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Questioning the questions in the EMBL Design Lab
You know what it's like being new in the job. Every time I start, I'm thinking about making my boss happy while completing tasks without angering the stakeholders (which also wouldn't help with the former). You feel like plankton in a shark tank. You go with the flow, and you don't even dream of controlling it one day.
Photo by Jake Gaviola on Unsplash
When I look back at my plankton phase at EMBL, it's hard to believe how quickly that turned into doing an illustration for Nature and my mum hanging it on the fridge. Much quicker than that I felt like part of the incredibly passionate team, and experienced what it is to be guided and mentored rather than supervised. In such an atmosphere, you soak up new ways of thinking, like a sponge, and that's what I want to talk about in this blog post.
It was one of my first weeks at EMBL. There was one particular project, with a lot of room for creativity and thus room for many mistakes. Discussions with stakeholders were mostly remote, and their opinions very direct. Tabea was very supportive, but sooner or later you need to start swimming by yourself. And although you know you can always get the help you need, you've just not learned to ask for it yet.
Me sometimes during my first weeks at EMBL / image credit: www.spongebob.com
I was staring at an unfinished email that was meant to, yet again, explain my own decisions, which I didn't trust. I had already done a few revision rounds, with three colour palettes. I was running in circles with shapes, forms and fonts. I wasn't only out of control with the flow; I also felt like it was leading me and the project nowhere.
I felt like I'd considered every possible variation of the font and colour, form and positioning, styles and shadows, round shapes, sharp edges, loose kerning, no kerning, you name it. I knew what colour I preferred. I also knew our stakeholders hated it. I knew their favourite font; I knew the least favourite one. It felt like the only question left would be their shoe size. A few days into the process, Tabea found me in front of a wall covered with countless versions of the visual.
She put a Curcuma chai drink on my desk, turned off my screen, and asked:
"What difference in the world do we want this visual to make?"
You may laugh at first. It sounds like a big existential question. After all, it's hard to imagine curing cancer or feeding the hungry with Photoshop. But that was, indeed, a real life-changer for me.
I stopped asking "Do you prefer blue or green?", or "What do you think would feel better against your existing visuals?".
While these remain relevant questions, it wasn't the right time to ask them. It was like assembling jigsaw puzzles but starting not from the edges but the centre. With the wrong picture in front of you.
Instead, I'd ask "Do you want this visual help you win a grant, get more followers on Twitter, or both?".
It would be a challenge to find people with more love for questions than scientists. We quickly came up with specific goals this project was to achieve. Everything fell into place. The critical feedback started making sense, and my explanations could finally relate to a context that matters to both sides of the table.
Tabea has often been untangling processes, but that lesson was particularly valuable. I know I've sometimes been asking this question somewhere between the lines, but making it a conscious step in the process made a huge difference.
We have different roles and expertise but having a sense of a shared goal, that we are all aware of, gets stakeholders excited and engaged. Asking first about the impact also helps us to evaluate our work and set metrics that matter. After all, how could you know whether you created the right thing without first asking what you wanted that thing to do?
What processes feel like when you ask the right questions at the beginning / image credit: The Walt Disney Company
Indeed, diving into an app development project needs different tasks and processes to be successful than having to retouch a portrait photo. After the big question, more specific questions follow. After every "How do we get more people to sign up for that conference?" there is a "Where should I place the button so people can find it?" and "What colour should the button be, so people are not afraid to click it?".
The more precise that first question is, the easier it is to think of useful follow-ups.
So what questions has Design Lab – together with colleagues from EMBL teams, research groups, and sites – recently come up with, you ask?
How do we make Heidelberg locals come and listen to a talk about the importance of vaccination? What image do we choose for the poster? How do we make injection needles look less scary?
How do we help new group leaders attract young talent? How do we help them get more followers on Twitter? What visual do we design, so they click on the link?
How do we help EMBL's brand strengthen its outlook? How can we make EMBL look more coherent across its sub-brands and sites? What font and colours could support it? Could EMBL's design past help futureproof its brand?
How do we bring more people to that PhD seminar? How do we make more room on the poster for a 'free pizza' sticker?
How do we attract new scientific talent to EMBL? How do we help EMBL scientists gain more recognition for their research? How do we win a Nature cover? How do we illustrate something nobody has ever seen before? Is it OK to retouch scientific images? How do we show the different stages of nuclear pore complex development?
Sweet potato fries or cabbage lasagne?
How do we stop the spread of antibiotic resistance? How do we make people read an article that says that taking too many painkillers may not be a good idea?
How do we help EMBL's researchers communicate their findings? How do we make more scientists aware of Design Lab and the help we can offer? How do we structure a Design Lab tour? How do we make the collaboration process exciting and efficient?
How do we get the public to see the scope of the work EMBL does? Who's our audience? Where do we want to reach them? How do we pack more than 40 research groups into one publication?
Why are there no beans in the coffee machine when I have three minutes until the morning team huddle?
Let's not make it seem like we spend our days only asking questions, though. We brainstorm, draft, look for the right vector curve and make sure we get the print presets right. We use the same tools, techniques and standards that design agencies do. There are things, however, that make our daily practice quite unique.
We learn about groundbreaking discoveries before they get published. We also get to hear about them from the scientists themselves, sipping tea by the benches where it happened.
With huge discoveries comes significant responsibility, though. When you're a designer at EMBL, drawing a DNA helix twisting the wrong way is like misspelling 'detail-oriented', and there are many critical mistakes you can make even if you're only asked to retouch an image. But we're not afraid to take on challenging projects. Tabea makes sure our tiny, tight-knit design team feels like a small family, and in such an atmosphere of honest openness, there is no room for fear other than the fear of a poor server connection.
On top of that, we spend hours watching YouTube explainer videos, proudly renew our library loan of Biology for Dummies every month, and visit scientists in their labs. They teach us the secrets of life that haven't yet been written in textbooks; we show them how to communicate these to the world visually, so the world listens. And on the listening thing, we're lucky to work together with colleagues across editorial, media, core content, digital and outreach teams. But that part deserves a whole new blog post, so let me leave it there.
P.S. Stay tuned for more blog posts from us as we guide you through our daily challenges. Follow us on Instagram @designlab_embl and in the meantime, spread the news because we're looking for a designer to join our awesome Design Lab in Heidelberg!
Author: Aleksandra Krolik
A designer and illustrator who works alongside scientists and physicians. She likes plankton, cosmic rays, and cheesecake. Not necessarily in that order. Proudly supporting EMBL Design Lab.	View all posts by Aleksandra Krolik
Author Aleksandra KrolikPosted on 18/05/2018 Categories UncategorisedTags design, personal perspectives, team
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About the EMBL Communications blog
EMBL Blogs is a platform for voices within the European Molecular Biology Laboratory. The EMBL Communications blog is hosted on this platform and was set up by EMBL's Communications team with the support of IT Services. Views expressed here are those of the authors and may not reflect EMBL's position. Please refer to the EMBL community guidelines and privacy policy.


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Trade liberalization and labor demand elasticities: Evidence from Turkey
Pravin Krishna, Devashish Mitra, Sajjid Chinoy
In the recent debate over the impact of trade reform on factor markets, it has been argued that trade liberalization will lead to an increase in labor-demand elasticities - thus placing labor markets under increased pressure. Using Turkish plant-level data spanning the course of a dramatic trade liberalization, we test this idea. However, we are unable to find any empirical support for this supposed theoritical link: in most of the industries we consider, we cannot reject the hypothesis of no relationship between trade openness and labor-demand elasticities.
Journal of International Economics
Factor demand elasticities
Imperfect competition
Trade reforms
Dive into the research topics of 'Trade liberalization and labor demand elasticities: Evidence from Turkey'. Together they form a unique fingerprint.
Demand Elasticity Business & Economics 100%
Labour Demand Business & Economics 96%
Trade Liberalization Business & Economics 84%
Turkey Business & Economics 74%
Trade Reform Business & Economics 55%
Factor Markets Business & Economics 51%
Labour Market Business & Economics 50%
Trade Openness Business & Economics 46%
Krishna, P., Mitra, D., & Chinoy, S. (2001). Trade liberalization and labor demand elasticities: Evidence from Turkey. Journal of International Economics, 55(2), 391-409. https://doi.org/10.1016/S0022-1996(01)00089-7
Trade liberalization and labor demand elasticities : Evidence from Turkey. / Krishna, Pravin; Mitra, Devashish; Chinoy, Sajjid.
In: Journal of International Economics, Vol. 55, No. 2, 2001, p. 391-409.
Krishna, P, Mitra, D & Chinoy, S 2001, 'Trade liberalization and labor demand elasticities: Evidence from Turkey', Journal of International Economics, vol. 55, no. 2, pp. 391-409. https://doi.org/10.1016/S0022-1996(01)00089-7
Krishna P, Mitra D, Chinoy S. Trade liberalization and labor demand elasticities: Evidence from Turkey. Journal of International Economics. 2001;55(2):391-409. https://doi.org/10.1016/S0022-1996(01)00089-7
Krishna, Pravin ; Mitra, Devashish ; Chinoy, Sajjid. / Trade liberalization and labor demand elasticities : Evidence from Turkey. In: Journal of International Economics. 2001 ; Vol. 55, No. 2. pp. 391-409.
@article{6d8fea33e2a04ca095667c26422862e5,
title = "Trade liberalization and labor demand elasticities: Evidence from Turkey",
abstract = "In the recent debate over the impact of trade reform on factor markets, it has been argued that trade liberalization will lead to an increase in labor-demand elasticities - thus placing labor markets under increased pressure. Using Turkish plant-level data spanning the course of a dramatic trade liberalization, we test this idea. However, we are unable to find any empirical support for this supposed theoritical link: in most of the industries we consider, we cannot reject the hypothesis of no relationship between trade openness and labor-demand elasticities.",
keywords = "Factor demand elasticities, Imperfect competition, Trade reforms, Turkey",
author = "Pravin Krishna and Devashish Mitra and Sajjid Chinoy",
note = "Funding Information: We are grateful to Jagdish Bhagwati, Moshe Buchinsky, Andrew Foster, Anjini Kocchar, Tom Krebs, Anne Krueger, James Levinsohn, Arvind Panagariya, Julie Schaffner, James Tybout and especially two anonymous for helpful comments and assistance in getting the data together. Pravin Krishna gratefully acknowledges financial assistance from Brown University in the form of a Salomon faculty research grant as also the hospitality and the intellectual and financial support of the Center for Research on Economic Development and Policy Reform at Stanford University where some of this research was conducted. ",
journal = "Journal of International Economics",
T1 - Trade liberalization and labor demand elasticities
T2 - Evidence from Turkey
AU - Krishna, Pravin
AU - Mitra, Devashish
AU - Chinoy, Sajjid
N1 - Funding Information: We are grateful to Jagdish Bhagwati, Moshe Buchinsky, Andrew Foster, Anjini Kocchar, Tom Krebs, Anne Krueger, James Levinsohn, Arvind Panagariya, Julie Schaffner, James Tybout and especially two anonymous for helpful comments and assistance in getting the data together. Pravin Krishna gratefully acknowledges financial assistance from Brown University in the form of a Salomon faculty research grant as also the hospitality and the intellectual and financial support of the Center for Research on Economic Development and Policy Reform at Stanford University where some of this research was conducted.
N2 - In the recent debate over the impact of trade reform on factor markets, it has been argued that trade liberalization will lead to an increase in labor-demand elasticities - thus placing labor markets under increased pressure. Using Turkish plant-level data spanning the course of a dramatic trade liberalization, we test this idea. However, we are unable to find any empirical support for this supposed theoritical link: in most of the industries we consider, we cannot reject the hypothesis of no relationship between trade openness and labor-demand elasticities.
AB - In the recent debate over the impact of trade reform on factor markets, it has been argued that trade liberalization will lead to an increase in labor-demand elasticities - thus placing labor markets under increased pressure. Using Turkish plant-level data spanning the course of a dramatic trade liberalization, we test this idea. However, we are unable to find any empirical support for this supposed theoritical link: in most of the industries we consider, we cannot reject the hypothesis of no relationship between trade openness and labor-demand elasticities.
KW - Factor demand elasticities
KW - Imperfect competition
KW - Trade reforms
JO - Journal of International Economics
JF - Journal of International Economics

