text:
\section*{Introduction}
Despite the weight of public procurement in governmental budgets \cite{OECDStat}, it is still one of the activities that is most vulnerable to corruption \cite{murray2014procurement, OECD2015}. In that context, corruption can have many forms \cite{angulo2018eu} and occur at any point in the procurement cycle---from the pre-tendering to the tendering and the past-award phases---making it difficult to detect and measure \cite{Mufutau2016Detection,Rustiarini2019why,Whiteman2019fraud}. In Brazil alone, corruption in procurement contracts can represent an additional $20\%$ to $30\%$ of the expected price, which represents losses of around 200 billion Reais annually \cite{zeferino2020corrupccao}. Likewise, in Europe, it is estimated that losses are of around 5B Euros annually \cite{hafner2016cost}. Naturally, these losses undermine the ability of governments and public authorities to push-forward essential investments in health, education, infrastructure, security, housing, and social services \cite{soreide2002corruption, Beittel2019Combating}. Unsurprisingly, there is a considerable effort to develop analytical solutions to understand and mitigate the effects of corruption in the public procurement process \cite{fazekas2018inovations}. Recently, the increasing availability of open data concerning public administration activities \cite{curado2020scaling} has renewed the scientific community's efforts on uncovering the hidden connections between participating agents and how their relationships can link to fraudulent activities \cite{Herrera2019network, kertesz2021complexity}.

One of the most challenging aspects of identifying corruption in the context of public procurement contracts is related to the lack of labeled data. Hence, it is often impossible to know which instances correspond to corruption \cite{wachs2019network}. In that sense, past works have approached this problem from an unsupervised learning perspective, meaning that they look to extract from the data more information about the relationships between the involved parties and, thus, flag groups of agents with patterns associated with a high risk of corruption. In that sense, a fundamental principle in public procurement is that of transparency in bidding \cite{adjei2019role, angulo2018eu, nowrousian2019combatting, spagnolo2012reputation}. In that sense, competition leads to greater efficiency for the public sector. As such, firms that developed the necessary relationships to achieve leverage to manipulating a tender process at a high risk of corrupting the procurement process \cite{hanak2018analysis}.

\begin{figure*}[!t]
	\centering
	\includegraphics[width=.95\textwidth]{Figure1.pdf}
	\caption{ Panel a), graphical representation of the process employed to infer the Firm-Firm co-bidding network. Panel b), comparison between the frequency of bidders per tender in the original data set (gray) and in the working data set (red) after filters have been applied. Panel c), comparison between the frequency of bids per firm in the original data set (gray) and in the working data set (red) after filters have been applied. In panel b) and c) dashed line represents the OLS regression lines, the domain of the line indicates the domain used for fitting the curve.
	\label{figure1}
	}
\end{figure*}

An open issue remains, can the communities identified from firms co-biding patterns allow us to highlight groups of firms that are more susceptible to collusion and market manipulation? The use of network analysis for the study of corruption is not new \cite{lauchs2011corrupt, chang2018understanding, grassi2019betweenness}. In the context of public procurement, past studies can be divided into two main groups: 1) works that explore bipartite relationships between public bodies and firms \cite{fazekas2016corruption, wachs2019social}; and 2) studies that explore firm-firm co-biding relationships in public tenders \cite{Toth2014toolkit, reeves2017bid, morselli2018net, wachs2020corruption}. Both approaches have their merits, and each is more suitable to identify different mechanics underlying the manipulation of the procurement process. For instance, bipartite relationships are suitable to identify fraud that stems from bribes and influence ties; while firm-firm relationships are more suited to identify cartels and collusion. Despite these, the use of network analysis to study the relationship between firms in procurement bids is a relatively new venture \cite{reeves2017bid}, and more evidence is required in order to have a clear picture of the universality of existing patterns and mechanics across cultural and socio-economic contexts.

Here, we use methods from network science and complexity sciences to map and characterize the co-bidding network \cite{wachs2019network, Piccolo2017design, Ramalho2020detection, reeves2017bid} between firms that participated in public tenders issued by the $184$ municipalities of the state of Ceará (Brazil). In that sense, we provide a characterization of the relationships between competing firms and identify the major communities of firms that often compete for tenders with similar scope. Moreover, we argue that some of such communities have characteristics that place them at a higher risk of market manipulation and irregular activities often associated with corruption.

\section*{\label{data}Data}
We use data from \textit{Tribunal de Contas do Estado Ceará} (Brazil) covering public tenders issued by the 184 municipalities of the State of Ceará between 2015 to 2019. Each observation informs about the bid of a firm to a tender and whether the bid was one of the winning bids. It also includes information about the municipality that issued the tender, and whether a firm won a contract. Hence, the data is naturally represented through a bipartite nature \cite{fierascu2017Networked}, which connects firms to tenders (see Figure~\ref{figure1}a). The data set contains $196,608$ observations that account for the bids of $45,502$ firms to $84,835$ tenders.

Information about the firms and tenders is anonymized, and bidding values are not available. Moreover, the data set does not contain information about which contracts/firms have been investigated in the past for irregularities.

\section*{\label{data}Network Inference}
Since we are interested in studying the relationships between firms we focus on the Firm-Firm projection. We estimate the projection from the co-bidding patterns of firms \cite{Piccolo2017design} using the Jaccard similarity coefficient \cite{veech2013probabilistic,mainali2017statistical,chung2019jaccard,wachs2019network}. Figures~\ref{figure1}a shows a graphical illustration of the structure of the data and depicts the steps conducted in order to infer the Firm-Firm network from the original Tender-Firm bipartite structure.

In order to infer the Firm-Firm co-bidding network, we start by discarding all firms that did not bid at least once during each year of analysis. By doing so, we are able to extract the core of active firms, while removing firms with sporadic activity. Figure~\ref{figure1}b and \ref{figure1}c compare the original ($P_\text{ALL}$) with the filtered data set ($P_\text{Sample}$). In particular, showing that filtering tends to remove excess participants from tenders, while it does not affect the distribution of the number of bids done by each firm. Likewise, we refer to firms present in the firm-firm co-biding network as Established firms. The final working data set includes $1,906$ firms, which account for $72,078$ bids to $39,523$ tenders.

\begin{figure*}[!t]
	\centering
	\includegraphics[width=0.95\textwidth]{Figure2.pdf}
	\caption{Panel a) shows the degree distribution, $D(k)$. The dashed black line represents the best exponential distribution fitted to the tail ($k>5$) of the empirical distribution. Panel b) shows the average clustering coefficient average per degree, $C(k)$. The dashed black line shows the best linear fit.
	Results have been estimated from the entire graph.
	\label{Figure2}
	}
\end{figure*}

Hence, next, we compute the centered Jaccard coefficient \cite{chung2019jaccard} is between each pair of firms, which can be computed as:
\begin{equation}
    J^c_{ij}=\frac{\sum_kb_{it}b_{jt}}{\sum_k(b_{it}+b_{jt}-b_{it}b_{jt})} - \frac{p_i p_j}{p_i+ p_j - p_i p_j},
    \label{eq1}
\end{equation}
where $b_{ik}$ is one if if firm $i$ made a bid to tender $t$, being zero otherwise; and $p_i$ is the fraction of tenders in which firm $i$ participated ($p_i = \sum_k b_{it}$. The second term in Equation~\ref{eq1} provides the expected number of observations when the bids from both firms are independent and identically distributed through a Bernoulli process \cite{chung2019jaccard}. Hence, the centered Jaccard coefficient allows us to distinguish between positive and negative associations between firms.

Finally, we estimate the significance of the computed $J^c_{ij}$ (i.e., to test the hypothesis that $J^c_{ij} > 0$). To that end, we bootstrap a null distribution ($\hat{J}_{ij}$) of centered Jaccard coefficient for each by generating an ensemble of $1000$ randomizations of the initial bipartite network. Data was randomized in order to ensure that the number of bids observed per firm and per year remained constant while keeping constant the number of firms bidding to each tender. Then, using statistical inference methods \cite{gotelli2000null}, we estimate the p-value associated with $J^c_{ij}$ by calculating the upper tail probability of obtaining a value equal or greater than $J^c_{ij}$ from the cumulative frequency of the null-distribution $\hat{J}_{ij}$. We discard links with $p$-value $>0.05$.

\begin{figure*}[!t]
	\centering
	\includegraphics[width=.95\textwidth]{Figure3.pdf}
	\caption{Graphical representation of the Firm-Firm network, which relates firms with similar bidding pattern. In order to build the network we consider only the most active firms, and edges with a significant Jaccard similarity index. Represented is the giant component with some relevant disconnected components. Nodes in the giant component are colored according to one of the eight major communities (out of $22$) identified using the Louvain algorithm \cite{blondel2008fast}. The presented partition of the network achieves a modularity of $0.61$.
	\label{Figure3}
	}
\end{figure*}

The resulting firm-firm co-bidding network contains $1,529$ nodes and $12,892$ edges. Relationships are treated as undirected and unweighted, identifying firms that have a similar pattern of bidding. The network exhibits an average degree of $16.86$, with a cluster coefficient of $0.52$ \cite{newman2020improved}, and $56$ connected components. Figure~\ref{Figure2} shows the Degree Distribution (panel \ref{Figure2}a) decays exponentially with the degree. Meaning that the underlying mechanics of co-bidding can be approximated with a random attachment process \cite{albert2002statistical}. However, the average clustering coefficient shows an inverse relationship with the degree (panel \ref{Figure2}b), suggesting the existence of some level of hierarchy in the structure of the network. It is noteworthy to mention that the largest connected component contains $1,141$ nodes, $10,630$ edges, and a clustering coefficient of $0.43$.

