Previous Post Next Post Offshore Injury Blog 2015 October What Do Stevedores Do?
The role of a stevedore is very important in the maritime industry. They make sure ships can move quickly from port to port by providing efficient cargo loading and unloading services. These aren't your average dock workers or musclemen. Stevedores are typically highly skilled and trained operators of advanced mechanical handling equipment. Some stevedores may still handle cargo hands on at certain ports, manually transferring cargo from between ship and shore when the time calls for it. Overall, a stevedore's job has developed to involve much less human labor and more specialized machinery.
This isn't always what a stevedore was known for. Back before shipping methods and cargo loading / unloading had progressed to 2015 standards, a stevedore had very different responsibilities. In the earlier years of the shipping industry, there wasn't any advanced machinery around to help stevedores remove and carry cargo to shore. Due to this, cargo was packed in loads that were small enough for a man to carry on his shoulders. This also meant that cargo was in varied packing containers of all shapes and sizes, requiring stevedores to know the best way to stow all shapes and sizes of cargo items.
When the shipping industry changed to uniform containers, it also changed the job and responsibilities of a stevedore Instead of a majority of their day being physical labor, stevedores must now focus on strategically maneuvering advanced machinery so that they can transfer shipping containers to and from vessels at the port.
Stevedores, also known as longshoremen, may drive cargo on and off a ship, use a crane to pick up and move containers, secure cargo, tie down or untie the ship's ropes at port, attach devices to winches, and even direct other dockworkers on the job. With such diverse responsibilities, stevedores play a key role in keeping the shipping industry efficient.
While there is less manual labor required for a modern day stevedore, there is still more physical work involved in this occupation than your average desk job. As a stevedore, you will face a physically demanding work environment. Even simply operating heavy machinery could begin to wear on you. When moving hazardous or heavy materials, workers can face the risk of falling objects, crushing incidents, and much more. Due to the proximity to the water, slip and fall accidents can also be very common. No matter the type of accident, injuries can have a lasting impact on a worker.
If you or a loved one have been injured on the job at a longshoreman, Arnold & Itkin is here to stand by your side. We understand your situation and are here to help. Call us now as (888) 346-5024.


text:
Superbike is een soort motorfiets en een tak van de wegrace-sport.

 Superbike (motorfietsclassificatie)

Diverse raceklassen:
 AMA Superbike Championship
 Brits kampioenschap superbike
 Dutch SBK, Het Nederlands kampioenschap superbike
 Europees kampioenschap superbike
 Wereldkampioenschap superbike

redpajama_set_name: RedPajamaWikipedia


"I'm often here because it's a great fishing spot and it's a beautiful, almost hidden part of the world, so to see a helicopter landing was totally out of the ordinary!"
"I thought it was the army, because it would have taken a very skilled pilot to land there."
"I saw a man and a woman getting out, and they were collected again later. I heard since that Bear Grylls and Courteney Cox were around, so I've figured out it must have been them!"
The man behind dinglepeninsulafishandhunt.com has since uploaded footage of their departure to YouTube.
The filming was for Bear Grylls' TV show, Running Wild, and Cox stayed in Killarney with fiancé, Johnny McDaid of Snow Patrol, during her trip to the county.
It comes just months after Friends co-star Matt Le Blanc visited Kerry for Top Gear filming with then co-presenter Chris Evans.