redpajama_set_name: RedPajamaCommonCrawl


text:
The 1926 U.S. National Championships (now known as the US Open) was a tennis tournament that took place on the outdoor grass courts at the West Side Tennis Club, Forest Hills in New York City, United States. The women's tournament was held from August 16 until August 23 while the men's tournament ran from 13 September until 18 September. It was the 46th staging of the U.S. National Championships and the fourth Grand Slam tennis event of the year.

Champions

Men's singles

 René Lacoste defeated  Jean Borotra  6–4, 6–0, 6–4

Women's singles

 Molla Bjurstedt Mallory defeated  Elizabeth Ryan  4–6, 6–4, 9–7

Men's doubles
 Richard Norris Williams /  Vincent Richards defeated  Bill Tilden /  Alfred Chapin 6–4, 6–8, 11–9, 6–3

Women's doubles
 Elizabeth Ryan /  Eleanor Goss defeated  Mary K. Browne /  Charlotte Chapin 3–6, 6–4, 12–10

Mixed doubles
 Elizabeth Ryan /  Jean Borotra defeated  Hazel Hotchkiss Wightman /  René Lacoste 6–4, 7–5

References

External links
Official US Open website

U.S. National Championships
U.S. National Championships (tennis) by year
U.S. National Championships
U.S. National Championships
U.S. National Championships
U.S. National Championships

redpajama_set_name: RedPajamaWikipedia


text:
\subsection{Related Work}

Various content placement strategies have been studied in scientific literature e.g., caching the most popular content everywhere \cite{Paschos_IEEE_Comm_Mag}, probabilistic caching \cite{Prob_Caching_Cache_Hit_vs_Througput_Optimal,Delay_Geo_Caching_2tier_HetNets}, cooperative caching \cite{Pappas_Zheng_Access_2019, Cooperation_Caching_for_HetNets_2017, Cooperative_Caching_Placement_in_Cached_D2D_Underlaid_Cellular_Nets, Chen_CooperativeCaching_TWC, FD_communications_2018}, or caching based on location e.g., geographical caching \cite{D2D_vs_Small_Cell_Caching}.

Additionally, several different performance metrics have been considered.
In earlier studies of wireless caching, cache hit probability (or ratio) \cite{D2D_vs_Small_Cell_Caching}, and the density of successful receptions or cache-server requests \cite{D2D_vs_Small_Cell_Caching, Prob_Caching_Cache_Hit_vs_Througput_Optimal} have been commonly investigated as a means of evaluating the performance of wireless caching systems.
Furthermore, there are several studies regarding energy efficiency or consumption of the different caching schemes \cite{D2D_vs_Small_Cell_Caching, EE_Downlink_Caching_at_BS, EE_Wireless_Caching_D2D_Cooperative, Cached_D2D_Communications_Offloading_Gain_and_Energy_Cost} as well as taking into account the traffic load of the wireless links \cite{Optimal_Caching_Placement_for_D2D, Spatially_Caching_for_D2D}.
Methods that reduce traffic load by  optimizing the offloading probability or gain can be found in \cite{EdgeCaching_D2D_offloading,Optimal_Caching_and_Scheduling_for_Cache_enabled_D2D_communications,CachingDiversity}.

More recently, a considerable amount of research works analyze wireless caching systems by considering throughput\cite{Pappas_Zheng_Access_2019, Delay_Geo_Caching_2tier_HetNets} and/or delay \cite{OnlineCaching}. Regarding the latter, the majority of research works cope with mitigating the backhaul or transmission delay under the assumption that traffic or requests are saturated. However, there are works that take into account stochastic arrivals of requests at different nodes \cite{Single_Bottleneck_Caching_Networks_Analysis, Adaptive_Video_Steaming_with_Multiple_Helpers}.

Caching has been applied to several  different network realizations e.g., FemtoCaching \cite{FemToCaching}
in which the so-called Femto Base Station (FBS) serve a group of dedicated users with random content requests while simultaneously the non-dedicated users might be served with delay due to cache misses or no FBS availability. The coded/uncoded cached contents are stored in multiple small cells, the so-called femtocells. Given the file requests distribution and the cache size of each femtocell, the content placement is studied such that the downloading time is minimized.

The advent of vehicular networks necessitates the use of caches in order to reduce the latency of content streaming and increase the offered Quality of Service (QoS) \cite{VehNets_OnDemandStreaming, VehNets_qlearning}. Supporting vehicle-to-everything connections urges the exploration of alternative data routing protocols in order to avoid incuring excessive end-to-end delay and backhaul resources allocation.
On the contrary, moving computational and storage resources to the mobile edge computing seems encouraging \cite{MEC_survey, CachingScheme_MEC, CachingChallenges}. This can be done e.g., by employing a new paradigm known as local area data network \cite{VehNets_LowLatencyRadioAccess}, or other advances in radio access networks (RANs) for Internet of Things (IoT) \cite{Advances_Edge_IoT}.

Many contemporary works consider to jointly optimize the problems of content caching (or placement), computing, and allocating radio resources. They usually consider and solve separately these important issues by formulating the computation offloading or content caching as convex optimization problems with different metrics e.g., service latency, network capacity, backhaul rate etc. \cite{JointOptimization1,JointOptimization2}. Works that simultaneously address the aforementioned problems together and propose a joint optimization solution for the fog-enabled IoT or cloud RANs (C-RANs) can be found in \cite{Fog_JointCachingComputing} and \cite{CRAN_ContentPlacement}, respectively.

For some applications e.g., broadcast or multicast applications, single transmissions from the base station to more than one user is useful. The authors in \cite{SmartGrid_caching} propose a content caching and distribution scheme for smart grid enabled heterogeneous networks, in which each popular file is stored in multiple service nodes with energy harvesting capabilities. The optimization of the total on-grid power consumption, the user association scheme, and the radio resource allocation improves the reliability and performance of the wireless access network.
The evolution of 5G mobile networks is going to incorporate cloud computing technologies. The authors in \cite{CaaS} propose the concept of ''Caching-as-a-Service'' (CaaS) based on C-RANS as a means to cache anything, anytime, and anywhere in the cloud-based 5G mobile networks with the intention of satisfying user demands from any service location with high QoS. Furthermore, they discuss the technical details of virtualization, optimization, applications, and services of CaaS in 5G mobile networks.

A key distinction among research papers in wireless caching is the assumption regarding the availability of caching helpers. Many papers consider that caching helpers can serve users requests whenever the requested file is cached while others adopt the more realistic assumption that caching helpers might be unable to assist user requests when, for example, serve other users of interest \cite{FemToCaching, D2D_vs_Small_Cell_Caching}.
To the best of our knowledge, the proposed wireless caching model has not been studied in literature. The work of \cite{Pappas_Zheng_Access_2019} analyzes a wireless network with one caching helper that does not consider hierarchical caching.

\subsection{Contribution}

In this paper, we study a wireless system in which traffic is distinguished in cachable and non-cachable.
A user with limited cache storage requests cachable content from a data center connected to a base station through a backhaul link. Two wireless nodes within the user's proximity exchange non-cachable files and have limited cache storage.
Therefore, they can act as caching helpers for the user by serving its requests for cachable content.
Files not available at the helpers can be transmitted by the base station.
The source helper is equipped with an infinite queue whose role is to save packets intended for the destination helper for transmission in a subsequent time slot. Packets arrive in bursts.

We analyze the system throughput by assuming that the wireless helper nodes have random access to the channel and there is no coordination between them.
First, we characterize the system throughput concerning the case in which the queue at the source helper is stable as well as unstable.
Moreover, we formulate a mathematical optimization problem to optimize the probabilities by which the helpers assist the user to maximize the system throughput.
Subsequently, we characterize the average delay experienced by the user from the time of a local cache miss until it receives the requested file.
Finally, we provide numerical results to show how the packet arrival rate at the source helper, the availability of caching helpers, random access to the channel, caching parameters, and the user's request rate affect the system throughput and delay.

\subsection{Organization of the paper}
In Section \ref{Sec:SystemModel}, we present the system model comprising the network, the caching, the transmission, and the physical layer model. Section \ref{Sec:Throughput} provides the analytical derivation of throughput for the cases of stable and the unstable queue at the source helper. The average delay performance is given in Section \ref{Sec:Delay}. In Section \ref{Sec:Results}, we numerically evaluate our theoretical analysis of the previous sections.
Finally, Section \ref{Sec:Conclusion} concludes our research work.
\subsection{Average delay at user $U$}

\begin{figure}[t]
		\centering
		\includegraphics[width=1.1\linewidth]{Fig10_DelayVsLambda}
	\caption{The average delay at $U$ vs. average arrival rate $\lambda$ at $S$ for $\delta \in \{0.5, 1.2\}$. }
	\label{Fig:delay_vs_lambda}
\end{figure}

Here, we present the numerical results of the average delay experienced by user $U$ to receive content from external sources.
The delay analysis can be found in Section \ref{Sec:Delay}.

In the following plots, we study how the average arrival rate $\lambda$, the data center's random availability $\alpha$, the probability that $S$ attempts transmissions to $D$, $\qs$, the probability that $D$ attempts transmissions to $U$, $\qd$, and the cache size at $U$, $M_U$ affect the average delay at $U$.
The wireless links characteristics can be found in Table \ref{table:links_params}.
The cache sizes were set to hold $M_S = 2000$ and $M_D = 1000$ files at $S$ and $D$, respectively and we used two different values for $\delta$ to examine its effect on the realized average delay. Hence, the values of $\qu, \phd,$ and $\phs$ were given by (\ref{eq:qu}) - (\ref{eq:phs}) depending on $\delta$. Also, we set $q_c=0.5$.