\section*{\label{results}Results and Discussion}
Figure~\ref{Figure3} presents the giant component of the firm-firm co-bidding network. In particular, we highlight the eight largest communities (nodes are colored accordingly). Using the \textit{Louvain} algorithm \cite{blondel2008fast} we identified $22$ communities with a modularity of $0.66$. We refer to these communities as $C_1$, $C_2$,...,$C_{22}$, whose index is ordered in descending order to the number of firms in the communities. The high modularity of the network is, however, unsurprisingly and can be explained by the fact that the network represents firms in different markets characterized by the different nature of contracts (e.g., works, services, etc) in different regions. As indicated in Figure~\ref{Figure3}, the largest communities divide the network into two major groups of firms that operate mostly in the northern (Red, Blue, and Green) and south (Purple and Yellow) regions of the state of Ceará, but also on contracts that deliver Food services (Blue and Purple) or construction works (Red and Yellow). Interestingly, the remaining communities highlighted identify firms that compete at state-wide level (Pink and light Blue) and one particular instance of a community (Violet) operating in a small region (Jaguaribe) and a single contract type (Food services). Hence, the network highlights competing markets and provides a characterization that is interpretable.

As discussed above, communities in Firm-Firm network structures can be used to identify market segments. In limiting scenarios, cases in which firms form "echo chambers" or highly dense communities, it also allows us to flag groups of firms that present a high risk of collusion and procurement manipulation. In other words, corruption. As such, next, we explore the use of network science as an approach to classify the communities of firms that might be of interest to investigate deeper by the authorities of interest.

\begin{figure*}[!t]
	\centering
	\includegraphics[width=0.95\textwidth]{Figure4.pdf}
	\caption{Characterization of the ten largest communities by the diversity of bids done by region and type. Panel a) shows the distribution of bids within each community by geographical region (left) and contract/tender type (right). For each community we compute the Simpson's diversity index ($\lambda_\text{reg}$ and $\lambda_\text{serv}$). Panel b) compares communities by their diversity of contracts in terms of regional span and type. Note that in panel a) we only show results for the ten largest communities, which are representative of the results. Communities not identified by a color code in Figure~\ref{Figure2} are shown in gray in panel b), in the particular community $C_{14}$ corresponds to the gray clique easily identifiable in the bottom left of the network in Figure~\ref{Figure3}.
	\label{Figure4}
	}
\end{figure*}

\subsection*{Activities Diversity}
We start by looking at the regional diversity on which firms performed their activities (\textit{e.g.}, bid on tenders), and also the diversity of the type of contracts that they bid to. While a firm with low diversity in both regional reach and contract-type can simply indicate a firm that is narrow in both scope and domain, the existence of clear groups (i.e., a community of firms) that share such indicator can highlight more a troublesome scenario. In particular, it can indicate the conditions for firms to coordinate and cooperate to control a specific market in a specific regional context, and should be investigated with further care. To that end, we start by estimating the Simpson's diversity index\footnote{In some fields the Simpson's index is also known as the Herfindahl index.} for each community, which can be computed as
\begin{equation}
    \lambda^{C_i}_\text{cat} = \sum_{t \in \gamma} (p_{C_i}^t)^2
\end{equation}
where $p_{C_i}^t$ corresponds to the fraction of bids done in a procurement contract of type $\gamma:$\{Consumables Health, Services, Construction, Events, Food, Fuel,...\} or regions type $\gamma:$\{Metropolinana, Norte, Sul, Noroeste,...\} by the firms in community $C_i$, $\forall i \in \{1,2,...,21,22\}$. The quantity $p_{C_i}^t$ is normalized per community, so that $\sum_{t} p_{C_i}^t = 1.0$. We estimate $\lambda^{C_i}_\text{cat}$ independently for each community ($C_i$), and for contracts according to the region that issue the tender and the tender contract type (e.g., services, food, tenancy, constraction, etc). Our choice of the Simpson's index over other alternatives (e.g., entropy) is due to its straightforward interpretation: the probability that two bids from a community are in the same category (e.g., region or contract type).

Figure~\ref{Figure4}a illustrates the empirical distributions ($p_{C_i}^t$) of procurement activity for the ten largest communities. We show the results for both the Regional distribution of activities and by  Contract Type. Blue colors denote a low relative frequency of bids, while red identifies a high frequency. These indicators allow us to infer the degree of specialization and agglomeration of a community. In particular, we find that Community 8 ($C_8$) activities are agglomerated in a single region (Jaguaribe) and firms specialize in one type of contract (Food). The same conclusion can also be inferred from the high levels of $\lambda^{C_8}_\text{cat}$, which means that Community 8 has low diversity of activity distribution. Figure~\ref{Figure4}b compares all the $22$ communities in terms of the two diversity indicators defined above. We find a clustering of communities in the bottom left quadrant--- a low level of agglomeration and specialization---that we associate with healthy markets composed of firms that, on average, have a diversified portfolio of activities and regional distribution. In contrast, in the top right quadrant, we find communities that rely on procurement contracts of a single type and agglomerated in a small number of regions.

The combination of these two diversity indicators, at the community level, provides a powerful feature to identify groups of firms that can dominate over a niche market or, in the worst case, develop undesirable leverage, as a group, in negotiating procurement contracts. Hence, lowering the desirable efficiency that public procurement aims at achieving in the tendering process. However, it is important to stress that these metrics are just indicative of potential problems, and thus the true nature of the activities of the firms in each community should be carefully investigated by the corresponding local authorities.

\subsection*{Bidding Coordination}
\begin{figure*}[!t]
	\centering
	\includegraphics[width=0.95\textwidth]{Figure5.pdf}
	\caption{Characterization by bidding activity. Panel a) shows the average number of single bids per community (i.e., the average of the number of times a firm in a community participated in a tender as the single bidder). We compare the values of each company with the average of the entire population of firms (horizontal red line). Panel b) shows the average number of bidders in tenders that firms within a community typically participates. We normalize the value obtained for each community by the number of firms in that community. The horizontal red line shows the threshold that marks the size of the community
	\label{Figure5}
	}
\end{figure*}
To further investigate the risk/susceptibility of market manipulation by firms, we next look at the propensity that each community has in participating in "single bidder" contracts. Another pattern often associated with corruption and loss of efficiency. Hence, what is the susceptibility of each community to such practice? To answer that question, we start by investigating the average number of times, per community, that a firm is the single bidder of a tender. Figure~\ref{Figure5}a shows the results for all $22$ communities in the largest component of the Firm-Firm network. Traditionally a higher level of single bidders would be the main indicator of market manipulation. However, at the community level, both very low and very high levels are indicative of unusual activity. In particular, if we set as a baseline number of single bids by a typical firm. The reason being that low levels indicate the risk of coordination (e.g., firms participating coherently in the same contracts) while high levels can sign the prevalence of less competitive markets. Overall, of the largest 10 communities, only Community 8 exhibits low levels of single bidders, a pattern extended to Communities 14 and 21 as well.. In contrast, we see that community 12 strongly deviates from the average with average value of single bidding that is roughly four times larger than the average.

In addition, it is important to look at the average number of bidders per tender in order to assess the potential existence of coherent behavior, that is, coordination between the firms in a community. To that end, for each community, we estimate the average number of bids per tender, which we normalize by the size of the community (i.e., the number of firms in a community). Interestingly, Figure~\ref{Figure5}b shows that in Community 8 firms participated tend to participate in tenders with a number of participating firms that matches almost exactly the size of the community. While, in some cases---Communities 14 and 19---the numbers are several times larger. Noteworthy to mention that this analysis is biased by the size of the communities, so the expectation would be to see a smoothly increasing relationship, with the largest community achieving the smallest value, and in the limiting case of a community with a single firm we would obtain the maximum. However, it is clear that in some cases--Communities 8, 14, and 19-- there are clear deviations.

\section*{\label{conclusions}Conclusions}
In this manuscript, we explore the potential of mining a large data set of public tenders collected from the activity of firms to compete for procurement contracts issued by the municipalities of the State of Ceará (Brazil). By matching firms with similar bidding patterns, we have inferred a firm-firm network comprising a total of $1,141$ nodes and $10,630$ edges.