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An independent Afghan news organisation met with young asylum seekers deported by the EU. Their footage shows what it's like for Afghan youth to be back in their home country. And it's certainly no safe place to be. Interviews with one young person in particular reveal the harrowing reality of life in war-torn Afghanistan.
There is no peace in Afghanistan. Every moment there is a suicide bombing here. There is a lot of poverty here and cold and one cannot save himself from them.
They [Europeans] don't understand our pain, how many problems we face in Afghanistan. We don't have a place [to live]. When I was going through the [migration] route, I saw a lot of dead bodies and faced certain death.
Hamdullah is now staying at Kabul's Deportee Safe House, set up by refugee rights activist Abdul Ghafoor.
The US has been intensifying the war in Afghanistan, with the Trump administration planning to deploy thousands more troops to the country. The war has been going on for 16 years now. Thousands of US forces and allied troops have been killed, with tens of thousands of Afghan civilians also having lost their lives.
Meanwhile, the EU continues to deport Afghan refugees. A new report from Amnesty International reveals that over 9,460 Afghans refugees were sent back to their home country from Europe between 2015 and 2016. This is in spite of the fact that the number of civilians killed and injured is reportedly reaching record levels.
It is, of course, difficult for each EU member state to decide how many refugees to accept. But when making these critical decisions, it is vital to have a full understanding of the situation at hand. And this includes recognising the personal accounts of people like Hamdullah. Because with Afghanistan afflicted by so much terror and turmoil, being deported as a refugee can feel like a death sentence.
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Threadlift (non-surgical face-lift) is minimally invasive and addresses signs of aging in the face, including droopiness of the skin of the brow, cheeks, jowls, and neck without incisions or scars. Threads are inserted painlessly under the skin to lift and contour the tissues, providing an immediate tightening. The procedure can be performed using local anesthesia, and most patients can return to normal activities the next day.
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Ecological theories often encompass multiple levels of biological organization, such as genes, individuals, populations, and communities. Despite substantial progress toward ecological theory spanning multiple levels, ecological data rarely are connected in this way. This is unfortunate because different types of ecological data often emerge from the same underlying processes and, therefore, are naturally connected among levels. Here, we describe an approach to integrate data collected at multiple levels (e.g., individuals, populations) in a single statistical analysis. The resulting integrated models make full use of existing data and might strengthen links between statistical ecology and ecological models and theories that span multiple levels of organization. Integrated models are increasingly feasible due to recent advances in computational statistics, which allow fast calculations of multiple likelihoods that depend on complex mechanistic models. We discuss recently developed integrated models and outline a simple application using data on freshwater fishes in south-eastern Australia. Available data on freshwater fishes include population survey data, mark-recapture data, and individual growth trajectories. We use these data to estimate age-specific survival and reproduction from size-structured data, accounting for imperfect detection of individuals. Given that such parameter estimates would be infeasible without an integrated model, we argue that integrated models will strengthen ecological theory by connecting theoretical and mathematical models directly to empirical data. Although integrated models remain conceptually and computationally challenging, integrating ecological data among levels is likely to be an important step toward unifying ecology among levels.
The search for unifying principles in ecology has spawned many ecological theories (Scheiner and Willig, 2011). These theories often span multiple levels of organization, connecting individuals to population, communities, and ecosystems (e.g., Brown et al., 2004; Falster et al., 2017). Although ecologists regularly translate theoretical models into mathematical frameworks (e.g., Hubbell, 2001; Brown et al., 2004; Kooijman, 2010), it has proven difficult to parameterize these complex, mathematical frameworks so that the resulting models are accurate, realistic, and applicable to real-world challenges (Marquet et al., 2014). We believe that attempts to parameterize complex, process-explicit models have been hampered by the isolation of data collected at different levels of organization. Although theoretical models regularly span multiple levels of organization, statistical models rarely connect data in this way.
Most models of ecological processes are fitted as "forward models," where model parameters are estimated from data measured at the level of the parameter (e.g., individual survival estimated from data on individuals through time) (Caswell, 2001; Tredennick et al., 2017). Although forward models usually support unique parameter estimates, it is challenging to collect the data required to parameterize forward models reliably while maintaining generality (Manning and Goldberg, 2010; Tredennick et al., 2017). Recently, several statistical advances have enabled "inverse models," where model parameters are estimated from data measured at higher levels of organization than the model parameters (e.g., Ghosh et al., 2012; González et al., 2016). For example, inverse modeling approaches have been used to estimate individual survival and fecundity from time series of population abundances rather than individual recapture histories (Ghosh et al., 2012). Inverse models make use of widely available data on higher levels of organization (e.g., population abundance surveys), which overcomes issues of data availability (Ghosh et al., 2012; Ovaskainen et al., 2016). However, inverse models often fail to identify unique parameter combinations because observed patterns at one level of organization are often consistent with multiple sets of parameters at lower levels of organization, leading to problems of non-identifiability (Peng et al., 2011; Ghosh et al., 2012).
In general, both forward and inverse models focus on a single type of data. For example, forward models of population dynamics typically are parameterized with data on survival and recruitment (e.g., life tables) (Fujiwara and Diaz-Lopez, 2017). By contrast, inverse models of population dynamics often are parameterized with data on populations (e.g., abundance time series) (Ghosh et al., 2012). Forward and inverse models face different challenges; a lack of data hampers forward models whereas a lack of specificity hampers inverse models. Importantly, these challenges are complementary, which suggests that a potential route is to combine forward and inverse modeling approaches to parameterize models from data collected at multiple levels of organization (Evans, 2012; Dietze, 2017). Connecting data collected at multiple levels—an "integrated" modeling approach—makes full use of available data and enables reliable parameter estimates without loss of generality (Besbeas et al., 2002; Schaub et al., 2007; Maunder and Punt, 2013).
Integrated models potentially overcome the practical challenge of estimating reliable, realistic parameters in complex mathematical models, such as dynamic energy budget models (Kooijman, 2010) or matrix population models (Caswell, 2001). This practical benefit has implications for fundamental ecology. For example, widespread estimates of demographic vital rates in natural conditions might give substantial new insights into spatial and temporal variation in life histories (e.g., McIntyre and Hutchings, 2003). Similarly, simultaneous analysis of data on individuals, populations, communities, and ecosystems might support realistic, data-driven models of biodiversity and ecosystem function (Isbell et al., 2018), and would bridge ecological analyses across distinct spatial scales, which is critical to the development of general ecological theory (Chave, 2013). In the following sections, we give a general introduction to integrated models, outline an illustrative application to real data, and discuss several open challenges.
The composite likelihood Lcomposite can be used with any likelihood-based methods of inference (e.g., maximum likelihood, Markov chain Monte Carlo) (Maunder and Punt, 2013).