In \figurename~\ref{Fig:delay_vs_lambda}, the average delay versus the arrival rate at helper $S$ is depicted for  $\qs = 0.9, \qd = 0.8, \alpha = 0.7$ and $M_U = 200$.
We observe that the delay increases with the arrival rate and the increase rate is steeper when $\delta=0.5$ compared to $\delta=1.2$.
As we explained in Section \ref{Sec:CachePolicy}, higher values of $\delta$ yield more requests for a few most popular files.
Therefore, for a given $M_U$, the higher the $\delta$, the lower the $\qu$ i.e., user $U$ requests files from external sources with lower probability, as well as lower value for cache hits $\phd$ and $\phs$ (for given $M_D$ and $M_S$).
Fewer requests for files from external resources require fewer transmissions to $U$ and, hence, less interference is realized. Consequently, less average delay is experienced at $U$.

In \figurename~\ref{Fig:delay_vs_alpha}, we present the average delay at $U$ versus data center's availability for two cases of arrival rate $\lambda = 0.2$ and $\lambda=0.4$.
We observe that the delay is lower when $\lambda = 0.2$ since a higher average arrival rate is more likely to create a congested queue at $S$ and, consequently, a higher delay.
In case $\lambda = 0.2$, the delay is decreased with the increase of $\alpha$ and the queue at helper $S$ is stable for any $\alpha \in [0.2,1 ]$. Additionally, the decrease is steeper with $\alpha$ when $\delta=1.2$.
When $\lambda = 0.4$ and $\delta=0.5$, the queue at $S$ remains stable for $\alpha  \in [0.2, 0.8]$ and the delay has the non-monotonic behavior of \figurename \ref{Fig:delay_vs_alpha}
(b). For $\alpha \in [ 0.8, 1]$, the average delay starts decreasing  with $\alpha$ and the queue at $S$ is unstable. When $\delta=1.2$, the queue at $S$ is stable for every value of $\alpha$ and the delay is decreased with the increased availability of the data center.
\begin{figure}[t]
	\centering
	\subfloat[$\lambda = 0.2$]{
	\includegraphics[width=1.05\linewidth]{Fig11A_DelayVsAlpha_lambda02}}
	\hfill
	 \subfloat[$\lambda = 0.4$]{
	 \includegraphics[width=1.05\linewidth]{Fig11B_DelayVsAlpha_lambda04}}
	\caption{The average delay at $U$ vs. data center's random availability $\alpha$ for $\delta \in \{0.5, 1.2\}$. }
	\label{Fig:delay_vs_alpha}
\end{figure}

\begin{figure}[t]
	\centering
	\subfloat[$\lambda = 0.2$]{	\includegraphics[width=1.05\linewidth]{Fig12A_DelayVsQd_Lambda02}}
	\hfill
	\subfloat[$\lambda = 0.4$]{ \includegraphics[width=1.05\linewidth]{Fig12B_DelayVsQd_Lambda04}}
	\caption{The average delay at $U$ vs. $\qs$ for $\delta \in \{0.5, 1.2\}$. }
	\label{Fig:delay_vs_qs}
\end{figure}

In \figurename~\ref{Fig:delay_vs_qs}, we plot the average delay at $U$ versus $\qs$ for $\lambda=0.2$ and $\lambda=0.4$. We observe that as long as the queue at $S$ is unstable, the delay increases with the $\qs$ increase. This is expected since as $\qs$ increases, helper $S$ attempts more transmissions to helper $D$ and, consequently, it is not only less likely to assist $U$ but also $U$'s probability to find an available helper is decreased (since the $S-D$ pair communicates).
Regarding the case in which the queue at $S$ is stable, increasing $\qs$ does not contribute to delay's improvement. Moreover, a lower value of $\qs$ is required to achieve queue stability at $S$ when $\lambda=0.2$ compared to $\lambda=0.4$. This is expected since a higher average arrival rate requires a higher average service rate to maintain queue stability.

\begin{figure}[t]
	\centering
	\subfloat[$\lambda = 0.2$]{ \includegraphics[width=1.05\linewidth]{Fig13A_DelayVsQdLambda02}}
	\hfill
	\subfloat[$\lambda = 0.4$]{ \includegraphics[width=1.05\linewidth]{Fig13B_DelayVsQdLambda04}}
	\caption{The average delay at $U$ vs. $\qd$ for $\delta \in \{0.5, 1.2\}$. }
	\label{Fig:delay_vs_qd}
\end{figure}

In \figurename~\ref{Fig:delay_vs_qd}, we demonstrate the average delay at $U$ versus $\qd$ for $\lambda=0.2$ and $\lambda=0.4$.
In the former case, the delay is slightly decreased with the increase of $\qd$
This can be attributed to helper $D$'s increased assistance that yields more transmissions to $U$ and, hence, potentially decreased delay.
When $\lambda=0.4$, the average delay decreases considerably with $\qd$ when $\delta = 0.5$ due to the increased assistance of helper $D$, but decreases slightly in case $\delta = 1.2$.
This is expected, as we previously explained, since higher values of $\delta$ create more requests for a few most popular files, and, thus, $U$'s request for external content is decreased. As a result, the average delay at $U$ is decreased compared to lower $\delta$ values.

\begin{figure}[t]
	\centering
	\subfloat[$\lambda = 0.2$]{	\includegraphics[width=1.05\linewidth]{Fig14A_DelayVsQu_Lambda02}}
	\hfill
	\subfloat[$\lambda = 0.4$]{	\includegraphics[width=1.05\linewidth]{Fig14B_DelayVsQu_Lambda04}}
	\caption{The average delay at $U$ vs. $M_u$ for $\delta \in \{0.5, 1.2\}$. }
	\label{Fig:delay_vs_Mu}
\end{figure}

In \figurename~\ref{Fig:delay_vs_Mu}, we show the average delay at $U$ versus the cache size at $U$ for $\lambda=0.2$ and $\lambda=0.4$.
The cache size $M_U$ affects the request probability for external content, $q_U$, and as it increases, the $q_u$ decreases.
In any case, the queue at $S$ is stable.
When $\lambda=0.2$, the effect of $M_U$ on the average delay at $U$ is minor.
However, when average arrival rate $\lambda$ is increased, then increasing cache size at $U$ decreases the average delay especially when $\delta$ is lowered.

\subsection{Maximum Weighted Sum Throughput vs. Average Arrival Rate $\lambda$}
\label{sec:Throughput_vs_lambda}
We consider a scenario where the wireless links parameters follow the values in Table \ref{table:links_params}.
The cache sizes and cache hit probabilities are set as per Table \ref{table:caches_params} for two different values for the variable $\delta$ of the standard Zipf law for the popularity distribution that the cached files follow.
\begin{table}[h] \caption{Wireless links parameters.}
	\def1.2{1.1
	\begin{center}
		\begin{tabular}{ | c  c |  }
			\hline \textbf{Parameter} & \textbf{Value} \\ \hline
			\hline
			$P_{tx}(S)$ & $ 1 ~mW$ \\

			$P_{tx}(D)$ & $0.5 ~mW$ \\

			$P_{tx}(DC)$ & $ 10 ~mW$ \\

			$ n $ & $ 10^{-11} ~W $ \\

			$ r_{S \rightarrow D}$ & $ 50 ~m$ \\

			$ r_{D  \rightarrow U}$ & $ 50 ~m $ \\

			$ r_{S \rightarrow U}$ & $ 40 ~m $ \\

			$ r_{DC \rightarrow U}$ & $ 80 ~m $ \\

			$ r_{DC \rightarrow D}$ & $ 100 ~m $ \\

			$ p $ & $ 4 $ \\
			\hline
		\end{tabular}
		\begin{tabular}{ | c  c |  }
			\hline \textbf{Parameter} & \textbf{Value} \\ \hline
			\hline
			$ P_{ S \rightarrow U} $	   & $ 0.903 $ \\

			$ P_{ D \rightarrow U }$   	   & $ 0.607 $ \\

			$ P_{ DC \rightarrow U }$     & $ 0.849 $ \\

			$ P_{ DC \rightarrow U/S }$  & $ 0.115 $ \\

			$ P_{ S \rightarrow D }$ 		& $ 0.779 $ \\

			$ P_{ S \rightarrow D/D }$ 	   & $ 0.779 $ \\

			$ P_{ S \rightarrow D/DC }$   & $ 0.223 $ \\

			$ P_{ D \rightarrow U/S} $     & $ 0.029 $ \\

			$ \gamma_1 $   & $ 0 ~dB $  \\

			$ \gamma_2  $  & $ 0 ~dB $  \\ \hline
		\end{tabular}
	\end{center}
	\label{table:links_params}
\end{table}
\begin{table}[ht!] \caption{Caches parameters and hit probabilities for different values of $\delta$.}
	\def1.2{1.2
	\begin{center}
		\begin{tabular}{ | c c |}
			\hline \textbf{Parameter} & \textbf{Value} \\ \hline
			\hline
			$ M_U $   			  &   $200$   \\
			$ M_D $   			  &  $1000$  \\
			$ M_S $   			  &  $2000$  \\
			$ F     $   			 & $10000$ \\ 	\hline
		\end{tabular}
		\begin{tabular}{ | c  c  c |}
		\hline \textbf{Parameter} & \multicolumn{2}{c|}{\textbf{Value}} \\ \hline
		\hline 	\textbf{$\delta$}   & \multicolumn{1}{|c}{\textbf{$ 0.5$}} &  \multicolumn{1}{|c|}{ \textbf{$ 1.2$}} \\ \hline
		$ \qu $    & \multicolumn{1}{|c}{$ 0.865 $} & \multicolumn{1}{|c|}{$0.196$} \\
		$ \phd $ & \multicolumn{1}{|c}{$ 0.206 $}  & \multicolumn{1}{|c|}{$0.109$} \\
		$ \phs $  &  \multicolumn{1}{|c}{$ 0.221 $}  &  \multicolumn{1}{|c|}{$0.045$} \\
		\hline
		\end{tabular}
	\end{center}
	\label{table:caches_params}
\end{table}

In \figurename~\ref{Fig:stableThroughput_vs_lambda}, the \maxThr~versus the average arrival rate $\lambda$ at helper $S$ is presented for three different values of $w$ when the queue at $S$ is stable.
We chose: (i) $w=1/4$ as a representative case in which $T_U$ is more important than $T_S$, (ii) $w=2/4$ to equalize the importance of  $T_U$ and $T_S$, and (iii) $w=3/4$ to put more emphasis on the importance of $T_S$ versus $T_U$.