We show that we are able to identify communities of firms with similar bidding patterns. The network exhibits a high modular structure partitioned in $22$ communities. These communities cluster firms that have a similar scope in procurement activity both in the nature of the contracts they celebrate and in the regional reach of their activities. Moreover, we look at two diversity indicators---regional diversity and procurement contract nature diversity---as a sign of the potential of certain communities to develop leverage over the procurement process. In other words, in affecting the expected efficiency of the market. Finally, we look at the sizes of the tenders, first by looking at the abundance of single bidders in communities, and secondly by looking at the average number of bidders in each tender. Overall we have identified on a particular community (Community 8) that combines several undesirable properties. Community 8 involves a group of firms that offers Food services in the region of Jaguarabe. They have an unusually low number of single bids; the average number of participating firms per tender matches the number of firms in the community, and they exhibit a high specialization and agglomeration in their activities.

Finally, it is important to highlight some shortcomings in our analysis and future working directions. The lack of pre-labeled data on past cases of corruption largely limited our ability to make any causal link between the network structure, its motifs, and the location of firms in the network with irregular procurement behavior. In that sense, our results are merely exploratory and show the potential of combining network science methods with descriptive statistics to highlight relevant groups of firms according to their activity pattern in a data-scarce environment. Future works should look at the evolution of the network, that is, if a larger temporal window is available, to capture the evolution and segregation of communities of interest but also of their parametric path in terms of the diversity of their activities.

\section*{Acknowledgments}
    MSL was financially supported by \textit{Tribunal de Contas do Estado do Ceará} through a Phd scholarship. BD, FLP, and FB acknowledge the financial support provided by FCT Portugal under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC). The authors are thankful to \textit{Tribunal de Contas do Estado do Ceará} for sharing the data for this study. The authors are thankful to Cristian Candia for the useful discussions and insights. The findings, interpretations, and conclusions expressed by the authors in this work do not necessarily reflect the views of the Tribunal de Contas of Ceará.

redpajama_set_name: RedPajamaArXiv


Liam Phelan, AIB Agri Advisor urges a planned and prudent approach to farm investment.
One of the last periods of significant on-farm investment occurred from 2006 to 2008, supported by the grant aid provided under the Farm Waste Management Scheme. It is estimated that gross on-farm investment was over €4.5 billion in that period, peaking at €2 billion in 2008. In addition to the investment undertaken from cash flow and farmers' own resources, lending to the sector reached record levels at this time.
The difficulty for many farmers who invested at that time was that this period of significant investment was quickly followed by a relatively short period of depressed commodity prices, poor weather and a somewhat lengthier period of high costs. This led to a dramatic reduction in farm profitability in 2009 (farm incomes fell by 41% between 2007 and 2009) and many farmers experienced a very difficult period with cash flow stress common on many farms.
There are some lessons which we can learn from this period, which may be useful for farmers considering future farm investment.
Many farmers undertook investment supported by strong average farm incomes in 2007 and the availability of grant assistance. Decisions to invest in the farm should never be made based only on a good year or on high performance expectations but rather on the longer term likely performance and actual capability of the farm. Our advice to customers is to take a multi-annual view and examine the performance of the farm over the previous three to five years taking account of variations in the actual outcome and profitability.
Farm investments often take longer to complete and cost more than originally planned. This was the case in 2007 and 2008 when the cost of steel and concrete increased considerably and added to on-farm development costs. Before undertaking any farm development, it is important to cost the investment fully and it is prudent to include a contingency cost of around 10-20% in all plans. To get a realistic idea of the potential cost involved, and to learn from the experiences of others, it is good advice to take the time to visit similar projects and get quotations from a number of reputable suppliers.
During periods of capital investment, good cash flow management is key. While many farm investments will contribute to increased profitability in the longer term, they can place immediate pressure on the farm current account. This is particularly the case where some of the investment is funded from cash flow. Where farm investment is carried out from cash flow, it can compromise the ability of the farm to withstand a cyclical or unexpected downturn in the sector and a period of low margin income. Many of the difficulties in 2009 were compounded by the fact that savings on many farms had been depleted to support farm development in the previous years. Before undertaking any farm investment, it is desirable to complete a cash flow forecast to understand the effect of the investment on the cash flow of the farm.
Ideally, a loan should be structured to match the useful life of the asset being financed. It is important not to put undue pressure on farm cash flow by seeking to repay a loan in an unrealistic timeframe which comes from trying to finance capital expenditure items over too short a period. The shorter the loan period, the higher will be the immediate repayment requirements.
Volatility has become a feature of the sector in recent years and it is important that farms can rebound quickly from periods of low returns. During periods of higher returns, farmers should seek to build a buffer which can be utilised during periods of cash flow pressure and low income returns. This buffer can take different forms, e.g. building a cash reserve, reducing creditors, improving soil fertility and / or forward purchasing of inputs. Those farmers who undertake significant investment tend to be more exposed to the effects of volatility as savings may be depleted and bank repayments tend to be higher. In 2009, a period of low market returns and higher costs coincided with higher bank repayments on many farms.
It is important that this investment is carried out in a planned and sustainable manner and that adequate time and resources are devoted to the planning process. The increased level of volatility witnessed on Irish farms in recent years has highlighted the importance of financial planning when undertaking farm investment. It is likely that this level of volatility will continue into the future and it is important that all farm businesses are able to take advantage of the good years, while weathering periods of reduced incomes and low market returns. It is important that we learn from the past and ensure that any future farm investment does not compromise the ability of the business to survive and prosper in the longer term.


text:
Namibians are dying and politicians aren't listening
Road carnage, Hepatitis E and Gender Based Violence (GBV) are three scourges battering the country and filling the cemeteries. As we go into the elections season, these three major national issues continue to cause untold misery. Lives are being lost and yet these issues are not the focus of party platforms and action programs.
We await the manifestos of each party contesting the upcoming elections. Will there be viable, well-funded programs to reduce death and injuries in road accidents, eradicate Hepatitis E and significantly reduce GBV?
The ruling party and most opposition parties have a 50/50 gender-balanced electoral list. Are these women in high office prepared to champion the major issues that are affecting other women? We have not seen this yet. The women sitting in parliament do just that…they sit. They do not agitate for women's concerns in law-making.
It is a national shame that women are dying like flies in domestic violence incidents and of avoidable disease outbreaks. Children are dying needlessly in bus accidents, being thrown from cars in road smash-ups and are tragically losing their parents and family members.
The Motor Vehicle Accident Fund (MVAF) statistics show that compared to last year's numbers, there has been a 113 percent increase in the number of children aged 10 to 14 who have died in road accidents this year. These are national tragedies. This should be an election year issue, yet it is not.
We have not seen the women who currently hold political power such as the Prime Minister, Deputy Prime Minister, or female ministers out front on these scourges that disproportionately affect women.
Politicians are alert to concerns about the drought, loss of cattle, failed crops and resulting food shortages in rural areas. There are programs in place. The Office of the Prime Minister is coordinating international donor efforts to provide support for those in need. The people are informed. There is institutional and political backing for drought relief.
Where is similar support for the three scourges killing off relatively large percentages of the Namibian people?
Namibian mind-sets, issues related to poverty and attitudes are the root causes of the three scourges. And yet, these are no excuses to become numb to the death plagues sweeping the population.
The dead on Namibia's roads need an advocate. Over a weekend this month, 23 people lost their lives in five separate road accidents. In just one weekend disaster, 14 people perished near Kalkfeld. A total of 101 people have died in 335 car crashes around Namibia in the past month. Where is the substantive plan to address the problem from civil society, churches, and the business community? Where are election speeches that list the changes that will take place to curb this kind of road carnage?
Experts agree that driver attitudes are causes of most road accidents. High speed and under-serviced vehicles are a part of the problem. Unskilled drivers make fatal errors. All involved pay the ultimate price when people overestimate their skills behind the wheel.
Every Monday morning the nation reads about who died on the roads over that weekend. Our leaders must come up with solutions before road accident deaths become normal. And then, we also read about deaths from Hep E.
The statistics about the alarming rise in Hepatitis E infections and deaths are a national crisis. We are in the midst of a major Hep E outbreak. Where are nationally televised speeches and radio announcements from health authorities on ways to avoid infections?
Media reports say that over the past year, the number of Hep E infections have increased by almost 80 percent. Deaths over the past 12 months rose by 77 percent, from 31 to 55 people.
The majority of Hep E deaths are women who were either pregnant or have recently had their babies. The Khomas region accounts for 63 percent of those impacted by this disease. Destitute citizens who are crammed in suburban ghettos without proper sanitation are victims of the outbreak. Those who are least prepared to manage the disease are mostly affected.
Politicians must have a Hep E management program in their political manifestos.
It seems that our leaders are numb to Hep E and shell shocked into insufficient action against road fatalities or GBV.
There are the armies of women being beaten and murdered by current or former spouses, boyfriends, and baby-daddies. Many continue to use the offensive term, "passion killings" to discuss brutal, cold hearted murder.
Too many people look away from GBV and consider it as 'a personal fight between a couple' or a 'social issue.' They say this as if listening to your neighbour beg a man not to kill her is no big deal. To those who stand around and watch the murder and abuse of women as if it is a reality TV show, we ask: where is your humanity?
We applaud the outstanding efforts of first lady Monica Geingos to spotlight GBV and advocate for women and girls. But, where is the Minister responsible for gender issues? We cannot remember the last public speech or media statement by the Gender ministry, about GBV and ways to defeat it.
There is a well-researched correlation between poverty and domestic abuse. Low-income abused women are especially more vulnerable to domestic violence. Poverty limits choices and resources. Men in various cultures are still considered as 'providers and bread-winners.' They are under pressure when they cannot do so. They take their frustrations out on those closest to them. As a result, black women are being beaten like unwanted dogs and slaughtered like cattle. Where is the political platform of any party with a program of action against GBV?
As the speeches and Star Rallies begin before the November elections, those in attendance must demand answers to these life and death issues. Road carnage, Hep E and GBV must be in the front room of the Namibian House, not shoved in a back closet.