The choice of process model is critical to an integrated model. The process model must connect data collected at multiple levels of organization (e.g., individuals and communities) and must be computationally tractable. Existing applications of integrated models have focused predominantly on population processes (e.g., species' occurrences, demographic models; Bird et al., 2014; Koons et al., 2017; Lahoz-Monfort et al., 2017; Zipkin et al., 2017), which facilitates computation while remaining relevant to multiple levels. However, any process model that connects multiple data types could be used (e.g., models of individual or ecosystem dynamics). A potentially useful focus is individual-based models, which could be connected to many data types at the expense of increased computational demands (Grimm and Railsback, 2005).
The primary challenge in the development of an integrated model is computational. Integrated models include potentially complex and dynamic process models, as well as multiple likelihoods that differ in complexity. Although a composite likelihood function is suited to many inference methods, high computational demands mean that fully Bayesian implementations are rare (but see Brooks et al., 2004; Zipkin et al., 2017). In addition, many implementations are hard-coded for particular case studies (Maunder and Punt, 2013), and generalizing these models typically requires knowledge of software for Bayesian hierarchical models (e.g., BUGS or AD Model Builder; Maunder and Punt, 2013; Koons et al., 2017). Recent advances in computational statistics and software are enabling more flexible implementations of integrated models, and we give an example of one such model in the following section.
Globally, freshwater river ecosystems are stressed by a combination of water extraction, changes to flow regimes, commercial and recreational fishing, the introduction of exotic species, and chemical and thermal pollution (Nilsson et al., 2005; Koehn et al., 2014). In many regions of the world, increased frequency of droughts and increases in consumptive water use have led to widespread water shortages, further exacerbating stresses on river ecosystems (Nilsson et al., 2005). In response to water shortages, many government agencies have invested heavily in programs to deliver environmental flows—releases of water to protect biological resources—often with an explicit focus on the viability of fish populations (Beesley et al., 2014; Koehn et al., 2014).
The Murray-Darling Basin in south-eastern Australia is highly valued environmentally, economically, and socially. Environmental values have been heavily affected by river regulation, particularly through the seasonal reversal of the timing of flows and reduced frequency and magnitude of flooding (Maheshwari et al., 1995). The Murray-Darling Basin supports over 40 native fish species, many of which have experienced substantial declines in abundance over the past century (Koehn, 2015). Here, we focus on Murray cod (Maccullochella peelii Mitchell, 1838), a large-bodied freshwater fish species listed as vulnerable under the Australian Commonwealth Environment Protection and Biodiversity Conservation Act, 1999. Murray cod are included in several state and federal conservation programs, which aim to identify management actions that will maintain viable populations, often with an explicit focus on environmental flows (Koehn, 2015). A key requirement of these programs is reliable models of population dynamics that can be used to predict population dynamics in different locations and under different management scenarios (Yen et al., 2013).
We used data collected over 20 years in six rivers in the Murray-Darling Basin: the Murray (1999–present), Ovens (2007–present), Loddon (2007–present), Goulburn (2008–present), Campaspe (2007–present), and Broken (2008–present). Available data included sizes of all individuals captured in a given survey, size-at-age data from otoliths collected from 55 individuals from 1999–present, and mark-recapture data from 1999–2017 at several locations in the Murray River. All three data sets had similar ranges of individual sizes and ages.
Our aim was to parameterize an age-structured model of population dynamics from data on size-abundance distributions, size-at-age data, and binary recapture histories (Figure 1). The underlying process model was a density-dependent Leslie matrix model with variation in vital rates among rivers (Caswell, 2001). We used five age classes and binned size-abundance data into eight size classes (round brackets exclude endpoints): [(0 , 200 g), (200 , 500 g), (500 , 1000 g), (1000 , 2000 g), (2000 , 5000 g), (5000 , 10,000 g), (10,000 , 20,000 g), and (20,000 , 60,000 g)]. These bins were chosen arbitrarily, with unequal bin widths to avoid the majority of individuals falling into one or a few size classes.
Figure 1. Overview of the integrated modeling approach used here. Different data types (in rectangles) are connected to one another through a density-dependent Leslie matrix model (oval). Several sub-models (diamonds) incorporate density dependence, a conversion from sizes to ages, and detectability. New data types (dashed rectangles) could be included in this model without altering the overall modeling approach. Similarly, data and sub-models could be added or removed as needed, and the main Leslie matrix model could be replaced with an alternative process model (e.g., an integral projection model or individual-based model).
We connected size-abundance data, size-at-age data, and recapture histories to the underlying Leslie matrix with three component likelihoods (Figure 1). First, we assumed size-class abundances were independently Poisson-distributed, conditional on a detection probability, a conversion from size classes to age classes, and the matrix population model (i.e., dependencies among age classes are captured in the Leslie matrix). Second, we used binary recapture histories to estimate the probability of detection, assuming a Cormack-Jolly-Seber model with time-varying survival probabilities (Lebreton et al., 1992). Third, we used size-at-age data to relate size-class abundances to age-class abundances, assuming that the distribution of individuals in a given size class among all age classes followed a multinomial distribution. We provide a detailed description of this model, including specification of prior distributions, in Appendix S1.
Constructing models in this way is fully modular, and allows any or all elements of a given model to be changed (Figure 1). For example, the matrix population model could be replaced with an integral projection model or individual-based model, data sets of the same or different type could be added to the analysis, and data sets could be removed to estimate parameters using a forward or inverse modeling approach (Figure 1). The only requirement is that the component likelihoods can be defined; all other aspects of the modeling process are unchanged.
We used the greta R package to generate fully Bayesian parameter estimates (Golding, 2018). We based parameter estimates on 40000 random-walk Metropolis-Hastings Monte Carlo iterations (four chains of 100000 iterations, retaining every tenth sample), following a 100000 iteration warm-up and burn-in period. We assessed model convergence through visual inspection of chains and used Bayesian r2 values to summarize model fit (Gabry and Goodrich, 2018). We did not use more-rigorous model validation (e.g., cross validation) because our goal was to illustrate the implementation of a simple integrated model rather than to present a full analysis of these data. Links to model code are in Data availability.
The integrated model fitted observed abundances moderately well in five of six rivers, with median Bayesian r2 values > 0.4 in all rivers except the Murray river (Figure S1, Table S1). Model fit was very poor in the Murray river (median Bayesian r2 = 0.00) (Figure S1, Table S1). Poor model fit in the Murray river, which had the highest observed abundances, might be due to shared priors on vital rates drawing estimates toward those of other rivers. Fitted abundances did not always track observed abundances closely (Figure S1), due primarily to the constraints imposed by the matrix population model. Although a process-explicit approach might not match observed data as closely as alternative methods (e.g., a purely statistical regression model), the use of an explicit process model potentially guards against overfitting and, therefore, might be expected to yield more-accurate long-term predictions (Cuddington et al., 2013). We acknowledge that more-detailed model validation would be required to test this claim.