In case $w=1/4$, the \maxThr~is a decreasing function of $\lambda$ when $\delta=0.5$ (see Fig. \ref{Fig:stableThroughput_vs_lambda}(a)), but increasing when $\delta=1.2$  (see Fig. \ref{Fig:stableThroughput_vs_lambda}(b)). When $w=2/4$, the \maxThr~is almost constant for any value of $\lambda$ when $\delta=0.5$ and increases with $\lambda$  for $\delta=1.2$.
Regarding $w=3/4$, the \maxThr~is an increasing function of $\lambda$ for any $\delta$ value since $T_S$ clearly dominates $T_U$ in this case.

\begin{figure}[ht]
		\centering
		\subfloat[$ \delta = 0.5$]{
		\includegraphics[width=1.05\linewidth]{Fig5A_StableThroughputVsLambda_d05}}
		\hfill
		\subfloat[$ \delta = 1.2$]{
		\includegraphics[width=1.05\linewidth]{Fig5B_StableThroughputVsLambda_d12}}
	\caption{The \maxThr ~vs. $\lambda$ for $\alpha=0.7$ and different values of $w$ when the queue at $S$ is stable for: (a) $ \delta = 0.5$ and (b) $  \delta = 1.2 $.}
	\label{Fig:stableThroughput_vs_lambda}
\end{figure}

Furthermore, it is observed that the \maxThr~is achieved when $q^{*}_c =1$ for any value of $w$ and $\lambda$ when the queue at $S$ is stable (see Table \ref{table:stable_throughput}), but this is not the case when the queue is unstable i.e., the average arrival rate  $\lambda$ is greater than the average service rate $\mu$ (see Table \ref{table:unstable_throughput} for different values of $\delta$).
In case the queue at $S$ is unstable, it is optimal for helper $S$ to avoid transmissions i.e., $q^{*}_c =0$, to $U$ when $\delta = 1.2$ for any values of $w$ and $\lambda$. However, this is not the case when $\delta=0.5$ since helper $S$ must always attempt transmissions to $U$ i.e., $q^{*}_c =1$, when $w \in \{ 1/4, 2/4 \}$ to achieve the \maxThr.

\begin{table}[h!] \caption{The values of $q^*_S, q^*_C, q^*_D$ for which the weighted sum throughput is maximized and the queue at $S$ is stable for $\alpha =0.7, M_U=200, M_D=1000,$ and $M_S =2000$.}
	\def1.2{1.1
	\begin{center}
		\begin{tabular}{ | c || c | c | c | c || c | c | c | c | } \hline
			\multicolumn{1}{|c||}{} & \multicolumn{4}{c||}{$ \delta = 0.5$} & \multicolumn{4}{c|}{$\delta=1.2$} \\
			\hline
			\hline w & $T_w$  &  $q^*_S$ & $q^*_C$ &  $q^*_D$ & $T_w$  &  $q^*_S$ & $q^*_C$ &  $q^*_D$\\ \hline
			$ 1/4 $  & $ 0.423 $ & $0.029$ & $1$    &  $0$        & $ 0.187 $ & $ 0.988 $ & $1$& $1$ \\ \hline
			$ 2/4 $ & $ 0.286 $  & $0.978$ & $1$    &  $1$        & $ 0.358 $ & $ 0.985 $ & $1$& $1$\\ \hline
			$ 3/4 $ & $ 0.392 $  & $0.996$ & $1$   & $1$        & $0.536 $ & $ 0.999 $& $1$& $1$ \\ \hline
		\end{tabular}
		\\

		\quad
	\label{table:stable_throughput}
	\end{center}
\end{table}
\begin{table}[ht] \caption{The values of $q^*_S, q^*_C, q^*_D$ for which the weighted sum throughput is maximized and the queue at $S$ is unstable for $\alpha =0.7, M_U=200, M_D=1000,$ and $M_S =2000$.}
	\label{table:unstable_throughput}
	\def1.2{1.1}
	\begin{center}
		\begin{tabular}{ | c || c | c | c | c || c | c | c | c | } \hline
			\multicolumn{1}{|c||}{} & \multicolumn{4}{c||}{$ \delta = 0.5$} & \multicolumn{4}{c|}{$\delta=1.2$} \\
			\hline
			\hline w & $T'_w$  &  $q^*_S$ & $q^*_C$ &  $q^*_D$ & $T'_w$  &  $q^*_S$ & $q^*_C$ &  $q^*_D$ \\ \hline
			$ 1/4 $ & $ 0.430 $ & $0$ & $1$ &  $0$ & $0.189$  & $1$ & $0$ & $1$ \\ \hline
			$ 2/4 $ & $ 0.286 $ & $0$ & $1$&  $0$ & $0.363$  & $1$ & $0$ & $1$  \\ \hline
			$ 3/4 $ & $ 0.399 $ & $1$ & $0.024$ &  $1$ & $0.537$  & $1$ & $0$ & $1$ \\ \hline
		\end{tabular}
	\end{center}
\end{table}

\subsection{Maximum Weighted Sum Throughput vs. Cache Size $M_U$}
In this section, we study how the cache size $M_U$ affects the \maxThr.
Recall that $\qu$ decreases as $M_U$ increases.
We consider two different values for $\delta$, same as previously, to examine how $\delta$ affects \maxThr~given different values for $M_U$.

\begin{figure}[ht]
	\centering
	\subfloat[$\delta = 0.5$]{
		\includegraphics[width=1.1\linewidth]{Fig6A_StableThroughputVsMu_d05}	}
	\hfill
	\subfloat[$\delta = 1.2$]{
			\includegraphics[width=1.1\linewidth]{Fig6B_StableThroughputVsMu_d12}	}
	\caption{The \maxThr ~vs. $M_U$ when the queue at $S$ is stable ($\lambda=0.4$) and $\alpha=0.7$ using different values of $w$ for: (a) $ \delta = 0.5$ and (b) $  \delta = 1.2 $.}
	\label{Fig:StableThroughput_vs_Mu}
\end{figure}
\begin{figure}[ht]
	\centering
	\subfloat[$\delta = 0.5$]{
		\includegraphics[width=1.1\linewidth]{Fig7A_StableThroughputVsMud05}}
	\hfill
		\subfloat[$\delta = 1.2$]{
		\includegraphics[width=1.11\linewidth]{Fig7B_StableThroughputVsMud12}}
	\caption{The \maxThr ~vs. $M_U$ for $\alpha=0.7$ using different values of $w$ when the queue at $S$ is unstable for: (a) $ \delta = 0.5$ and (b) $  \delta = 1.2 $.}
	\label{Fig:UnstableThroughput_vs_Mu}
\end{figure}
In \figurename~\ref{Fig:StableThroughput_vs_Mu}, the \maxThr~versus $M_U$ is presented for $\alpha = 0.7, M_D = 1000, M_S = 2000$ and $\lambda = 0.4$ for which the queue at $S$ is stable.
We observe that as the cache size at $U$ increases, the \maxThr~remains almost constant when $\delta = 0.5$ and slightly decreases when $\delta = 1.2$. This is expected since increasing cache size at $U$ results in fewer requests for files from external results.
Moreover, the \maxThr~is higher when the value of $\delta$ is lower since, for a given cache size e.g. $M_U=200$, the probability of requesting content from external resources decreases as $\delta$ is increased.

In \figurename \ref{Fig:UnstableThroughput_vs_Mu}, the \maxThr~versus $M_U$ is presented for the same parameters as in the \figurename \ref{Fig:StableThroughput_vs_Mu} but unstable queue at $S$.
The \maxThr~is an increasing function of $M_U$ for every value of $\delta$ when $w \in \{2/4, 3/4\}$. This is expected since, for these values of $w$, the throughput achieved by $D$ i.e., $T_S$, dominates $T_U$, and $T_S$ is increasing due to the decrease of requests to external content by $U$ (recall that as $M_U$ increases, $q_U$ decreases).
When $w=1/4$, the \maxThr~is almost constant ($\delta=1.2$) or decreases ($\delta=0.5$) as $M_U$ increases.
The latter decrease can be attributed to the fact that $T_U$ i.e., the dominant term in the \maxThr, decreases as $\qu$ decreases (and $M_U$ increases).

The values of $q^*_S, q^*_C,$ and $q^*_D$ that achieve the \maxThr ~are given in Table \ref{table:StableThr_vs Mu} and \ref{table:UnstableThr_Mu} when the queue at $S$ is stable and unstable respectively.

In case $\delta=0.5$ and the queue at $S$ is stable, the \maxThr~$T_w$ is achieved for $q^*_C =1$ and $q^*_D=1$  for every value of $M_U$ and $w$. This means that, for the aforementioned parameters, user $U$ should always be assisted by both $S$ and $D$ to achieve \maxThr~$T_w$.
This is not the case for $\delta = 1.2$ while the queue at $S$ is stable and $M_U \geq 400$.
For every value of $w \in \{1/4, 2/4, 3/4 \}$, user $U$ should only be assisted by $S$ to achieve the \maxThr~$T_w$ since $q^*_C=1$ and $q^*_D = 0$.
We also observe that, in this case, $S$ should more frequently assist $D$ since $q^*_S$ has almost always a higher value compared to $\delta=0.5$.

\begin{table}[htbp]\caption{The values of $(q^*_S, q^*_C, q^*_D)$ that maximize the weighted sum throughput $T_w$ for different values of $M_U$ when $\alpha = 0.7$ and the queue at $S$ is stable.}
	\label{table:StableThr_vs Mu}
	\begin{center}
	\def1.2{1.2}
	\begin{tabular}{ | c || c | c | c | c |  }
			\hline \multicolumn{5}{|c|}{$\delta = 0.5$} \\ \hline
\multicolumn{1}{c}{$M_U $}  &  \multicolumn{1}{c}{} & \multicolumn{1}{c}{$ w = 1/4$} & \multicolumn{1}{c}{$ w = 2/4$} &  \multicolumn{1}{c}{$ w = 3/4$} \\ \hline

		$ 200$ & max. $T_w$ & $0.227$ 	& $0.285$  		  & $0.342$ \\
		$ 		$  & $(q^*_S, q^*_C, q^*_D)$  & $(0.999, 1, 1)$	& $(0.999,1,1)$ & $( 0.999, 1, 1)$ \\ \hline

		$ 400$ & max. $T_w$ & $0.224$ 	   & $0.283$  		  & $0.341$ \\
		$ 		 $ & $(q^*_S, q^*_C, q^*_D)$  & $(0.801, 1, 1 )$  & $(0.824,1,1)$  & $( 0.769, 1,1)$ \\ \hline

		$ 600$ & max. $T_w$ & $0.221$ 	  	& $0.281$ 		   & $0.340 $ \\
		$   	 $ & $(q^*_S, q^*_C, q^*_D)$  & $(0.756, 1, 1)$  &  $(1, 1, 1)$  		 & $(1,1,1)$ \\ \hline

		$ 800$ & max. $T_w$ & $0.219$ 	   & $0.279$ 		  & $0.340$  \\
		$ 		 $ & $(q^*_S, q^*_C, q^*_D)$  & $(1, 1, 1)$ 		   & $(1, 1, 1)$  		  & $(0.746, 1, 1)$ \\ \hline