redpajama_set_name: RedPajamaCommonCrawl


Water is one of our most valuable resources and something that many people take for granted. Water treatment occurs before any water reaches you, but for added protection and better taste, water softeners provide an important treatment step at your home or business. If you are interested in having a softener installed in your home, trust the reputation of Triple-T Plumbing, Heating & Air with your service.
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When water is in the ground, it picks up various soluble minerals. In some cases, this can lead to contamination that makes it unsafe to drink, but most of the time it collects magnesium and calcium and becomes "hard". While hard water won't harm you if you drink it, the minerals do affect the water's usefulness. Soap doesn't lather in hard water, and, in particularly bad cases, calcium deposits are left on the insides of pipes.
Water softeners remove magnesium and calcium through a process called ion exchange where excess calcium and magnesium ions are exchanged with sodium ions, leaving water softer and easier to use in the home. The major work in the softening process occurs in a water softener's mineral tank, where special negatively charged beads attract positively charged calcium and magnesium ions to remove them from the water. These ions are replaced by sodium and potassium ions which come from a brine solution in a separate tank. This brine solution requires special water softener salt to keep the softener functioning as normal.
As with most appliances in your home, your water softener is subject to potential problems and may need to be repaired. The most common problem in water softeners is blockages which can happen when salt and other minerals in the softener accumulate and form deposits in the filter screen and the brine line. These are easily fixed by homeowners with little help from professionals.
The resin beads in salt-based water softeners can sometimes become damaged and need to be replaced. These beads in the mineral tank can often last for as long as the softener itself, but they may need to be cleaned or replaced if you notice debris floating in softened water. This usually means that the beads are deteriorating.
Your water softener should be reliable as long as you receive the proper maintenance and is filled with the right softener salt. Water softeners seem like simple machines, but you will notice if your softener isn't working properly and you are stuck with hard water.
Call the experts at Triple-T for water softener service and repair today!We serve clients throughout Utah County in Provo, Orem, Salt Lake City, Lehi, and American Fork.


Your family's health is always the first thing on your mind. When it comes to their oral health and hygiene, you want the best for them. That means when it comes time to find a dentist for your children, you want the best pediatric dentistry has to offer.
What are your children going to need that a dentist for adults can't provide? After some research, you've learned the difference between a dentist and a pediatric dentist, and now armed with that knowledge, you're ready to go out into the world to find the best pediatric dentist for your family.
Here are the things to look for in a pediatric dentist.
A pediatric dentist earns their dentistry degree and then spends several more years focusing on pediatric dentistry. This includes learning all about early childhood education and how best to work with kids. They are comfortable with a screaming child or one that wants to ask a thousand questions.
Having an education in pediatric dentistry means your dentist was committed, not just to dentistry, but to children as well.
As soon as you walk in a pediatric dentist's office, you know you're in a place for children. There are bright colours on the wall, fun pictures, and a box of toys in the waiting room. In the big chair, there may be pictures on the ceiling for your child to look at while the dentist works on their teeth.
All this goes a long way to putting children at ease and ready for their visit to the dentist.
It's a small difference but it makes a big impact. Adults have larger mouths than kids. A dentist with equipment sized for their smaller mouths means you have a medical professional who understands the uncomfortable nature of going to the dentist (and how kids are often afraid).
Every little step towards making their experience less uncomfortable and less frightening is a positive one.
You will get a sense that this place is for your kids when the employees are friendly and introduce themselves to your children as well as you. By greeting you first, then your kids, it shows any nervous children that their parent is okay with this situation and that they are important, too.
By asking questions about their likes and hobbies, the frontline staff can feed this information to the dentist who can start by asking questions about their hobbies to create a quick rapport to build on.
An important part of pediatric dentistry is teaching children about each instrument, treatment, and process they're about to go through. A dentist that focuses on this educational piece will put children at ease and make a trip to the dentist not only less threatening, but interesting, too!
This will also become important when it comes to encouraging children to take on good oral hygiene habits. It's not enough just to tell kids to do something; teaching them why they should do it and making it fun will make them more likely to embrace it.
As good as your kids are, knowing that a prize awaits them at the end of a good check-up is encouragement to cooperate. A pediatric dentist's office that offers a prize at the end of an appointment is an office that knows how to work with kids and make the experience more pleasant.
If you want to ensure your family has a competent, considerate, and exceptional dentist, consider these elements when seeking the right dental offer for your family. There are many dentists out there, but some are exceptional and experienced at dealing with kids and will provide your family with the best service.


Whether you're an entertainer, musician, DJ, venue owner, event organiser or simply like to throw a party, www.ASTOUNDED.com is your ultimate source for everything required to make your special event happen.
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Products are only half of the picture, and as well as having what you need, when you need it, we want you to be delighted, not simply satisfied. This is why we offer fast, reliable delivery and a 14-day money back guarantee on all products, as well as free technical support with a personal touch. See below some reviews from our customers!
We suppy entertainment and events equipment to customers across the UK, Ireland and Europe, and strive to offer everything you may need for a fully integrated special event. Whether you need disco lights, a PA System, fireworks, or even just some spare cables, we strive to ensure you can get everything you need from us without having to trawl through countless suppliers.
We scour the entertainment equipment market to find the best products from up-and-coming manufacturers who are as passionate about value as we are. Our approach means we can offer incredible sound and light equipment for a fraction of the cost of bigger brand names – providing you with the best value products of any UK supplier.
We are a new company but have years of experience in the industry, founded by a small but dedicated group of experts with a passion for entertainment and an understanding of your needs, having previously worked for a leading retailer.
Our acclaimed and user-friendly design services, The Audio Wizard, Lighting Wizard & Firework Wizard, provide a convenient way to generate detailed designs, recommendations and quotations for any project – large or small.
All orders of £40 or more are delivered free to UK mainland addresses. The price you see is the price you pay.


On this week's episode of Taste of Taylor, Taylor gets a chance to interview all three founding members of Betches: Aleen Kuperman, Jordana Abraham and Sami Fishbein. Taylor "Howard Stern's" them, which is betches speak for getting them to spill sh*t. They also dish on their new book "When's Happy Hour?"
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Jenna Reid is looking for top marks today. She wears the perfect outfit for her driving test and you are her instructor! You do your best to keep Jenna on track but her flirting is in full throttle! You come to a complete stop and Jenna checks her blind spot before cranking on your driveshaft! Take this show on the road, literally! Jenna pleasures you while she continues her driving test! Things really gear up as you brake for more roadside fun. Forget ten and two, keep it at six and nine!
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Never have I ever - data edition!
Let's play a game. Never have I ever: the data edition.
You know how it works. We give you a scenario. If it's never happened to you, award yourself one point.
Ordered the wrong quantity of a product because of a data error. Such as the local pub who managed to create gridlock on a country road. A glitch in their data management caused them to order a truckload of boxes of frozen peas – not the few bags they actually needed. Or the retailer who ordered five times the amount of gammon needed for Christmas season (later known – not affectionately – as Gammongate to those involved).
Sent a customer the wrong product because of lazy data labelling. Like the supplier who duplicated product information online without thoroughly reviewing the details. This resulted in a convoy of 24 industrial printers winging its way to their customer - not the 24 desktop printers the buyer thought they had ordered.
Dented profits through poor data governance. A restaurant chain was using one type of chicken in their recipe data, and another in their ordering data. The discrepancy was costing them approximately £300k a year – yet it had gone unnoticed.
Struggling to score a point on this one? We're not surprised. Data dilemmas aren't always hinged around big disasters. Sometimes, it's small inconsistencies that are having a sizeable impact, but go unchecked due to a lack of data governance.
Not every business has a Gammongate, but every business has a chicken.
If you don't know, it's time to find out. At Comma, we put data governance at the heart of our MDM strategies. We work with MDM and PIM vendors to ensure that their clients get the technology that's right for them, while creating an internal framework that embraces data.