Median survival probabilities ranged from 0.32–0.67 in one and two year age classes, with the lowest values in the Murray river and highest in the Loddon river (Figure 2). Median survival probabilities of three and four year age classes was generally between 0.5 and 0.8, with lower values in the Ovens river (0.16 and 0.41 for three and four year age classes, respectively) (Figure 2). Median survival probabilities of the five year old age class were mostly between 0.4 and 0.6, with a slightly lower value in the Loddon river (0.37) and a slightly higher value in the Goulburn river (0.66) (Figure 2). Estimates of survival probabilities in one, two, and four year age classes were similar to values reported in the literature, but estimates in three and five year age classes were generally lower than values in the literature (Figure 2). Median fecundity estimates, which encompass egg and larval survival, ranged from 1 to 5, with low values (<2) in the Campaspe, Goulburn, Ovens, and Loddon rivers, and relatively high values (>4) in the Broken and Murray rivers (Figure 2). These higher estimates of fecundity in the Broken and Murray rivers match closely with existing estimates in the literature (Figure 2). Density dependence was weak in the Murray, Ovens, Goulburn, and Broken rivers (k < 10−4) but strong in the Campaspe (k = 0.01) and Loddon rivers (k = 0.02), indicating relatively low carrying capacities in the Campaspe and Loddon systems (Figure S2, Table S2).
Figure 2. Posterior distributions of survival and fecundity from age-structured population models of Murray cod. Values are shown for each of six rivers in south-eastern Australia (black points and lines), along with independent estimates from Yen et al. (2013) (gray points and lines). Points are median values and bars span 95% credible intervals.
There are several possible extensions to this model. A relatively straightforward extension would consider temporal variation in vital rates, particularly in response to local or regional environmental conditions (Figure 1). Incorporating temporal variation in this way would enable projections of population abundances that are linked closely to contemporary and projected future environments (e.g., Yen et al., 2013). More challenging extensions might include size- or age-structured models of recapture histories or spatially explicit variation in vital rates. Although extensions such as these introduce computational challenges, it is conceptually straightforward to incorporate new data and models into the integrated modeling approach used here (Figure 1).
The example presented here illustrates the use of an integrated model to estimate the parameters of a complex, process-explicit model. This approach enabled simultaneous estimates of demographic vital rates, detection probabilities, and size-age associations, which would not be possible without an integrated modeling approach. However, this example also illustrates several challenges. These include the computational demands of increasingly complex model structures (e.g., individual-based models), the development of generalizable likelihoods that allow novel data types to be included in arbitrarily complex model structures, and the development of more-sophisticated composite likelihoods that incorporate dependencies among data types. Rapid development of integrated modeling techniques suggest that these challenges will soon be overcome, enabling many new applications of integrated models. Two particularly exciting applications are the use of integrated models to connect data among many levels, and the use of integrated models to support advances in ecological theory.
Most integrated models have focused on data collected at one or two levels. For example, integrated models of species distributions use data on populations (e.g., Lahoz-Monfort et al., 2017; Pacifici et al., 2017; Zipkin et al., 2017), while integrated models of demography use data on individuals and populations to estimate demographic vital rates (e.g., Brooks et al., 2004; Schaub et al., 2007; Bird et al., 2014; Koons et al., 2017). Few studies have moved beyond two levels to consider data spanning many levels (but see Péron and Koons, 2012). Given that individual behavior and movement, population dynamics, interspecific interactions, and ecosystem processes are all connected to the same underlying processes, it seems plausible that an integrated model could connect data among many levels.
Although integrated models are restricted by the computational demands of complex process models and likelihood functions, recent advances in computational software and hardware are rapidly overcoming these barriers. High-performance software libraries such as TensorFlow (Abadi et al., 2015) are enabling rapid computation of complex, dynamic models, supported by accessible libraries and packages in the R and Python computing environments (e.g., Golding, 2018). In addition, increasing availability and accessibility of high-performance computing environments is allowing non-specialists to undertake computationally demanding analyses. A major advance will be the development of flexible and modular software for integrated models that include different amounts and types of data.
Integrated models have been used largely as a practical solution to data shortages and non-identifiable models (e.g., Maunder and Punt, 2013; Zipkin et al., 2017). However, a broader benefit of integrated models is their capacity to link ecological data directly with ecological processes. Ecological theory often is quantified through a mathematical process model. Therefore, connecting data with ecological processes supports close ties between theory and data, with theory informing the process models that underpin integrated data analyses. Similarly, an inability to parameterize process models from available data often limits the development of ecological theory. In this case, using all available data to parameterize complex process models has the potential to hasten the development of ecological theory. Examples include the refinement of life-history theory through more accurate demographic models and new insight into biodiversity-ecosystem function theory through models that connect population, community, and ecosystem dynamics. Although it is not entirely clear whether ecologists should emphasize theory developed from first principles or theory derived from data (Marquet et al., 2014), it is likely that close ties between theory and data will improve inferences, predictions, and understanding of ecological processes (Marquet et al., 2014; Rillig et al., 2015; Dietze, 2017).
All model code and data are archived at https://zenodo.org/record/2551819 (DOI: 10.5281/zenodo.2551819). Data are provided specifically for purposes of reproducibility. Raw data suitable for alternative analyses are publicly accessible through the Victorian Biodiversity Atlas (https://vba.dse.vic.gov.au/vba/).
JY and PV developed the statistical methods presented here. ZT, JL, WK, AK, and KS provided the case study and all data. JY conducted all analyses and wrote the first draft of the manuscript. All authors contributed to subsequent revisions of the manuscript.
JY and PV were supported by the Australian Research Council Centre of Excellence for Environmental Decisions (CE11001000104). Data collections used in this work were funded by the Murray Darling Basin Authority, the Commonwealth Environmental Water Office Long-Term Intervention Monitoring project, and the Victorian Environmental Flow Monitoring and Assessment Program, Department of Environment, Land, Water and Planning. JY received additional funds from The University of Melbourne and The Robert Johanson and Anne Swann Fund.
We thank Diego Barneche, Julie Messier, Mary O'Conner, and Angelica Gonzalez for the invitation to write this perspective. Nick Golding provided advice on computational aspects of the work presented here. Many staff at the Arthur Rylah Institute contributed data used in this work, particularly Jason Lieschke, Joanne Kearns, John McKenzie, John Mahoney, Justin O'Mahony, Graeme Hackett, Andrew Pickworth, Scott Raymond, David Dawson, Matt Jones, Justin O'Connor, and Renae Ayres.
Abadi, M., Barhma, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2015). "TensorFlow: a system for large-scale machine learning," in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (Savannah, GA).
Caswell, H. (2001). Matrix Population Models: Construction, Analysis, and Interpretation, 2nd Edn. Sunderland, MA: Sinauer Associates.
Cuddington, K., Fortin, M.-J., Gerber, L. R., Hastings, A., Liebhold, A., O'Connor, M., et al. (2013). Process-based models are required to manage ecological systems in a changing world. Ecosphere 4:20.
Dietze, M. C. (2017). Ecological Forecasting. Princeton, MA: Princeton University Press.
Gabry, J., and Goodrich, B. (2018). rstantools: Tools for Developing R Packages Interfacing With 'Stan'. R Package version 1.5.1. Available online at: http://mc-stan.org/rstantools (accessed January 10, 2019).
Golding, N. (2018). greta. R package version 0.2.3. Available online at: https://greta-stats.org (accessed January 10, 2019).
Grimm, V., and Railsback, S. F. (2005). Individual-Based Modelling and Ecology. Princeton, MA: Princeton University Press.
Hubbell, S. P. (2001). The unified neutral theory of biodiversity and Biogeography. Princeton, MA: Princeton University Press.
Kooijman, S. A. L. M. (2010). Dynamic Energy Budget Theory For Metabolic Organisation, 3rd Edn. Cambridge, UK: Cambridge University Press.
Scheiner, S. M., and Willig, M. R. (eds.).(2011). The theory of ecology. The University of Chicago Press, Chicago, United States.
Copyright © 2019 Yen, Tonkin, Lyon, Koster, Kitchingman, Stamation and Vesk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