		$1000$ & max. $T_w$ & $0.217$ 	   & $0.278$ 		  & $0.339$ \\
		$		  $ & $(q^*_S, q^*_C, q^*_D)$ & $(0.732, 1, 1)$  & $(1, 1, 1)$  		  & $(1, 1, 1)$ \\
	\end{tabular}

	\begin{tabular}{ | c || c | c | c | c |  } \hline
	\hline \multicolumn{5}{|c|}{$\delta = 1.2$} \\ \hline
	\multicolumn{1}{c}{$M_U $}  &  \multicolumn{1}{c}{} & \multicolumn{1}{c}{$ w = 1/4$} & \multicolumn{1}{c}{$ w = 2/4$} &  \multicolumn{1}{c}{$ w = 3/4$} \\ \hline

		$ 200$  & max. $T_w$ 							 & $ 0.145$  	 	  & $ 0.230 $ 		 & $ 0.315 $ \\
		$  		$   & $(q^*_S, q^*_C, q^*_D)$ & $ (0.713, 1, 1)$ & $( 0.713, 1, 1)$  & $(0.713, 1, 1)$ \\ \hline

		$ 400$ & max. $T_w$ 							& $ 0.135$    	 	& $ 0.223 $ 			& $ 0.312$ \\
		$ $  	  & $(q^*_S, q^*_C, q^*_D)$	 & $(1, 1, 0)$  	  & $(0.999, 1, 0)$ & $(0.999, 1, 0)$ \\ \hline

		$ 600$ & max. $T_w$ 							& $ 0.129$ 		   	& $ 0.220 $   & $ 0.310 $ \\
		$ 		 $ & $(q^*_S, q^*_C, q^*_D)$  & $ (1, 1, 0)$    	 & $(1, 1, 0)$  & $(1, 1, 0)$ \\ \hline

		$ 800$ & max. $T_w$  							& $ 0.126$   	& $ 0.217 $ 	& $0.309$ \\
		$ 		 $ & $(q^*_S, q^*_C, q^*_D)$  & $(1,1,0)$     & $(1, 1, 0)$   & $(1,1,0)$ \\ \hline

		$1000$ & max. $T_w$  							& $0.123$    & $ 0.216 $   & $0.308$ \\
		$ 		 $ & $(q^*_S, q^*_C, q^*_D)$  & $(1,1,0)$   & $(1, 1, 0)$   & $(0.540,1,0)$ \\ \hline
	\end{tabular}
\end{center}
\end{table}

In case the queue at $S$ is unstable, the values of $(q^*_S, q^*_C, q^*_D)$ for which the \maxThr~$T'_w$ is achieved can be found in Table \ref{table:UnstableThr_Mu}.
We observe that neither helper $S$ should serve helper $D$ ($q^*_S=0$) nor the latter should assist $U$ ($q^*_D=0$) to maximize $T'_w$ when (i) $\delta=0.5$ and $w=1/4$ for any cache  size $M_U$ or (ii) $\delta=0.5, w=2/4$ and user $U$'s cache can hold $M_U = 200$ files.

Moreover, when $\delta = 0.5, M_U \geq 400$ and $w \in \{ 2/4, 3/4 \}$, helper $S$ should only serve helper $D$ and the latter should assist user $U$ since $(q^*_S, q^*_D) = (1,1)$. However, helper $S$ should slightly assist $U$ in some cases when e.g., $M_U = 400$ or $600$.
When $\delta=1.2$, helper $S$ should only serve the destination helper $D$ and the latter should assist user $U$ for any value of $M_U$ and $w$.
Additionally, helper $S$ should not assist user $U$ for any cache size $M_U$ but $400$.

Furthermore, it should be noted that, for any value of $M_U$, the \maxThr~is decreasing as $\delta$ increases when $w=1/4$ and increases as $\delta$ increases when $w \in \{ 2/4, 3/4\}$.

\begin{table}[htbp]\caption{The values of $(q^*_S, q^*_C, q^*_D)$ that maximize the weighted sum throughput $T_w$ for different values of $M_U$ when $\alpha = 0.7$ and the queue at $S$ is unstable.}
	\label{table:UnstableThr_Mu}
	\begin{center}
		\def1.2{1.2}
		\begin{tabular}{ | c || c | c | c | c |  }
			\hline \multicolumn{5}{|c|}{$\delta = 0.5$} \\ \hline
			\multicolumn{1}{c}{$M_U $}  &  \multicolumn{1}{c}{} & \multicolumn{1}{c}{$ w = 1/4$} & \multicolumn{1}{c}{$ w = 2/4$} &  \multicolumn{1}{c}{$ w = 3/4$} \\ \hline
			$ 200 $ & max. $T'_w$  & $0.430$     &  $0.286$ &  $0.399$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  &  $(0,1,0)$    &  $(0,1,0)$ &  $(1,0.024,1 )$ \\ \hline
			$ 400 $ & max. $T'_w$  & $0.398$     &  $0.289$ &  $0.404$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  &  $(0,1,0)$    &  $(1,0.029,1)$ &  $(1,0.005,1)$ \\ \hline
			$ 600 $ & max. $T'_w$  & $0.374$     &  $0.295$ &  $0.410$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  &  $(0,1,0)$    &  $(1,0,1)$ &  $(1, 0.011, 1 )$ \\ \hline
			$ 800 $ & max. $T'_w$  & $0.354$     &  $0.295$ &  $0.416$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  &  $(0,1,0)$    &  $(1,0,1)$ &  $(1,0,1)$ \\ \hline
			$ 1000 $ & max. $T'_w$  & $0.337$     &  $0.298$ &  $0.422$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$   &  $(0,1,0)$    &  $(1,0,1)$ &  $(1,0,1)$ \\
		\end{tabular}
		\begin{tabular}{ | c || c | c | c | c |  } \hline
			\hline \multicolumn{5}{|c|}{$\delta = 1.2$} \\ \hline
			\multicolumn{1}{c}{$M_U $}  &  \multicolumn{1}{c}{} & \multicolumn{1}{c}{$ w = 1/4$} & \multicolumn{1}{c}{$ w = 2/4$} &  \multicolumn{1}{c}{$ w = 3/4$} \\ \hline
			$ 200 $ & max. $T'_w$  & $0.189$ & $0.363$ & $0.537$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  & $(1,0,1)$ & $(1,0,1)$ & $(1,0,1)$ \\ \hline
			$ 400 $ & max. $T'_w$  & $0.190$ & $0.368$ & $0.546$  \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  & $(1,0.046,1)$ & $(1,0,1)$ & $(1,0.484,1)$  \\ \hline
			$ 600 $ & max. $T'_w$  & $0.190$ & $0.371$ & $0.552$  \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$  & $(1,0,1)$ & $(1,0,1)$ & $(1,0,1)$  \\ \hline
			$ 800 $ & max. $T'_w$  & $0.191$ & $0.373$ & $0.556$ \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$ & $(1,0,1)$ & $(1,0,1)$ & $(1,0,1)$  \\ \hline
			$ 1000 $& max. $T'_w$  & $0.191$ & $0.375$ & $0.559$  \\
			$ $ 		& $(q^*_S, q^*_C, q^*_D)$   & $(1,0,1)$ & $(1,0,1)$ & $(1,0,1)$  \\ \hline
		\end{tabular}

	\end{center}
\end{table}

\subsection{Maximum Weighted Sum Throughput vs. Average Arrival Rate $\lambda$ when $M_D = 0$}

We consider a scenario where the system parameters are the same as in Section \ref{sec:Throughput_vs_lambda} (see Tables \ref{table:links_params} and \ref{table:caches_params}), but helper $D$ cannot assist user $U$ since its cache cannot hold any files i.e., $M_D = 0$. Consequently, $\qd=0$ and $\phd=0$ as well.
This scenario will allow the study of the \maxThr~versus $\lambda$ when only one of the two helpers, the least powerful, is unable satisfy $U$'s needs for content from external resources.
\begin{figure}[ht]
	\centering
	\subfloat[$\delta = 0.5$]{
		\includegraphics[width=1.05\linewidth]{Fig8A_StableThroughputVsLambdaMD0_d05}}
	\hfill
	\subfloat[$\delta = 1.2$]{
		\includegraphics[width=1.05\linewidth]{Fig8B_StableThroughputVsLambdaMD0_d12}}
	\caption{The \maxThr ~vs the average arrival rate $\lambda$ for which the queue at $S$ is stable, $ M_U = 200, M_D=0, M_S =2000,$ and $\alpha=0.7$ for: (a) $ \delta = 0.5$ and (b) $  \delta = 1.2 $.}
	\label{Fig:stableThr_vs_lambda_M_D_0}
\end{figure}

In \figurename \ref{Fig:stableThr_vs_lambda_M_D_0}, we plot the \maxThr~versus $\lambda$ when the queue at $S$ is stable and $M_D=0$. We observe that, when $\delta=0.5$, the \maxThr~ (i) is a decreasing function of $\lambda$ for $w = 1/4$, (ii) slightly decreases for $w = 2/4$, and (iii) increases for $w = 3/4$. Recall that, by definition, in the first case $T_U$ dominates $T_S$, in the second case both thoughput terms contribute equally, and in the third case $T_S$ dominates $T_U$.
When $\delta=1.2$, the \maxThr~is increasing with $\lambda$. We observe that higher values of $w$ yield steeper increases in the \maxThr.

When the queue at $S$ is stable, the \maxThr~is always achieved when $ q^*_C = 1 $ for any $w, \delta,$ and $\lambda$ using the system parameters we quoted before.
However, in case $\delta =0.5$, helper $S$ should nearly always assist $U$ since $q^*_S \in \{0.977, 0.999\}$. When $\delta=1.2$, helper $S$ should always assist $U$ as Table \ref{table:stableThr_vs_lambda_MD0} depicts.