Blink. Rub your eyes. You didn't read wrong. You didn't stare at yesterday's eclipse through defective glasses. It's true.
Portent's in the process of being acquired.
Like an avowed bachelor, I swore I Would Never Sell. Then along came the perfect partner.
Our soon-to-be parent company is called Clearlink. They're a performance marketing company. Which is like calling a Tesla a nice little car.
Clearlink has built the ultimate performance-based marketing engine: They have millions of pages of great content, integrate that content with landing pages and PPC, and then have their own call centers to handle incoming calls without the typical marketing and sales wobbly-baton-hand-off.
In short, they get to own and optimize the whole funnel.
They represent brands like AT&T, State Farm, and Progressive.
Some of Clearlink's big clients want agency services. When the deal's done, we're going to help them provide those services. Which is why this acquisition makes sense on both sides.
Put it all together, and you can see why I jumped at this opportunity. This isn't your typical acquisition.
Excited, nervous, thrilled, and philosophical all at once.
Philosophical because, after 22 years, this opens a new chapter for my team and me.
Thrilled to move the team into that new chapter.
Nervous about a very new direction for myself and my team.
But more than anything, I'm excited about what we're going to do together.
And, to repeat: I'm not going anywhere. My hope is to be our version of Bertram Cooper someday, hanging out in my office, offering sage bits of advice and, if I'm lucky, kicking the bucket at 100 years old while watching humanity land on Europa. You'll have to watch Mad Men to get the reference.
PS: We'll be slowly morphing Portent's brand, but not for a few months. You can continue to spam me at my address.
Very cool to hear this, Ian! Congratulations!
Thrilled for you and the team Ian! And plenty jealous too — I've long been trying to move from the "aspiring entrepreneur" column to the "successful entrepreneur" column (and I know it's way harder than it looks).
Hope it's a wonderful partnership — congrats to Clearlink, too. They've chosen very wisely.
Thanks – I am wildly excited. And I am rarely wildly excited.
I am very happy for you and your team, Ian. You are an inspiration to us all, and always have been.
I'm happy for you and your team, Ian! I know you've worked hard to get where you are, and it's great to see you reaping more benefits than just an 80 hr work-week! Clearlink won the lottery!
Thanks Doc. Great stuff awaits, I think. This is a pretty great setup.
Wow, wow, wow… Very happy for you, bro. They are lucky to have you. I'm jealous you'll get to hang out in SLC more.
Massive congrats, Ian! Well-deserved & even more congratulations to Clearlink for getting such a terrific company.
Woot woot! Congrats on such well-earned recognition and success.
Way to go, Ian! Huge congratulations!
Congrats! Along with Distilled I've been following Portent closely because of so many great posts you've shared. Fingers crossed for you all!
Wow, awesome news Ian. Always had/have a great deal of respect for you on the speaking circuit and as an Entrepreneur. Kudos for landing what seems like a great deal for you and your team. All the best!!
Wonderful news! So happy for you!
Congratulations on the acquisition for you and the Portent team! Sounds like this may free you up a bit to follow some of your other passions around speaking and mentoring others. Give it a bit of time!
Congrats Ian, very exciting news!!!
Congratulations, Ian, and everyone at Portent!
Congrats. New client of Portent so a little nervous but happy for the team.
Don't be nervous! An important part of this acquisition is that we keep doing what we're doing. My team will take very good care of you.
Congratulations Ian and team, look forward to celebrating with you at Retail Global next month.
Nothing like an being acquired to get the juices going. I hope Clearlink appreciates what Portent brings to the table.
Ian, we couldn't be happier for you. Your post concisely and respectfully covers just about every question anyone might consider when thinking about your exciting news. You are wonderful with words.
Congrats again Ian – I think one key indicator that makes this sound like a good acquisition is the fact that they want you to do more blogging and writing. That is clearly your talent (or one of them) and definitely the way I'd want to spend a lot of my time as well! Look forward to your posts!
Congratulations on your news Ian! Look forward to hearing more about your new chapter.
We knew you when – and are not one bit surprised at you continued success! When the book is written on your amazing life, Ian, just don't forget those of us who just might have a story or two!
Congrats! Hope to get more blogging from you, as you're probably the funniest writer in digital marketing, for sure.
Congrats to the whole Portent team from your (even more) northern friends! ??
Congratulations Ian! I know the stress that comes with ownership so hopefully you can relax a bit 🙂 Sounds like a win win. Best of luck with the completion of the acquisition.
So great for you and the company! What an achievement and a promising partnership. Wins wins are rare these days and it seems like you hit the nail on the head. I always enjoy your voice in this industry and respect everything you've accomplished. Looking forward to more amazing content from you in the future!
Welcome to Clearlink! It's been fun to kind of get to know you through Confluence!


The mother of a 14-year-old Shrewsbury boy killed when he was struck by a train paid tribute to her "unique" son, as his inquest opened.
Jeff David Antwis died on January 30 after he was hit by a train at Shrewsbury's Harlescott Level Crossing.
Jeff was taken to hospital following after the incident at around 5.33am, but later died in hospital.
Speaking at the inquest, his mother Lorraine Antwis said Jeff, a schoolboy at Thomas Adams School in Wem, suffered from high functioning Aspergers and had been struggling with "low self esteem" for a number of months.
She described her son as "thoughtful and intelligent" and said he was a "loving caring child".
She said concerns about Jeff had been raised back in October, and he had admitted attempting to take his own life several times.
Ms Antwis said she had been in contact with the Shropshire Community Health NHS Trust's Child and Adolescent Mental Health Services (CAMHS) to discuss her concerns and possible mental health treatment for Jeff, and that he had received two initial assessment appointments, with the second on January 25.
She told the hearing, that at that assessment they had been given a date for an appointment with a doctor on March 17.
She said: "He was tormented, he'd had enough and he was told he had to wait another two months."
The hearing also heard evidence from Dr Eleanor Adams, a close friend of the family, Di Cooper, a teaching assistant at Jeff's school who had close contact with him from the time he began secondary school.
The hearing was also addressed by Dr B Praveen, a GP who had referred Jeff for urgent assessment by CAHMS.
The inquest is expected to last three days at the Shirehall in Shrewsbury.


These are lyrics by Jewel that we think are kind of nonsensical.
Enough stimulants? I didn't know that nicotine and caffeine were supposed to make you 'brilliant'.
But they're not yours, they are my own.
Well, duh! If they're yours, of course they're not mine. Thank you, captain obvious!
Jewel's, "Love Me, Just Leave Me Alone"
What is a phone machine and how do you put it on hold? Some phones themselves have hold buttons and you can put a call on hold after answering it if switching phones or having someone else pick it up, though that is not what she is saying to do in this song.
Jewel's, "Who Will Save Your Soul"
Um, what flower is she talking about? Is that what you see when you die, like 'first you see the ring, then you die'? Instead, first you see the flower, then you die? I am really confused!


They say, If wall surfaces can speak ??, but actually they can! They could tell tales with the art pieces with which they are embellished. Enhancing wall surfaces with wall surface art items of different forms is common for all nations and also cultures. Sophisticated or shabby, natural or hodge-podge? every person has some kind of art as a house decor thing on their wall surfaces.
Pictures are an old fave amongst house decor items. Personalized mounting or professionally picture tries look graceful in any kind of official living area, while candid photos of the family being silly could style the stairway instance or living room. You could bring in dimension to your house decor with items like shadow boxes and also 3D structures where together with the picture, you could likewise display youth memorabilia like your boys first footwear or his baseball bat from little league.
This is the least pricey means to bring in a house decor thing on the wall surface. You could acquire poster prints rolled up at chain store or currently matted and also framed. From whimsical to down ideal funny, religious to charming, you could acquire any kind of kind of poster print to suit your decor.
These are usually more pricey house decor items, specifically if you buy them from an art gallery or event. Some could regulate hundreds of bucks in asking prices as well as though that could look like a pretty penny, that thing could change your house decor significantly. Other than that it is a wonderful cash financial investment, and also later on, it can be sold or willed and also passed through generations.
Wire art, beads and also switches, needle point, patchworks and also rugs are all coming to be wall surface adornments and also house decor items of some sort or the other. Some say it is because of a absence of area in tiny city dwellings, other say it is due to the fact that people wan to re-use the very same things but see them in a various context. Whatever the reason could be, viewing ceramic tiles or woollen on the wall surface could be elegant for some and also simply ordinary goofy for others.
The bottom line is? allow your imagination go wild and also decorate the walls in your house with decor items that have their very own tales to tell. Your wall surfaces don?t have to be dumbfounded anymore? with appropriate house decor items you could make them speak the language you want!