We're fast approaching the end of October, and while everyone else is getting in the Halloween spirit, it struck us that it also marks the three month countdown to the Self Assessment deadline. So here are our top tips on how to get sorted, file on time and avoid those fines.
This may sound obvious but HMRC can take up to 4 weeks to complete your registration process. Register now and avoid any additional stress later on in the year. Similarly, if you are already registered for self assessment, check that you know where your details are and that they work. If any problems arise, you still have time to sort them out.
If you work on a freelance basis, then you'll need to register for self assessment as a sole trader. As a sole trader, the income that you receive has not already had National Insurance and Income Tax deducted, so this is what you need to tell HMRC about. HMRC can then calculate how much tax (if any) you owe.
If you are an employee, but have additional income such as any properties that you may rent out, or income from another country then you will also need to file a tax return, as will any Directors of Limited Companies.
--Your income. So any invoices you have raised and business related expenses you incurred over the last tax year.
--If applicable, details of any dividends that you have paid yourself from your Limited Company or any income that you have received from a Partnership.
We hope that you have found this post helpful. If you get organised and plan ahead, self assessment can be straight forward and stress free, but if you feel that you would like professional help, we'd love to hear from you. Just give us a call or drop us a line. Details can be found on our contact page!


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Serving bowl Synthesis 250 ml in the colour black. This Synthesis bowl from Mepal is perfect for serving vegetables, fruit, chips, snacks or dips.
Serving bowl Synthesis 250 ml in the colour black. This Synthesis bowl from Mepal is perfect for serving vegetables, fruit, chips, snacks or dips. The unique shape of the Synthesis collection is a combination of round and square blended together, which has resulted in a modern, yet natural design. The Synthesis bowls come in duo-tones. This means that they are coloured on the outside and white on the inside, making the presentation of the food even more appealing. The 250 ml lavender Synthesis serving bowl is also available in 5 other sizes of 175 ml, 600 ml, 1.5 litres 2.5 litres and 4.0 litres.