On the other hand, when the queue at $S$ is unstable and $\delta=0.5$, helper $S$ should only assist $U$ when $w \in \{1/4, 2/4 \}$ and only assist $D$ when $w=3/4$ (see Table \ref{table:unstableThr_vs_lambda_MD0}).
This is expected since in the latter case, $T_S$ dominates $T_U$ and, hence, it is preferable that $S$ always serves $D$ to maximize the contribution of $T_S$.
In this case, if user $U$ requests content from external resources, it will be only served by the data center.
Moreover, when $\delta = 1.2$, it is optimal that helper $S$ serves only $D$ for any value of $w$.

\begin{table}[t!]\caption{The values of $q^*_S$ and $ q^*_C$ for which the weighted sum throughput is maximized when the queue at $S$ is stable, $\alpha = 0.7, M_U = 200, M_D = 0,$ and $ M_S = 2000$.}
	\def1.2{1.1
	\begin{center}
	\begin{tabular}{ | c || c | c | c || c | c | c |  } \hline
	$M_D =0 $&\multicolumn{3}{c||}{$\delta = 0.5$} & \multicolumn{3}{c|}{$\delta = 1.2$}  \\ \hline
	\hline w & max.  $T_w$  &  $q^*_S$ & $q^*_C$ & max.  $T_w$  &  $q^*_S$ & $q^*_C$ \\ \hline
			$ 1/4 $  & $ 0.445 $ & $0.994$ 	& $1$ & $ 0.187 $  & $ 1 $ & $1$ \\ \hline
			$ 2/4 $ & $ 0.300$  & $0.977$	& $1$ & $ 0.358 $  & $ 1 $ & $1$ \\ \hline
			$ 3/4 $ & $ 0.348 $  & $0.999$ 	& $1$ & $ 0.529 $ &  $ 1 $ & $1$  \\ \hline
	\end{tabular}
	\end{center}

	\label{table:stableThr_vs_lambda_MD0}
\end{table}

\begin{table}[ht] \caption{The values of $q^*_S$ and $q^*_C$ for which the weighted sum throughput is maximized when the queue at $S$ is unstable, $\alpha = 0.7, M_U = 200, M_D = 0,$ and $M_S = 2000$.}
	\def1.2{1.2}
	\begin{center}
	\begin{tabular}{ | c || c | c | c || c | c | c |  } \hline
	$M_D =0 $&\multicolumn{3}{c||}{$\delta = 0.5$} & \multicolumn{3}{c|}{$\delta = 1.2$}  \\ \hline
		\hline w  & max. $T'_w$ &  $q^*_S$ & $q^*_C$ & max. $T'_w$ &  $q^*_S$ & $q^*_C$  \\ \hline
		$ 1/4 $ & $ 0.451 $ & $0$ & $1$ & $0.187$  & $1$ & $0$\\ \hline
		$ 2/4 $ & $ 0.301 $ & $0$ & $1$ & $0.359$  & $1$ & $0$ \\ \hline
		$ 3/4 $ & $ 0.349 $ & $1$ & $0$ & $0.531$  & $1$ & $0$\\ \hline
	\end{tabular}
	\end{center}
	\label{table:unstableThr_vs_lambda_MD0}
\end{table}

\subsection{Maximum Weighted Sum Throughput vs. Average Arrival Rate $\lambda$ when $M_s = 0$}
Here, we study the \maxThr~versus the average arrival rate $\lambda$ when node $S$ is not equipped with cache i.e., $M_S=0$, and, hence, $\qc=0$ and $\phs=0$.
The parameters of helper $D$'s cache and the wireless links can be found in Tables \ref{table:caches_params} and \ref{table:links_params}, respectively.

\begin{figure}[htbp]
	\centering
	\subfloat[$\delta = 0.5$]{
	\includegraphics[width=1.05\linewidth]{Fig9A_StableThroughputVsLambdaMS0_d05}}
	\hfill
	\subfloat[$\delta = 1.2$]{
	\includegraphics[width=1.05\linewidth]{Fig9B_StableThroughputVsLambdaMS0_d12}}
	\caption{The \maxThr ~vs. the average arrival rate $\lambda$ for which the queue at $S$ is stable, $M_U =200, M_D = 1000, M_S=0$ and $\alpha=0.7$ using different values of $w$ for: (a) $ \delta = 0.5$ and (b) $  \delta = 1.2 $.}
	\label{Fig:stableThr_vs_lambdaMS_0}
\end{figure}

In \figurename~\ref{Fig:stableThr_vs_lambdaMS_0}, we plot the \maxThr~versus $\lambda$ for which the queue at $S$ is stable when $M_S=0$ for different values of $w$.
Regarding $\delta = 0.5$, when $w=1/4$, the \maxThr~is decreasing with $\lambda$.
When $w \in \{2/4, 3/4 \} $, and $\delta \in \{0.5, 1.2\}$ or $w=1/4$ and $\delta=1.2$, the \maxThr~is an increasing function of $\lambda$.

\begin{table}[ht]	\caption{The values of $q^*_S$ and $ q^*_D$ for which the weighted sum throughput is maximized when the queue at $S$ is stable, $\alpha = 0.7, M_U = 200, M_D=1000,$ and $M_S = 0$.}
	\def1.2{1.1
	\begin{center}
		\begin{tabular}{ | c || c | c | c || c | c | c |} \hline
			$M_S =0 $&\multicolumn{3}{c||}{$\delta = 0.5$} & \multicolumn{3}{c|}{$\delta = 1.2$}  \\ \hline
			\hline w & max.$T_w$  &  $q^*_S$ & $q^*_D$ & max.$T_w$  &  $q^*_S$ & $q^*_D$ \\ \hline
			$ 1/4 $  & $ 0.383$  & $ 0.993 $ 	& $1$ & $ 0.189 $  & $ 1 $ & $1$ \\ \hline
			$ 2/4 $ & $ 0.296$  &        $ 1  $	   & $1$ & $ 0.362 $  & $ 1 $ & $1$  \\ \hline
			$ 3/4 $ & $ 0.498$  & 		 $ 1  $    & $1$ & $ 0.536 $  & $ 1 $ & $1$ \\ \hline
		\end{tabular}
	\end{center}
	\label{table:stableThr_vs_lambdaMS_0}
\end{table}

In Table \ref{table:stableThr_vs_lambdaMS_0}, we present the values of $q^*_S$ and $q^*_D$ that achieve \maxThr~when the queue at $S$ is stable for different values of $w$. Recall that, in this specific scenario, $q^*_C=0$ since helper $S$ has no cache and, thus, it cannot assist $U$. Therefore, $S$ is only useful to helper $D$.
We observe that the \maxThr~is lowered compared to the case when $M_D=0$ for $\delta = 0.5$ and slightly higher for $\delta=1.2$ (compare with Table \ref{table:stableThr_vs_lambda_MD0}). Additionally, helper $S$ should almost always serve  $D$ and the later should always assist user $U$ to achieve the \maxThr.
\begin{table}[ht] 	\caption{The values of $q^*_S$ and $q^*_D$ for which the weighted sum throughput is maximized when the queue at $S$ is unstable, $\alpha = 0.7, M_U = 200, M_D = 1000,$ and $M_S = 0$.}
	\def1.2{1.1}
	\begin{center}
		\begin{tabular}{ | c || c | c | c || c | c | c |  } \hline
			$M_S =0 $&\multicolumn{3}{c||}{$\delta = 0.5$} & \multicolumn{3}{c|}{$\delta = 1.2$}  \\ \hline
			\hline w  & max.$T'_w$ &  $q^*_S$ & $q^*_D$ & max.$T'_w$ &  $q^*_S$ & $q^*_D$ \\ \hline
			$ 1/4 $   & $ 0.387 $    & $0$ 		  &	$1$  		& $ 0.189$     & $1$ 		 & $1$  \\ \hline
			$ 2/4 $  & $ 0.286$ 	& $1$ 		  & $1$    		& $ 0.363$    & $1$ 		& $1$ \\ \hline
			$ 3/4 $  & $ 0.399$ 	& $1$ 		  & $1$ 	   & $ 0.537$  	  & $1$ 		& $1$ \\ \hline
	\end{tabular}
	\end{center}
	\label{table:unstableThr_vs_lambdaMS_0}
\end{table}
In Table \ref{table:unstableThr_vs_lambdaMS_0}, we present the values of $q^*_S$ and $q^*_D$ that achieve \maxThr~when the queue at $S$ is unstable for different values of $w$.
In order to maximize the weighted sum throughput, helper $S$ should always serve $D$ for any values of $\delta$ and $w$ apart from the case in which $w=1/4$ and $\delta =0.5$ for which $S$ should remain silent since $q^*_S=0$.
Furthermore, helper $D$ should always assist $U$ requests for every value of $w$ and $\delta$ we used.
The \maxThr~is higher compared to the case in which $M_D = 0$ (compare with Table \ref{table:unstableThr_vs_lambda_MD0}) for every value of $w$ and $\delta$ apart from the cases in which $\delta=0.5$ and $w \in \{ 1/4, 2/4 \}$.

\section{Introduction}
\input{Introduction.tex}
\section{System Model}\label{Sec:SystemModel}
\input{SystemModel.tex}
\section{Throughput Analysis} \label{Sec:Throughput}
\input{Throughput.tex}
\section{Delay Analysis}\label{Sec:Delay}
\input{Delay.tex}
\input{DelaySolutions.tex}
\section{Numerical Results}\label{Sec:Results}
\input{results.tex}
\input{resultsThroughputA.tex}
\input{resultsThroughputB.tex}
\input{resultsThroughputC.tex}
\input{resultsThroughputD.tex}
\input{resultsDelayA.tex}
\section{Conclusion}\label{Sec:Conclusion}
\input{conclusion.tex}
\bibliographystyle{IEEEtran}

\subsection{Network Model}

\begin{figure}[t!]
	\centering
	\includegraphics[width=1\linewidth]{System.pdf}
\caption{The wireless network we analyze in our system model: user $U$ requests cachable content from the data center $DC$ which can be directly accessed through the base station $BS$. The pair $S$-$D$ exchanges non-cachable content and falls within $U$'s proximity. Packets randomly arrive with average arrival rate $\lambda$ to the queue of node $S$ who serves node $D$ with non-cachable traffic. Both $S$ and $D$ have their own caches to serve user $U$'s requests for cachable content.
It is preferable to $U$ when it is served by $D$ or $S$, in case of $D$'s cache miss or failure, since the link between $BS$ and $U$ can be problematic.}
\label{figure:SystemModel}
\end{figure}

We consider a network system with four wireless nodes: a pair of caching helpers $S$ and $D$, a random user $U$ within the coverage of the helpers and a base station ($BS$) node connected to a datacenter ($DC$) through a backhaul link as depicted in Fig. \ref{figure:SystemModel}. We consider slotted time and that a packet transmission takes one time slot.

Helper $S$ is equipped with an infinite queue and the packet arrivals in its queue follow a Bernoulli process with average arrival rate $\lambda$. It transmits packets to the destination helper $D$.
In each time slot, user $U$ requests for a file in its own cache. In case $U$'s cache miss, which happens with probability $\qu$, it requests the file from external resources i.e., the caching helpers or the data center (through $BS$). The data center stores the whole library and, hence, every file that $U$ may request.

Requesting a file directly from the $BS$ is not necessarily the best policy since there might be congestion at the link connecting $BS$ and $U$. Consequently, limited throughput or increased delay might be experienced using this link instead of requesting the file from one of the caching helpers. Moreover, the $BS$ is not always available to help $U$; this happens with probability $\alpha$ in each time slot. Therefore, it is preferable to $U$ when it is served by the caching helpers.

The flowchart of user $U$'s operation with respect to its request content search is shown in Fig. \ref{Fig:operationU}.
The operation of caching helpers $S$ and $D$, represented as flowcharts, are depicted in Figs. \ref{Fig:operationS} and \ref{Fig:operationD}, respectively.

\subsection{Cache Placement and Access}\label{Sec:CachePolicy}
We assume the content placement is given and hierarchical i.e., when the user node requests for a file that is not stored in its most popular files, it first probes the closest caching helper which stores the next most popular files. If this probe fails, then the second caching helper is probed for the requested file. If it also cache misses, then the file can be found in the data center.
Additionally, the source helper is equipped with a queue whose role is to save the excessive non-cachable traffic with the intention of transmitting it to the destination helper in a subsequent time slot.

