the whole-round core injection method. Based on an average sediment water content of ~80%, the added sulfide resulted in a final sulfide concentration of 10 mM in the porewater after its dilution into the sediment (5 cm3 sediment per injection point). The second sub-core remained untreated (hereafter 'unspiked core'). After an equilibration for 1-2 hrs at 12ºC in the dark, 10 μL of carrier-free 35S-sulfate radiotracer (dissolved in water, 1.86 MBq, specific activity 37 TBq mmol-1) was injected into both sub-cores at 1 cm depth increments according to the whole-core injection method. In the spiked core, radiotracer was injected through the same ports used for sulfide injection. Both spiked and unspiked cores were incubated with radiotracer for 6-8 hrs at 12ºC in the dark. After incubation, bacterial activity was stopped by slicing sub-cores at 1 cm increments and transferring sediment layers into 50 mL plastic centrifuge tubes filled with 20 mL zinc acetate (20 % w/w). Triplicate 'killed' controls were produced from additional sediment of the same MUC core and microbial activity was first terminated with zinc acetate before the addition of radiotracer to the centrifuge vial. All samples were frozen at -20ºC until analysis, when sulfate reduction rates were determined following the cold chromium distillation procedure.

Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features, and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific, limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM location, and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field. Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animal’s home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data. Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use.

Collision with electric power lines is a conservation problem for many bird species. Although the implementation of flight diverters is rapidly increasing, few well-designed studies supporting the effectiveness of this costly conservation measure have been published. We provide information on the largest worldwide marking experiment to date, including carcass searches at 35 (15 experimental, 20 control) power lines totalling 72.5 km, at both transmission (220 kV) and distribution (15 kV–45 kV) lines. We observed a small (9.6%) but significant decrease in the number of casualties after line marking compared to before line marking in experimental lines. This was not observed in control lines. We found no influence of either marker size (large vs. small spirals, sample of distribution lines only) or power line type (transmission vs. distribution, sample of large spirals only) on the collision rate when we analyzed all species together. However, great bustard mortality was slightly lower when lines were marked with large spirals and in transmission lines after marking. Our results confirm the overall effectiveness of wire marking as a way to reduce, but not eliminate, bird collisions with power lines. If raw field data are not corrected by carcass losses due to scavengers and missed observations, findings may be biased. Palabras clave: Bird, Mortality

Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L-1 selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 ± 17 mgSe L-1 day-1) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L-1 day-1, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856±26 mgSe-1day-1gbiomass-1, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H2Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (≥80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 µm. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.

Site selection for cervical stabilization surgery in horses with spinal ataxia frequently relies on measurements derived from radiographic myelography. A variety of measurement criteria exist and can provide conflicting results. The main objectives of this study were to assess the correlation between two commonly used myelographic measures, dorsal contrast column reduction (DCCR) and dural diameter reduction (DDR), and their association with previously selected operative sites in a population of horses operated at a tertiary clinic. Secondary objectives were to determine if articular process joint (APJ) atrophy occurred in a subset of operated horses with radiographic follow-up, and to describe complications of cervical stabilization surgery and long term outcomes. The study was primarily cross-sectional using previously recorded medical information and images from horses operated between 2008 and 2022: three masked raters assessed previously acquired pre-operative myelograms obtained in neutral, flexed and extended neck positions from horses that had subsequently undergone stabilization surgery consisting of cervical interbody fusion via a Kerf-cut cylinder technique at one or two sites. A veterinary radiologist evaluated changes in APJ in radiographs obtained in a subset of horses re-evaluated >18 months after surgery. DCCR was unremarkable at nearly all articulations in all horses, while DDR met reduction criteria at over 50% of articulations in flexed position. Neither DCCR nor DDR distinguished operated from non-operated sites at most intervertebral junctions, except at the C6-7 articulation in neutral and extended position. The two measures were also poorly correlated at most sites and in most positions. Surgical complications included a high incidence of laryngeal hemiplegia. Comparison of operated to non-operated sites within individuals radiographed years later showed consistent, mildly reduced APJ opacity at most operated sites without a consistent decrease in APJ height or area ratios. Our results suggest that DCCR and DDR measures did not reliably predict surgical site selection in this surgical cohort except at C6-7, and that the two measures yielded conflicting diagnostic classification at many sites and positions. Complication rates from stabilization surgery were high; and predictable reduction in APJ height or area after surgery was not demonstrated by radiography in this study.

Our Milky Way provides a unique test case for galaxy evolution models because of our privileged position within the Milky Way's disc. This position also complicates comparisons between the Milky Way and external galaxies, due to our inability to observe the Milky Way from an external point of view. Milky Way analogue galaxies offer us a chance to bridge this divide by providing the external perspective that we otherwise lack. However, overprecise definitions of 'analogue' yield little-to-no galaxies, so it is vital to understand which selection criteria produce the most meaningful analogue samples. To address this, we compare the properties of complementary samples of Milky Way analogues selected using different criteria. We find the Milky Way to be within 1{sigma} of its analogues in terms of star formation rate and bulge-to-total ratio in most cases, but we find larger offsets between the Milky Way and its analogues in terms of disc scale length; this suggests that scale length must be included in analogue selections in addition to other criteria if the most accurate analogues are to be selected. We also apply our methodology to the neighbouring Andromeda galaxy. We find analogues selected on the basis of strong morphological features to display much higher star formation rates than Andromeda, and we also find analogues selected on Andromeda's star formation rate to overpredict Andromeda's bulge extent. This suggests both structure and star formation rate should be considered when selecting the most stringent Andromeda analogues. Cone search capability for table J/MNRAS/498/4943/table3 (Table of Milky Way Analogues, with no redshift cut applied)