text:
Q: Weird TypeError when calling simple matplotlib function I have been trying to execute some matplotlib.pyplot code and kept getting this random error:

TypeError: int() argument must be a string, a bytes-like object or a real number, not 'KeyboardModifier'

When I tried to simply put the tutorial code in to see if it was a problem with my code, I got the same error.
I literally tried to run:
import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()

and still got the same error?
for full error code things, this is the full error message:
Traceback (most recent call last):
  File "c:\Users\stefa\OneDrive - Catholic Education Western Australia\Desktop\Coding\python\main.py", line 3, in <module>
    plt.plot([1, 2, 3, 4])
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 2730, in plot
    return gca().plot(
           ^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 2227, in gca
    return gcf().gca()
           ^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 832, in gcf
    return figure()
           ^^^^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\_api\deprecation.py", line 454, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 773, in figure
    manager = new_figure_manager(
              ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 348, in new_figure_manager
    _warn_if_gui_out_of_main_thread()
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 338, in _warn_if_gui_out_of_main_thread
    if (_get_required_interactive_framework(_get_backend_mod()) and
                                            ^^^^^^^^^^^^^^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 207, in _get_backend_mod
    switch_backend(dict.__getitem__(rcParams, "backend"))
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 252, in switch_backend
    switch_backend(candidate)
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\pyplot.py", line 265, in switch_backend
    backend_mod = importlib.import_module(
                  ^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1206, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\backends\backend_qtagg.py", line 12, in <module>
    from .backend_qt import QtCore, QtGui, _BackendQT, FigureCanvasQT
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\backends\backend_qt.py", line 72, in <module>
    _MODIFIER_KEYS = [
                     ^
  File "C:\Users\stefa\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\backends\backend_qt.py", line 73, in <listcomp>
    (_to_int(getattr(_enum("QtCore.Qt.KeyboardModifier"), mod)),
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'KeyboardModifier'

Any extra help would be much appreciated!

redpajama_set_name: RedPajamaStackExchange


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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Using a function in R to scrape website, returning "subscript out of bounds" error I am trying to scrape player data from the Baseball Reference website, using a function to loop through multiple years (variable "year") for each player notated by "playerid."
library(plyr)
library(XML)

fetch_stats <- function(playerid, year) {
  url <- paste0("http://www.baseball-reference.com/players/gl.cgi?id=",playerid,"&t=b&year=",year)
  data <- readHTMLTable(url, stringsAsFactors = FALSE)
  data <- data[[3]]
  data$Year <- year
  data$PlayerId <- playerid
  data
}

This function works perfectly well when it is applied to a single year's worth of data, as seen here:
AdrianGonzales <- ldply("gonzaad01", fetch_stats, year= 2008, .progress="text")