Furthermore, the user device $U$ and the caching helpers $D$ and $S$ have cache capacity to $M_U$, $M_D$, and $M_S$ files, respectively, and $M_U  \leq M_D \leq M_S$ holds.
We also consider the Collaborative Most Popular Content (CMPC) policy. According to CMPC, user $U$ stores the first $M_U$ most popular files in its own cache, helper $D$ stores the next most $M_D$ popular files, and $S$ stores the next most $M_S$ popular files.
Following CMPC requires exchange of information among devices e.g., the cache size of each device and the content placement in each device. We assume that this information exchange is negligible.

\begin{figure*}
	\centering
	\includegraphics[width=0.9\linewidth]{Flowchart-Operation-U.pdf}
	\caption{Operation of $U$ in the described protocol.}
	\label{Fig:operationU}
\end{figure*}

\begin{figure*}
	\centering
	\includegraphics[width=0.9\linewidth]{Flowchart-Operation-S.pdf}
	\caption{Operation of $S$ when user $U$ requests a file $f$ from external resources.}
	\label{Fig:operationS}
\end{figure*}

\begin{figure*}
	\centering
	\includegraphics[width=0.9\linewidth]{Flowchart-Operation-D.pdf}
	\caption{Operation of $D$ when user $U$ requests a file $f$ from external resources.}
	\label{Fig:operationD}
\end{figure*}

\subsection{Transmission Model}
In each time slot, $S$ will attempt transmissions of non-cachable content to $D$ with probability $\qs$ (if its' queue is not empty), and is available for $U$ with \pro $1-\qs$.
We assume that the caching helpers assist $U$ only when specific conditions apply: $D$ will attempt transmission to $U$ with probability $\qd$, and $S$ will help $U$ only when it is not transmitting to $D$.
When the source caching helper $S$ is transmitting to helper $D$ and the user $U$ requests a file from external resources, then $U$ can be served by $D$ or by $DC$.
In that case, there are two parallel transmissions one from $S$ to $D$, and one from $D$ (or $DC$ respectively) to $U$.
If the caching helper $S$ is available for $U$, then there is no parallel transmissions since only one of $S$, $D$, or $DC$ can help $U$ at the same time slot.

Regarding $DC$, we model its availability with a \pro $\alpha$ to model the fact that it is not always available to serve $U$ due to serving other users or failure.
If the $DC$ is always available to $U$, then $\alpha = 1$. On the other hand, if the $DC$ is not available for $U$, then $\alpha = 0$.
\begin{table}[t!] \caption{Notation table}
	\def1.2{1.2
	\begin{center}
		\begin{tabular}{ | c  l |}
			\hline
			\textbf{Notation} & \textbf{Event} \\ \hline
			$\qu$   & $U$ requests file from external resources \\
			& ($U$ cache miss) \\
			$\phd$ & cache hit at $D$ (CMPC policy) \\
			$\phs$ & cache hit at $S$  (CMPC policy) \\
			$\qs$  & $S$ attempts to transmit to $D$ if $ Q \neq 0 $ \\
			& 	(or $S$ is available to serve $D$) \\
			$\qc$  & $S$ attempts to transmit  to $U$ \\
			$\qd$  & $D$ attempts to transmit  to $U$ \\
			$\A$ & the $DC$ is available to serve $U$ \\
			$\PSNR{i}{j} $ & success probability of link $i \rightarrow j$, \\
			& when $i$ is transmitting \\
			$\PSINR{i}{j}{k} $ & success probability of link $i \rightarrow j$,  \\
			& when $i$ and $k$ are transmitting \\
			$\prob( i \Rightarrow j) $& queued node $i$ attempts to transmit to $j$ \\
			\hline
		\end{tabular}
	\end{center}
	\label{table:probabilities}
\end{table}
We summarize the aforementioned events and notation in Table \ref{table:probabilities}.
Additionally, the operation of $U$, $S$, and $D$ as flowcharts can be found in Figs. \ref{Fig:operationU}-\ref{Fig:operationD}.

\subsection{Physical Layer Model}
We assume Rayleigh fading for the wireless channel and that a packet transmitted by node $i$ is successfully received by node $j$ if and only if $SINR(i,j) \geq \gamma_j$, where $\gamma_j$ is a threshold characteristic of node $j$.
Therefore, the received power at node $j$ when $i$ transmits is $P_{rx}(i,j) = A(i,j)h(i,j)$, where $A(i,j)$ is exponentially distributed and the received power factor is:
\[ h(i,j) = \frac{P_{tx}(i)}{r(i,j)^{p}},\]
where $P_{tx}(i)$ is the transmit power of node $i$, $r(i,j)$ is the distance in $m$ between node $i$ and node $j$, and $p \in [2,6]$ is the path-loss exponent.

By assuming perfect self-interference cancellation, the success probability of link $i \rightarrow j$, with $\mathcal{T}$ denoting the set of transmitting nodes, is given by \cite{Pappas_FD_TWC}:
\begin{align*}
\scriptsize
\PSINR{i}{j}{\mathcal{T}} &= exp\Big(-\frac{\gamma_j n_j}{v(i,j)h(i,j)}\Big)
\times \\
& \times
\prod_{k \in \mathcal{T} \setminus \{ i,j \}} \Big( 1 + \frac{\gamma_{j} v(k,j)h(k,j)}{v(i,j)h(i,j)} \Big)^{-1},
\end{align*}

where  $v(i,j)$ is the parameter of the Rayleigh fading r.v., and $n_j$ is the noise power at receiver $j$.
To simplify notation, we use $\PSNR{i}{j}$ to denote the success \pro of link $i \rightarrow j$ if node $i$ is the only transmitting node.

redpajama_set_name: RedPajamaArXiv


Marina Park and Aquarium is a prime attraction in Andaman and Nicobar Islands. It is located in the unspoiled seashore close proximity to Water Sports Complex. The local inhabitants and the visiting tourists use this park for spending their valuable evenings by enjoying the nature's gifts of pristine beauty, stunning sea waves and lush green vegetations.
The Aquarium is praise worthy place enclosing many marine lives including various kinds of sea fishes and turtles. A children's park with many facilities for kids enjoyment, a toy train and many other amusement facilities make this park a much sought picnic spot.


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When fellow believers in Sweden first began coming to him with information from the Internet that contradicted the church's history and teachings, he dismissed it as "anti-Mormon propaganda," the whisperings of Lucifer. He asked his superiors for help in responding to the members' doubts, and when they seemed to only sidestep the questions, [Hans] Mattsson began his own investigation.
Goodstein's story is interesting because it manages to evoke plenty of sympathy for Mattsson—"I felt like I had an earthquake under my feet. Everything I'd been taught, everything I'd been proud to preach about and witness about just crumbled under my feet. It was such a terrible psychological and nearly physical disturbance"--while simultaneously raising the question of what took him so long to do some simple research. "I was just in a bubble, and we felt so happy," is pretty much his explanation.
Every faith has its skeptics and detractors, but the Mormon Church's history creates special challenges. The church was born in America only 183 years ago, and its founder and prophet, Joseph Smith, and his disciples left behind reams of papers that still exist, documenting their work, exposing their warts and sometimes contradicting one another.
"The Roman Catholic Church has had 2,000 years to work through the hiccups in its history," said Terryl L. Givens, a professor of English, literature and religion at the University of Richmond and a Mormon believer. "Mormonism is still an adolescent religion."
1. Why does the church always portray Joseph Smith translating the Book of Mormon from golden plates, when witnesses described him looking down into a hat at a "peep stone," a rock that he believed helped him find buried treasure?
2. Why were black men excluded from the priesthood from the mid-1800s until 1978?
3. Why did Smith claim that the Book of Abraham, a core scripture, was a translation of ancient writings from the Hebrew patriarch Abraham, when Egyptologists now identify the papyrus that Smith used in the translation as a common funerary scroll that has nothing to do with Abraham?
4. Is it true that Smith took dozens of wives, some as young as 14 and some already wed to other Mormon leaders, to the great pain of his first wife, Emma?
Pop quiz: which of these doesn't quite fit with the other three? Hint: it isn't #4, which Mattsson deems "shocking." It's #2. You'll notice that the other three questions are about the truth of the Mormon story. Question 2 is about disgusting bigotry that lasted until the late 1970s! Mattsson doesn't ask any more about question 2, content as he is to concern himself with whether he was given a false version of various narratives. "I don't want to hurt the church," he eventually says. "I just want the truth." One might cynically observe that even though the Mormon story has been called into question, larger concerns about the truth of the religion don't seem to bother Mattsson.
Still, by exhibiting some doubt about narratives, Mormons may be more likely to allow themselves to ask broader questions. Small heresies can lead to larger ones, as someone once said. Generally, I would guess, it works the other way around: people don't like the Catholic Church's position on, say, abortion, so they begin questioning their role in the Church, and eventually call into question the history they have been told. This story is interesting because it shows that confusion over a faith's narrative can elicit questions about the faith's role in the world.