text:
AIMC History
AIMC Congresses
16th Congress – 2018 Paray-Le-Monial France
15th Congress – 2016 Spilimbergo, Italy
14th Congress – 2014 Vienna, Austria
13th Congress – 2012 Athienou, Cyprus
12th Congress – 2010 Athens, Greece
11th Congress – 2008 Gaziantep, Turkey
10th Congress – 2006 Skopje, Republic of North Macedonia
9th Congress – 2004 Melbourne, Australia
8th Congress – 2002 Vitoria, Brasil
7th Congress – 2000 Ravenna, Italy
6th Congress – 1996 Alexandria, Egypt
5th Congress – 1994 Kamakura, Japan
4th Congress – 1992 St. Petersburg, Russia
3rd Congress – 1986 Louvain La Neuve, Belgium
2nd Congress – 1984 Trier, Germany
1st International Meeting 1980 Ravenna, Italy
AIMC Exhibitions/Contests
International Congresses Exhibitions
2018 Paray-le-Monial, France
2016 Spilimbergo, Italy
2012 Athienou, Cyprus
2010 Athens Greece
Solo Exhibitions of AIMC members
Bibliomosaico Edition 2017
AIMC PRIZE for STUDENTS (PAS) Premio AIMC STUDENTI
AIMC Publications
AIMC Portraits
Some notes on the foundation and history of the AIMC openclose
The AIMC (L'Associazione Internazionale Mosaicisti Contemporanei / International Association of Contemporary Mosaicists) was founded in Ravenna in 1980. The idea for the association grew out of a two-day study on contemporary mosaic, and it became a reality through the efforts of Professor Isotta Fiorentini, Peter Fisher (1922-2000) and many Italian and international mosaicists and scholars. The idea was given official support by the Cultural Commission of the Municipality of Ravenna. Isotta Fiorentini, the historical heart and soul of the association, recounts how Peter Fisher, a journalist and art critic who frequented at the studio of Renato and Carlo Signorini during those years, was instrumental in contacting mosaicists from around the world. Although German by nationality and a resident of London since 1956, Fisher considered Ravenna his beloved city and he spent time there every year. He argued that mosaic is a timeless, profound art. He strongly believed in mosaic as an appropriate means of contemporary artistic expression, due to its intrinsic nature which enables the use of a stylistic pluralism, characteristic of our time.
In 1987, the AIMC created a project in Ravenna of particular importance: a large public work of art, entitled MOSAIC OF FRIENDSHIP AMONG PEOPLES, at THE PEACE PARK. It is a permanent outdoor exhibition of mosaic works by contemporary artists from many countries, a unique open-air museum. The AIMC still enjoys the support of the City of Ravenna, which houses the administrative office of the Association at the Art Museum of the City (MAR), on the ground floor of the Loggetta Lombardesca. Since its foundation, the AIMC has remained active with conferences, exhibitions and publications aiming to promote contemporary mosaic throughout the world. The Association has organized International Conferences and Exhibitions in:
Trier, Germany with the local University, Department of Letters (1984)
Louvain-la-Neuve, Belgium with the local University, Department of Architecture (1986)
St. Petersburg, Russia with the Academy of Fine Arts (1992)
Kamakura, Japan with the State University of Fine Arts and Music of Tokyo (1994)
Alexandria, Egypt with the local University, Department of Fine Arts (1996)
Ravenna, Italy with the University of Bologna, Department of Cultural Heritage and Conservation (2000)
Vitoria, Brazil with the "Universidade Federal do Espirito Santo" (2002)
Melbourne, Australia with the ACU National University and the Australian Multicultural Foundation (2004)
Skopje, Republic of North Macedonia with the Museum of Modern Art and the sponsorship of UNESCO (2006)
Gaziantep, Turkey in collaboration with the Zeugma Museum (2008)
Athens, Greece in collaboration with the Greek Mosaic Association (2010)
Athienou, Cyprus at the Callinikeio Municipal Museum (2012)
Vienna, Austria at the Kulturzentrum Palais Palffy (2014)
Spilimbergo, Italy with the Scuola Mosaicisti Del Friuli (2016)
Paray-le-Monial, France, with Mosaïque Magazine and Maison de la Mosaïque Contemporaine (2018)
AIMC continues to host a Congress every two years, each time in a different international location. The Congress includes several days of activities, including presentation of works by members, speakers on relevant topics, exhibition of members' works, a General Assembly meeting, as well as much informal networking, exploration of the cultural offerings of the locality, and fun!
In October 2018, Maison de la Mosaïque Contemporaine in Paray-le-Monial, and Mosaïque Magazine will host in Paray-le-Monial, France, our next congress.
Stay tuned on our website -page: AIMC Congresses- and newsletter for updates on the 2018 congress.
The first logo of the AIMC (1980-2013)
AIMC Congress - Louvain-la-Neuve-Belgium, 1986
AIMC members present in the exhibition catalog in Egypt, 1996
Harriet Backer photo archive. openclose


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FASSEL IS IRRITATED AT SEHORN'S STATUS
Jason Sehorn and New York Giants Coach Jim Fassel seemingly were at odds Thursday after the cornerback, recovering from knee surgery, didn't participate in an afternoon workout at training camp in Albany, N.Y.
While neither man said there was a problem, both seemed to be frustrated by the constant questions about when Sehorn will play in a game.
Sehorn hinted that he probably won't play until the Giants open the regular season Sept. 5 against San Francisco.
Fassel seemed more concerned about not having Sehorn at practice on Thursday. He gave a somewhat cryptic "no" response when asked whether Sehorn was supposed to practice or something happened. The coach then answered, "No!" when asked if Sehorn would play against the Jets or if anything about the Sehorn situation was bothering him.
CHARGERS RIP RAMS
The San Diego Chargers intercepted St. Louis backup QB Marc Bulger twice and rolled to a 31-10 victory over the host Rams.
The Chargers (2-1) put the game away with a 21-point third quarter. The Rams lost FB James Hodgins, who fractured his left foot in the second quarter. Hodgins will be sidelined an estimated six weeks.
QB Drew Brees played the first half and was 12-of-17 for 150 yards, but the Chargers got only three points on a 27-yard field goal by Wade Richey. Backup Doug Flutie played the third quarter and was four-of-four for 84 yards and a touchdown.
HEAT BOTHERS ROOKIE
Qasim Mitchell, a 335-pound rookie offensive lineman, was taken to a hospital as a precaution after experiencing heat-related problems during the Cleveland Browns' practice.
Team spokesman Todd Stewart said Mitchell, an undrafted free agent, was taken to the Cleveland Clinic for evaluation.
"We expect him to be fine," Mitchell said.
Near the end of the two-hour workout, Mitchell, who participated in all the drills, was assisted off the field by two trainers and taken to the locker room.
KRAFT HAS BYPASS SURGERY
New England Patriots owner Robert Kraft was recovering after undergoing unscheduled heart bypass surgery.
Kraft was at Brigham and Women's Hospital in Boston for a check-up Wednesday when an angiogram identified a heart condition that required immediate surgery.
The 61-year-old Kraft is expected to make a full recovery, the Patriots said in a statement.
RAIDERS WAIVE HOYING
The Oakland Raiders waived backup QB Bobby Hoying.
Hoying was Rich Gannon's seldom-used backup for most of three seasons after arriving in a trade with Philadelphia in 1999.
Although the effects of QB Kordell Stewart's concussion have subsided, Pittsburgh Steelers Coach Bill Cowher selected Tommy Maddox to start Saturday's preseason game at Detroit. Maddox is scheduled to play the first half. Ex-Lions QB Charlie Batch will play the second half. . . . The Baltimore Ravens signed CB Robert Tate. Tate, a five-year veteran, started 22 games over the past two seasons with the Vikings.
Sampling the best of the Beaver: 7 things I tried at Buc-ee's in Daytona


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Decorative concrete has grown in popularity so much that it is now commonly used for indoor as well as outdoor applications. With options of adding color into the concrete, colored powdered release in addition to the existing stain products available, you are limited only by your imagination.
Stamped or colored concrete is now commonplace in every facet of the home and business. With so many choices available, it is helpful to consult with knowledgeable people to sift through the options and customize the benefits for each application.
integral coloring of cementitious materials.
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Eco-Stain is a liquid semi-transparent water-based concrete stain for accent coloring that can be sprayed, brushed rolled or even sponged. Used for staining concrete floors and walls including overlays and cast concrete products. Offered in 30 UV stable concrete stain colors, EcoStain can be diluted up to 10:1 to create virtually any color stain color desired. Applying concrete dyes has never been easier.
So how does it work? Unlike acid stains which perform a chemical reaction once in contact with the surface of the concrete to achieve their color, Eco-Stain for concrete floors is a penetrating concrete stain which gives much more control over the design and colors you may want to apply to your project. Giving you more control isn't the only benefit, though. Since it is a water-based solution, there is no need for any neutralization or clean-up! As soon as the product has been down long enough to be dry to the touch, you're ready to seal.
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A release agent is required when stamping concrete. Without it, the concrete paste can adhere to the stamp, ruining the final texture and appearance. There are two primary kinds of release agent: powdered and liquid. Powdered release agents will become embedded in the low stops of the concrete (the high spots of the stamp), imparting a contrasting color in a process called antiquing. When the dust of a powdered release is not desirable or antiquing is not the desired effect, clear liquid release agent can be sprayed on to the surface and the stamps to achieve the same bond breaking effect without adding any contrasting color.