However, as soon as I actually use the function to loop through the multiple years in a players career, it always spits out the following error:
AdrianGonzales <- ldply("gonzaad01", fetch_stats, year= 2009:2004, .progress="text")

Error in data[[3]] : subscript out of bounds
In addition: Warning message:
XML content does not seem to be XML: 'http://www.baseball-   reference.com/players/gl.cgi?id=gonzaad01&t=b&year=2009
http://www.baseball-reference.com/players/gl.cgi?id=gonzaad01&t=b&year=2008
http://www.baseball-reference.com/players/gl.cgi?id=gonzaad01&t=b&year=2007
http://www.baseball-reference.com/players/gl.cgi?id=gonzaad01&t=b&year=2006
http://www.baseball-reference.com/players/gl.cgi?id=gonzaad01&t=b&year=2005
http://www.baseball-reference.com/players/gl.cgi?id=gonzaad01&t=b&year=2004'

From what I have been able to find, the "subscript out of bounds" error happens when you exceed the limits of a defined dataset within R.  For this particular function, I may just be dumb, but I don't see how that would apply in this case- or why it would work for a single year, but not for several at a time.
I'm open to any and all suggestions.  Thanks ahead of time.

A: You could just use lapply as in the following way below.  I put in a minor fix to fetch_stats as it seems that the 6th column returned has no name.  You can do what you like with it, as it is just to show how you can use lapply instead.
library(plyr)
library(XML)

# Minor change made to get function working (naming column 6)
fetch_stats <- function(playerid, year) {
  url <- paste0("http://www.baseball-reference.com/players/gl.cgi?id=",playerid,"&t=b&year=",year)
  data <- readHTMLTable(url, stringsAsFactors = FALSE)
  data <- data[[3]]
  data$Year <- year
  data$PlayerId <- played
  ### Column six name is empty.
  names(data)[6] <- 'EMPTY'
  data
}
res   <- lapply(2009:2004, function(x) fetch_stats("gonzaad01", x))
resdf <- ldply(res)

This will create a list of 6 elements, one for each year, then convert the list to a data.frame
The way ldapply is applied in your code, it is not giving it one year at a time, it is giving the entire vector of years all at once.
EDIT
After looking a little closer, here is a solution using ldply
new_res <- ldply(.data = 2009:2004,
                 .fun = function(x) fetch_stats("gonzaad01", x),
                 .progress="text")

This gave me the same results as the other method above.


text:
Lady Vegas ist ein US-amerikanischer Spielfilm von Stephen Frears aus dem Jahr 2012 mit Bruce Willis, Rebecca Hall, Catherine Zeta-Jones und Vince Vaughn in den Hauptrollen. Der Film basiert auf einer wahren Begebenheit, die von Beth Raymer in dem Buch Lay the Favorite: A Memoir of Gambling festgehalten wurde.

Handlung
Die hübsche Beth versucht ihr Glück in Las Vegas und will dort als Kellnerin arbeiten. Sie trifft jedoch auf Holly, die ihr einen Job beim Buchmacher Dink verschafft. Sie platziert für ihn Wetten bei den Wettbüros der Casinos und setzt für ihn Wetten telefonisch bei Wettbüros in der Karibik.

Beth beginnt sich in Dink zu verlieben. Dinks Frau Tulip bekommt dies jedoch mit, worauf Dink Beth kündigt. Im Casino lernt Beth Jeremy kennen, die beiden verbringen die Nacht miteinander und Beth beschließt mit Jeremy nach New York zu gehen.

Bei Dink läuft das Wettgeschäft sehr schlecht. Als Beth gerade ihr Motelzimmer räumen will, taucht Dink auf und kann sie überreden wieder für ihn zu arbeiten. Die Geschäfte laufen bei Dink jedoch weiterhin schlecht. Da Dink zunehmend cholerisch wird, kündigt Beth und geht nach New York zu Jeremy. Statt sich einen legalen Job zu suchen beginnt sie für den Buchmacher Rosie in New York zu arbeiten. Sie sammelt für ihn die Wetteinsätze ein und verteilt die Wettgewinne an die Spieler. Darüber hinaus stellt sie den Kontakt zwischen Rosie und dem Spieler Dave her. Im Gegensatz zu Las Vegas sind derartige Wettbüros in New York jedoch illegal. Dink warnt Beth vor den illegalen Geschäften von Rosie.

Rosie eröffnet ein eigenes Wettbüro auf der Karibikinsel Curaçao, da Glücksspiel dort legal ist. Beth geht mit Rosie dorthin, um das Wettbüro zu leiten. Jeremy bleibt in New York und übernimmt dort für Beth die Wettabrechnungen. Das Wettbüro in Curaçao läuft jedoch nicht so, wie Beth es sich vorgestellt hat. Beth beschließt daher zurück nach New York zu gehen. Als Dave seine Spielschulden nicht bezahlen will, droht das illegale Wettgeschäft in New York aufzufliegen. Dink und Tulip beschließen nach New York zu fliegen und dort Beth und Jeremy zu helfen. Nach einiger Suche können sie Dave ausfindig machen und Beth setzt ihn unter Druck, um das Geld einzutreiben. Um seine Schulden zu begleichen, platziert Dave eine letzte Wette. Beth geht auf den Handel ein. Dave kann seine Wette schließlich gewinnen.