The workshop on a fire plan for Alpine County was a success, according to Alpine County Fire Safe Council coordinator Clint Celio of C.G. Celio & Sons.
The Fire Safe Council met on Nov. 22 to mull over a draft of the Alpine County Community Fire Plan and to get comments from county supervisors and the public.
Celio said the plan should be finalized by the end of the year.
Communities within the county that are most at risk are Woodfords, Markleeville, Bear Valley and Kirkwood, according to the fire plan.
In Woodfords, the Mesa Vista and Upper Diamond Valley/Manzanita Lane planning areas received the highest risk ratings. Alpine Village, Crystal Springs, River Ranch and the Washoe Indian Colony got medium-risk ratings.
In Markleeville, the Markleevillage/Thornburg subdivision and the Shay Creek subdivision were rated as high risks for fire. Downtown Markleeville and the Morrison subdivision received a medium-risk rating.
Ratings for the Kirkwood and Bear Valley planning areas have not been completed.
The fire plan states that the homeowner is the first line of defense in protecting structures from wildfire.
"In general, defensible space needs to be improved around structures," it said.


Investigations are underway after a man who was tasered by police in Llanelli, Carmarthenshire on Tuesday evening died.
He became unresponsive after being stunned, but officers are reported to have 'engaged' with the man before discharging the weapon. He was later pronounced dead.
Police were originally called in to deal with the man, identified by locals as 43-year-old Spencer Beynon, because of concerns about his behaviour and welfare. Beynon appeared to be injured before he was stunned, though specific details are yet to emerge.


text:
Q: Choosing the best clustering algorithm and evaluating the results I'm trying to separate my data into clusters using the k-means algorithm and the hierarchical algorithm, choose which algorithm fits my data the best, and evaluate the results. However, all of my results are conflicting.
As a first step, I plotted my data using the k-means method. When I split the data into 6 or more clusters, the clusters overlapped. I decided that 5 clusters is the optimal number in this case, based on the graphs (we want all of the data points within one cluster to have approximately the same distance from the cluster's centre). The Elbow plot showed that the optimal number of clusters is 3, while the Silhouette plot showed that the optimal number of clusters in this case is 5.

Then, I plotted my data using different hierarchical methods (agnes, diana, ward, ward.2 etc). Honestly, I couldn't deduct anything from the respective dendrograms. The Elbow and Silhouette plots show that the optimal number of clusters n this case (FUN = hcut) is 3.

Finally, I used the clValid function for the clustering methods "kmeans", "hierarchical", "agnes", "diana", and for 2 to 7 clusters. These were the  optimal score results:
Optimal Scores:

             Score  Method       Clusters
Connectivity 9.5071 kmeans       2
Dunn         0.4821 hierarchical 5
Silhouette   0.3050 diana        4

I have to idea how to evaluate my results and choose the most fitting algorithm. Not only are all of the results vastly different, they are not good at all. In all of the cases above, the Silhouette coefficient is about 0.3. The connectivity and Dunn scores are not good either. I know that I shouldn't set my mind on choosing only one algorithm, however these are my assignment's instructions. I also know that hierarchical clustering is not the best for big datasets, however I'm not sure if my dataset qualifies as big (7 variables x 30 rows).
Any help would be appreciated!
Here is my data (it has been scaled):
cities_struct <-
  structure(
    c(
      -0.531504494839432,
      0.934498212505445,
      -0.482512116444874,-0.601224417939382,
      -0.961129966914795,
      -0.900831655044569,
      -0.968667255898573,
      0.849703711437939,
      -0.380758715163867,
      0.420078239362577,
      -0.139565467682961,
      1.18511307121607,
      -0.139565467682961,
      -0.900831655044569,
      2.20641572851803,
      0.146851513700614,
      2.09900936049919,
      0.930729568013556,
      -1.29465500444698,-0.495702372166485,
      0.69330496502454,
      -1.11941303557414,
      1.73721948927783,-0.900831655044569,
      -1.08172659065525,
      -0.955477000176961,
      -0.424098126820592,
      0.744181665665043,
      -0.450478638263816,
      0.781868110583935,
      -0.695243776334721,-0.910447510783003,
      -1.41435207817665,
      -1.29273817418867,
      -1.20760844139709,-1.63907342250225,
      -0.246858599892108,
      1.11627463698178,
      -1.04792409789984,-0.961208096795374,
      -0.330402064370802,
      0.454800750508065,
      0.72658138811597,-0.33304584489228,
      0.169272454188477,
      1.34363976182886,
      1.16280517415978,-0.901458657009979,
      -0.211960697008603,
      -0.0654952561187398,-0.0395862070082589,
      0.983028098699304,
      0.173502503022841,
      1.11151583204312,-1.41540959038524,
      0.784215803484183,
      1.22202585784088,
      0.702258607318375,
      0.716535022134355,
      2.04635662443762,
      -1.28905720128345,
      0.202857461317712,-0.929495034397562,
      -1.19245841017978,
      -1.57885357459447,
      -0.720197653672938,-1.54665397755992,
      0.063325874167963,
      -0.0225397179241905,
      0.202857461317712,-0.0225397179241905,
      0.063325874167963,
      1.57134033528641,
      -1.54665397755992,
      1.201044969389,
      0.846849402008864,
      0.562419628203605,
      0.562419628203605,
      0.524853431663288,
      -0.16207130507394,
      0.562419628203605,
      -0.489433874925275,
      0.562419628203605,
      -0.908028636374524,
      -0.628965462075025,
      -0.730930852684457,
      0.23505705835227,
      0.562419628203605,
      1.56060713627489,
      2.48366225126554,
      1.41192357030813,
      1.2940176354181,
      -0.725121499573696,
      -0.747228862365577,-0.563000839099902,
      -0.865134797255608,
      0.173911253962797,
      2.61309028200033,-0.0913770995397747,
      0.0928509237259,
      -0.415618420487362,
      -0.106115341401029,-0.636692048406172,
      -1.52835568101204,
      -0.673537653059307,
      1.38981620751625,-0.0545314948866398,
      -1.04936282052128,
      -0.526155234446767,
      1.15400433773619,-0.437725783279243,
      -1.29254381123197,
      0.085481802795273,
      0.741333565621075,-1.1893761182032,
      1.49298390054503,
      -0.887242160047489,
      0.947668951678631,
      0.675011477245432,
      -0.282974243736076,
      -0.396322302114211,
      -0.360792185380256,-0.422162387011633,
      -0.68702325721021,
      -0.735473416392876,
      -0.873717870594084,
      0.0436051432643998,
      0.440896448562264,
      -0.564282853947455,
      -0.548132800886566,-0.00355301167339554,
      0.53585876056029,
      -0.82203770079924,
      -0.71738535696468,-0.802011635003738,
      -0.469966544071864,
      -0.14373547224191,
      -0.693483278434565,-0.647617127741641,
      2.6747717879444,
      -0.220609724811741,
      -0.435082429460344,-0.407304338195616,
      1.74517473375964,
      -0.725783384556343,
      3.07206309324227,
      0.474488558928913,
      0.917000012797266,
      0.99775027810171,
      -0.22513173966879,
      0.886213890432966,
      0.204510897792223,
      0.917200390098455,
      0.576348893778083,
      1.30453163591706,
      0.390429895785153,
      0.312963646621432,
      -0.244793347357358,-0.415219095517544,
      0.343950146286921,
      -0.3997258456848,
      -0.0743675991971721,-1.17438833732201,
      0.932693639931199,
      0.436909645283385,
      -1.85609132996275,
      0.917200390098455,
      1.30453163591706,
      0.390429895785153,
      -0.880016590499869,
      0.312963646621432,
      0.343950146286921,
      -1.68566558180257,
      -1.74763858113354,-0.3997258456848,
      -0.0743675991971721,
      -1.17438833732201,
      1.97074137872506,
      0.436909645283385,
      -1.85609132996275,
      -0.206505199008913,
      0.365459487675157,-0.981534271497978,
      -0.00259471159639672,
      -0.407031279991346,-0.178583845960726,
      -0.950228512019708,
      0.0388642671721227,
      -0.533100419511947,-0.038130979112271,
      -0.397724162308617,
      -0.236964856879662,
      0.481375407905508,
      0.141242561682141,
      1.77421866419731,
      -1.72356538129373,
      -0.981534271497978,-0.407031279991346,
      -0.178583845960726,
      1.56776987196223,
      -0.950228512019708,-0.038130979112271,
      0.108244598988829,
      1.07364653317008,
      -0.397724162308617,-0.236964856879662,
      0.481375407905508,
      2.76331144338914,
      1.77421866419731,-1.72356538129373
    ),
    dim = c(30L, 7L),
    dimnames = list(
      c(
        "1",
        "2",
        "3",
        "4",
        "5",
        "6",
        "8",
        "9",
        "10",
        "11",
        "15",
        "16",
        "18",
        "19",
        "21",
        "22",
        "25",
        "27",
        "28",
        "29",
        "30",
        "33",
        "34",
        "35",
        "37",
        "38",
        "40",
        "43",
        "44",
        "45"
      ),
      c(
        "sunshine",
        "pollution",
        "work",
        "activities",
        "places",
        "bottle",
        "membership"
      )
    ),
    "`scaled:center`" = c(
      sunshine = 2140.06666666667,
      pollution = 44.0786666666667,
      work = 1674.2,
      activities = 230.4,
      places = 1661.5,
      bottle = 1.348,
      membership = 37.3406666666667
    ),
    "`scaled:scale`" = c(
      sunshine = 530.694790740912,
      pollution = 18.9123112126017,
      work = 186.337735641866,
      activities = 135.701396328546,
      places = 1547.98253019635,
      bottle = 0.645442377032191,
      membership = 11.818911477194
    )
  )

A: Some thoughts to hopefully help you... Do you have a reason to think that there should be clusters in the first place? If so, how many would you say is a sensible number?
In the end, clustering algorithms and selection methods will return some optimal number of clusters even if the data is mostly noise, unless it is really white noise, which is unusual in many real cases. In my experience, it is not surprising that different methods of clustering and assessment give different results since they rely on different assumptions.
I think it is also worth considering how you want to use the results. If you want to prove that there are some clusters, in my opinion the results you show are not convincing. However, if you want to use the clusters as a sort of data reduction to facilitate interpretation, then I wouldn't be too obsessed about finding the optimal number based on some metrics. I would instead think in terms of what number is more reasonable.
You could try also (h)dbscan which has the nice feature of allowing some datapoints to remain unclustered. But again, I wouldn't rely solely on algorithms to decide what is best.

redpajama_set_name: RedPajamaStackExchange


text:
// Copyright 2005-2010 Gallio Project - http://www.gallio.org/
// Portions Copyright 2000-2004 Jonathan de Halleux
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

using System;
using System.IO;
using Gallio.Runtime.ConsoleSupport;
using MbUnit.Framework;

namespace Gallio.Tests.Runtime.ConsoleSupport
{
    [TestFixture]
    public class CommandLineArgumentAttributeTests
    {
        [Test]
        public void UnLocalizedArgumentAttributeDoesNotNeedResource()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.LongName = "no-logo";
            Assert.AreEqual("no-logo", attribute.LongName);
        }

        [Test]
        public void UnLocalizedNullArgumentAttributeReturnsNull()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.Description = null;
            Assert.AreEqual(null, attribute.Description);
        }

        [Test]
        [ExpectedException(typeof(InvalidOperationException), "Localized attribute strings must supply a resource")]
        public void LocalizedArgumentAttributeMustSupplyResourceType()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.Description = "#Description";
            var desc = attribute.LocalizedDescription;
        }

        [Test]
        [ExpectedException(typeof(InvalidOperationException), "Localized attribute string not found in resource named")]
        public void LocalizedArgumentAttributeMustFindResourceNamed()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            attribute.Description = "#DescriptionWhichWillNotBeFoundInResource";
            var desc = attribute.LocalizedDescription;
        }

        [Test]
        [ExpectedException(typeof(InvalidOperationException), "Localized attribute string does not point to a string resource")]
        public void LocalizedArgumentAttributeMustFindStringResourceNamed()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            attribute.Description = "#CommandLineArgumentAttributeTests_NotAStringResource";
            var desc = attribute.LocalizedDescription;
        }

        [Test]
        public void LocalizedArgumentAttributeShortName()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.ShortName = "#CommandLineArgumentAttributeTests_ShortName";
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_ShortName", attribute.ShortName);
            Assert.AreEqual("Short name", attribute.LocalizedShortName);
        }

        [Test]
        public void LocalizedArgumentAttributeLongName()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.LongName = "#CommandLineArgumentAttributeTests_LongName";
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_LongName", attribute.LongName);
            Assert.AreEqual("Long name", attribute.LocalizedLongName);
        }

        [Test]
        public void LocalizedArgumentAttributeDescription()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.Description = "#CommandLineArgumentAttributeTests_Description";
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_Description", attribute.Description);
            Assert.AreEqual("Description", attribute.LocalizedDescription);
        }

        [Test]
        public void LocalizedArgumentAttributeValueLabel()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.ValueLabel = "#CommandLineArgumentAttributeTests_ValueLabel";
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_ValueLabel", attribute.ValueLabel);
            Assert.AreEqual("Value label", attribute.LocalizedValueLabel);
        }

        [Test]
        public void LocalizedArgumentAttributeOneSynonym()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.Synonyms = new [] {"#CommandLineArgumentAttributeTests_Synonym1"};
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_Synonym1", attribute.Synonyms[0]);
            Assert.AreEqual("Synonym1", attribute.LocalizedSynonyms[0]);
        }

        [Test]
        public void LocalizedArgumentAttributeMultipleSynonyms()
        {
            var attribute = new CommandLineArgumentAttribute(CommandLineArgumentFlags.AtMostOnce);
            attribute.Synonyms = new[] { "#CommandLineArgumentAttributeTests_Synonym1", "#CommandLineArgumentAttributeTests_Synonym2" };
            attribute.ResourceType = typeof(Gallio.Tests.Properties.Resources);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_Synonym1", attribute.Synonyms[0]);
            Assert.AreEqual("Synonym1", attribute.LocalizedSynonyms[0]);
            Assert.AreEqual("#CommandLineArgumentAttributeTests_Synonym2", attribute.Synonyms[1]);
            Assert.AreEqual("Synonym2", attribute.LocalizedSynonyms[1]);
        }

    }
}

redpajama_set_name: RedPajamaGithub


text:
Home » Prevailing crypto winter wipes over 70,000 Bitcoin millionaires during entire 2022
Silas Stein / DPA Picture Alliance / Avalon
Prevailing crypto winter wipes over 70,000 Bitcoin millionaires during entire 2022
written by LLT FINANCE REPORTER 3rd Jan 23 4:49 pm
The world of Bitcoin (BTC) has produced several millionaire success stories, as early adopters of the cryptocurrency have seen their investments pay off significantly. However, the recent volatility experienced by Bitcoin has resulted in millionaire holders suffering significant losses.
In particular, according to data acquired and calculated by Finbold as of January 2, 2023, the number of Bitcoin millionaire addresses stood at 28,007, representing a drop of about 71.73% or 71,085 addresses from January 2, 2022. At the start of last year, the number of millionaire addresses stood at 99,092.
Elsewhere, in January 2022, the number of addresses holding $1 million worth of Bitcoin stood at 90,902 to record, a year-over-year (YoY) drop of 73.33% to 24,208 in the first month of 2023. At the same time, addresses with at least $10 million worth of Bitcoin stood at 8,190 in January 2022 before plunging by 62.5% to 3,799 in January 2023.
Notably, most Bitcoin addresses hold between $100 and $999 worth of BTC at 13.47 million as of January 2023, representing a drop of about 13.33% YoY from 15.63 million. The second largest group of Bitcoin holders accounts for at least $1,000 worth of BTC at 5.33 million as of January 2023, while in 2022, the figure stood at 6.54 million.
Bear market wipes out Bitcoin millionaires
The research report identified some of the contributing factors to the drop in the number of Bitcoin millionaire holders. According to the research report:
"As Bitcoin's price continues to fall, many investors have seen their holdings significantly devalue. Therefore, the drop in millionaire addresses highlights how far the crypto market has fallen from the 2021 bull run. The slump correlates with the asset's volatility which has been a highlight of 2022, plagued by a combination of several factors such as regulatory scrutiny, turbulent markets, macroeconomic factors, and fraud-related incidents like the FTX crypto exchange crash and the Terra (LUNA) ecosystem crash."
Indeed, commentators believe Bitcoin can recover as the crypto market undergoes an extended phase of volatility. At the same time, there is skepticism that the market will reclaim the previous highs.
BitcoinblockchainCrypto FinanceCryptocurrencies
Coinbase adds 19 million verified users in 2022 despite crashing...
PayPal stock price forecast for 2023
Yoyo makes bank card linked retail loyalty a reality, on and offline


You are welcome, Michel. I'm currently working on a set of lowercase letters, and including accented ones now I know at least one person will find them useful!
I was hoping someone would try that! My printer is running out of coloured ink so when I tried, the tiles were a bit too feint to work consistently. I'm going to try printing them on photo paper to see if that works.
Did you try doing that with Numbers Tiles? Woud be great to have them as images for printout, too!
This is great! Do you happen to have them in a pdf or other file where they are the correct size and ready to print as a set? I'm thinking I'd like to print and laminate them. I'm having troubles with my 2nd graders using them with germy hands. We're spreading sickness and they're not really easy to clean since its paper on cardboard.


text:
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