Kritik

Von den bei Rotten Tomatoes gesammelten Filmkritiken fallen 25 % positiv aus (Stand Dezember 2012).

Veröffentlichung
Der Film startete in Deutschland am 19. Juli 2012 in den Kinos. In den USA lief er ab dem 7. Dezember 2012.

Weblinks

  auf film-zeit.de

Einzelnachweise

Filmtitel 2012
US-amerikanischer Film
Stephen Frears


text:
Caltrans fast-tracks water projects
The California Department of Transportation announced Thursday that it has allocated emergency funding to continue to install smart sprinklers on state property in response to California's protracted drought.
The announcement came one day after Gov. Jerry Brown mandated a 25 percent cutback on water use for all non-agricultural uses.
Caltrans said it hopes to save water with the smart sprinklers that automatically adjust to weather conditions, soil moisture or broken pipes. Where installed, these smart sprinklers have cut water use by 50 percent, officials said.
"Caltrans districts have significantly curbed irrigation, modernized equipment and halted many landscaping projects across California," Caltrans Director Malcolm Dougherty said in a prepared statement. "These efforts have saved billions of gallons of water, but as this State of Emergency continues in California, we must work even harder — and keep leading the way on this critical issue."
Look along most of the state's major highways, and the landscaping along their sides falls under the care of Caltrans — the equivalent of 22,600 football fields, officials said.
Among the conservation efforts is $47 million in emergency funding to upgrade or repair 1,250 irrigation systems statewide, including smart sprinklers. Caltrans has also postponed 34 landscaping projects; invested $8 million in four major water recycling projects in San Luis Obispo, Shasta and San Diego; and canceled any turf or grass watering, including all state rest stops, unless recycled water is used.
Caltrans has shut off irrigation in areas of the state with the most severe drought conditions. Essential landscaping that is needed to prevent water pollution or erosion uses only inert ground covers, such as mulch or gravel, or drought-tolerant native or non-thirsty plants.
Last week, Caltrans partnered with the California Transportation Commission to authorize an additional $28 million in emergency funding to update more irrigation systems with smart sprinklers.
Included in that allocation was $350,000 for various spots in San Joaquin County on Interstate 5 and Highways 4 and 99 for upgrades and improvements to the irrigation systems.
Also last week, the California Transportation Commission approved new allotments for roadwork in San Joaquin, Calaveras and Amador counties.
In San Joaquin County, $1.5 million has been allocated for improvements along Highway 99 through Stockton. The money will go toward installing ramp metering systems, closed-circuit monitors and vehicle pullouts at six on-ramps between Highway 4 and Hammer Lane. The work is designed to improve mobility and safety.
In Calaveras County, more than $4.9 million has been allocated to repair pavement on 38 lane miles of Highway 4 from the Calaveras-Stanislaus county line to Highway 49 in Altaville. The work is designed to improve ride quality and extend the service life along this stretch of highway.
In Amador County, nearly $13.5 million has been allocated to repair pavement on 32.2 lane miles of Highway 88 from Peddley Hill to west of Tragedy Springs Road. The work is designed to improve ride quality and extend the service life along this stretch of highway.
Some other projects that had already received approval also received funding.
"Caltrans is embracing a 'fix it first strategy' by focusing on preventative maintenance to our existing transportation infrastructure," said Director Malcolm Dougherty. "We are striving to give California's taxpayers the most bang for their buck because every $1 spent on preventative pavement maintenance translates into a savings of $6 to $14 that would have been spent on expensive pavement repairs."
A project in San Joaquin County on Highway 4 from the Contra Costa County line to east of Bacon Island/Trapper Island Road will see nearly $5 million in improvements as Caltrans will rehabilitate nearly 17 lane miles of pavement by repairing localized areas of failures, pave rubberized asphalt on the roadway, construct rumble strips and replace roadway signs.
A project in San Joaquin County on Highway 99 from north of Marsh Street Pedestrian Overcrossing to south of the Hammer Lane Overcrossing will see $3.5 million in work to rehabilitate 4.6 miles of pavement by repairing localized failed areas and replacing asphalt pavement below overcrossings with concrete slabs. This will extend pavement service life and improve ride quality.
A project in Calaveras County on Highway 49 from the Stanislaus County line to Highway 49 will see nearly $5 million in improvements as Caltrans will rehabilitate 38 lane miles of pavement by placing asphalt concrete overlay to extend pavement service life and improve ride quality.
Two projects in Calaveras County will bring improvements to the Calaveras County Airport. Caltrans will spend $50,000 to upgrade the automated weather observing system and $20,000 to replace the rotating beacon.