text:
5 Nigerian musicians that rocked the 70's
Posted on July 6, 2020 July 6, 2020 by
Before the arrival of music talents like Wizkid, Davido and 2baba, there were other notable musicians that rocked the musical scene years before and after Nigeria's colonial emancipation from Britain. Amazingly, the songs they dished out are rendered evergreen as they continue to resonate in the minds of many people.
Listed below are 5 of this musicians who made their mark in the sixties and even up till this day.
Stephen Osita Osadebe
Chief Stephen Osita Osadebe, often referred to as just Osadebe, a Nigerian highlife musician from Atani. His career spanned over 40 years, and he is one of the best known Igbo highlife musicians and king of highlife music. His best-known hit was the 1984 single "Osondi Owendi", which established him as a leader in the highlife genre and was one of Nigeria's most popular records ever.
Prof. Sir Victor Uwaifo
Prof. Sir Victor Uwaifo is a Nigerian musician, writer, sculptor, and musical instrument inventor. He also served as commissioner for arts and culture in Edo State under the government of Lucky Igbinedion. Uwaifo is famous for his joromi music.
MOST READ: Is Duncan Mighty going off the radar again
Christy Uduak Essien-Igbokwe
Christy Uduak Essien-Igbokwe was a Nigerian musician and actress. Called "Nigeria's Lady of Songs", she was known for her song "Seun Rere". Essien was the first female president of the Performing Musicians Association of Nigeria (PMAN), and the chairwoman and managing director of Soul Train Entertainment.
She sang in Igbo, Ibibio, Efik, Hausa, Yoruba and English. Essien's fluency in Igbo, Hausa, Yoruba, English and her native Ibibio gave her an appeal which crossed tribal lines.
King Sunny Adeniyi (KSA)
King Sunny Adé is one of Nigeria's most famous musicians. He became the worldwide icon of juju, the Yoruba praise music driven by the rhythm of the talking drum accompanied by Hawaiian-style guitar. After gaining local popularity during the '70s oil boom, when the Lagos elite invited Sunny Adé and His African Beats to spice up their parties, he started touring internationally in the 1980s, having been called "The New Bob Marley" by his agents. He was nominated for a Grammy twice, but never broke through on the world stage. Among world-music lovers, however, his fame is substantial.
MOST READ: 5 Reasons Why We Love The New Star Burna Boy!
Fela Kuti (Olufela Olusegun Oludotun Ransome Kuti)
Fela was a Nigerian multi-instrumentalist, musician, composer, pioneer of the Afrobeat music genre and human rights activist. At the height of his popularity, he was referred to as one of Africa's most "challenging and charismatic music performers".
His struggles with the government distinguished him from other Nigerian artistes as he was in and out of prison due to his unconventional and uncompromising lifestyle. Interestingly, Fela did not call people to flood the street of Lagos to protest rather, he condemned the government with his hard hitting music whose lyrics appalled state authorities.
As his Afrobeat music was being suppressed in Nigeria, he gained international publicity while Nigerians continued to appreciate his music. The height of his musical craze took the life of his mother but this did not deter the carefree Fela.
Today, his music lives on as many themes he sang about are becoming reality in Nigeria as a result he his tagged a prophet by some Nigerians. Some of his songs include Zombie, Expensive shit, Water no get enemy and Sorrow, Tears and Blood amongst others. Fela is indeed an Afrobeat legend.
MOST READ: 4 times Davido has called out people on a song
Who Redefined Afrobeat ??? 5 Nigerian musicians who were born into rich families Is Nas a Nigerian ? 5 Nigerian artists we can't wait to see on the movie scene regularly List of Nigerian artist born in January "Something Different" Versus "Nobody"… Which of the song will last through your playlist"
Posted in EditorialTagged Christy Uduak Essien-Igbokwe, Fela Kuti (Olufela Olusegun Oludotun Ransome Kuti), King Sunny Adeniyi (KSA), Prof. Sir Victor Uwaifo, Stephen Osita Osadebe


Fontana was Elvis' last remaining band member alive and died peacefully in his sleep.
MEMPHIS, Tenn. (AP) - D.J. Fontana, the drummer who helped launch rock 'n' roll as Elvis Presley's sideman, has died at 87, his wife said Thursday.
Fontana rose from strip joints in his native Shreveport, Louisiana to the heights of musical history as Presley's first and longtime drummer.
He played on many of the soundtracks - and was occasionally seen on camera - for Presley's movies in the '50s and '60s.
"And he told me one day, he said 'You know, I wish I wasn't Elvis.' And that struck me funny - even back then. You know he kinda wanted to get away for a while. I think he should have retired for about 7 or 8 years, and then come back - you know. And then he might still be with us."


Whenever I park my bicycle, I'm careful to lock it securely to a bike rack or lamp post. I use a Kryptonite shackle lock and always make sure I run it through my bike frame and around secure anchors. I do all this with the hope that my bike will be right where I left it when I get back.
Why did I pick a Kryptonite lock? I asked the guy at the bike store what he recommended, I read some online reviews, and I paid attention to what riders with expensive bicycles were using for their own security. Plus, I was aware of the Kryptonite brand and believed that they build a good strong lock.
But walking along Santa Monica Boulevard one afternoon, I came across this lock laying on the ground.
There were no bike or bike parts laying around, just the carcass of a lock that had been cut open. Imagine how an image like this can shatter (no pun intended) all the hard work that a company does to build its brand. A picture is indeed worth a thousand words.
Of course, I never thought a Kryptonite lock – or any lock for that matter – was invulnerable nor invincible. Truth is, using a good bike lock doesn't mean a crook can't steal your bike. Using a good lock just makes it easier for the crook to steal someone else's bike.
But seeing that worthless lock completely eroded my faith in the brand.
Chipotle's second quarter same-store sales did rise 3.3%, even more than Wall Street projected. Still, these mind pictures continue to make it hard for Chipotle to overcome their brand perception issues.
It seems that some problems and crises hurt brand value more than others, despite the size or ramifications of the actual issues. Chipotle suffered possible outbreaks in very few of their 2000 stores, but the image of sick customers vomiting up their lunches paints a very provocative image. So does a broken lock.
Volkswagen, on the other hand, was convicted of falsifying their diesel exhaust numbers around the world. Yet "Dieselgate's" ramifications are very hard to picture. Fortune Magazine estimates that "Volkswagen's deception — which resulted in emissions 30 to 40 times allowable levels when driving — could exceed $100 million in economic costs from health damages.
It is estimated that VW's financial toll was between 18 and 25 billion dollars. And a few of their executives have been given jail sentences. Still, while many consumer lawsuits have been enacted, Volkswagen appears to be winning most of them. Even when judges ruled that VW used an illegal device, most courts have concluded that consumers suffered no compensable injury.
Is that because VW has been doing a better job of cleaning up both the environment and their brand? Sadly, no.
Instead, it's because the damage they've done to consumers is much less visually and emotionally provocative and identifiable than the problems Chipotle caused. And eventual health damage is also harder to illustrate than the shocking image of a broken lock lying next to a bike rack.
Because if a picture is indeed worth a thousand words then what is the lack of a picture worth?


Blessed Trinity Catholic School admits students of any race, color, and national, or ethnic origin to all rights, privileges, programs, and activities generally made available to students at the school. Blessed Trinity School does not discriminate on the basis of race, color, national or ethnic origin in the administration of its educational policies, admission policies, or athletic and other school administered policies. The school does not discriminate on the basis of disability, if with reasonable accommodations, the student can meet the academic and behavioral requirements of the school.
Students who are new to our school will be on probation for the first quarter. At the close of the period, they will be evaluated for progress, both academically and behaviorally, to ensure that they are able to progress in our program.
All new students will be screened upon applying for admission. A testing fee will apply.
NOTE: Failure to provide the correct vaccination information will exclude the student from classes until the original forms are brought into the office.


Mini maple pancake muffins from Bakerella. JOY.
You're mini. You're maple pancakes. You're chocolate chips. You're muffins. Really, what else is there in life!?
PS: Find the Bakerella recipe here. You won't want to share. I didn't.
You're perfect for a warm, rainy day of exploring Kansas.


I have decided that I will attempt to "section hike" all 5 Skåneleden Trails in Sweden. I've already hiked different sections on different trails. I am progressing with SL3 at present time and so far I have completed about 77 km of this trail. In January I got off the trail not far from Frostavallens Hostel so this is where I started. I took a train from Copenhagen Central to Mamö where I took another train to Höör. From here I rode a bus up to the hostel.
It was about 10:30 am on Saturday morning when I started out. There was a lot of snow still here compared to Copenhagen. I wore my inov-8 ROCLITE 282 GTX Gore-Tex Trail shoes and a pair of Darn Tough Boot Socks Full Cushion. These proved to be a perfect choice even with the "slosh ice" snow on the trail. Gore-tex will after sometime allow moisture to penetrate the material, but my wool blend socks kept my feet nice and toasty. Later that evening when I changed into my Smartwool thick hiking socks for sleeping in, I realised that my Darn Tough socks, that I had hiked in all day, were slightly wet although I could not feel that they were. Just love these socks!
It didn't take me long to hike the last bit of section 7 and reach the Maglebjär shelter. I'm glad that I always have my trekking poles with me; it made this trip especially a lot easier.
I could see that there was 2 different parties ahead of me on this section of the trail. There was a group of about 3 and one person with a dog. When I reached Onsvalakällan I met up with the group of 3 who had occupied the shelter there. They were 3 young Danish guys. I wished them a good weekend and continued. I'm not good at taking breaks or stopping for lunch. I normally walk all day just snacking on food as I go. This seems to work for me; maybe I'll stop for breaks in the warmer months next to some nice stream or a place with a view.
After that I didn't see anybody the rest of the weekend. Although I could still see the person with the dog tracks in the snow until they disappeared at some time on Sunday. I continued through the beech woodland and past this beautiful lake area.
I had planned to tent at the Bjeveröd shelter area for the night, but it didn't appeal to me and it was also too early at around 3:30 pm. So with nearly 2 hours of daylight left, I continued on.
I eventually found a spot in a forrest not too far from the trail. It was about 16:40 and I had about 45 minutes before the sun went down. It didn't take me long to get settled and have dinner. It started to rain and so I settled in for the night.
I was looking forward to listening to the latest of Bob's podcasts from the Outdoors Station. This time it was about Stormin Stoves. I was quite tickled to be laying there out in the middle of nowhere and I received a mention on the podcast. I had left a question to ask Stormin Stoves on the Outdoors Station Facebook page.
I took my time getting out of bed on Sunday morning. There was a constant rain so I decided to have breakfast and coffee in bed today. Quite easy to do in my Zpacks Duplex tent; in fact I had the door open all night even with the rain coming down. I had no rain enter the cabin.
I guess it was about 9:15 am before I continued on my way backpacking the last of section 10 for this hike. It rained for a further 3 hours so I used my EuroSchirm Swing liteflex trekking umbrella. I still haven't found a fastening system for fixing it to my backpack that I feel comfortable with yet, so I stored one of my trekking poles in my pack.
After passing the shelter area at Ekeröd I continued on for a short road walk until I reached the bus stop with about 20 minutes to spare. The Malmö Express stops here 10 minutes in the hour several times a day. From Malmö I took the train back to Copenhagen Central station and you guessed it, I had to buy a 🍔 before catching my awaiting train home.
You've been hiking quite a few parts of Skåneleden. Is there a section or loop-trail that's perticullary beautiful that you could recommend? I'm planning the hikes for the spring. I'll probably try to keep most of the hikes within two hours drive from Växjö so I thought I might try parts of Skåneleden.
Hi Axel, good to hear from you. I will be finished with SL3 soon and then I'll be moving on to Österlenleden (SL4). Hopefully I'll be able to have my 16 year old son with me. You definitely need to see Söderåsens Nationalpark. It depends on if you leave the car at home or not (which is what I do a lot of the time) and take the train/bus to Klippan. Then do the boring 6 km road walk up to Klövahallar (there is always water there from May) and hike SL3 sections last bit of 2, 3 and 4 to Söderåsens nationalpark entrance where you can catch a bus to a train station again.
Axel, a good loop trail is the The Österlen circle. I'm planning on doing it when I've completed SL3. It's on the southern end of SL3 and the northern end of SL4. I know a couple of people that have done it. There has been some problems with the water pumps but I believe that you can get some water at the church at Hörröd. At Hallamölla you can see the highest waterfall in Skåne which is 23 metres high. You can park your car near Brösarp.
Thanks for the tip of the Österlen circle. It looks like a great trail, and 34 km is a good distance for a weekend hike. I'll hike there as soon as the spring comes.


To say that Brooklyn's Gowanus Canal "rolls through Red Hook, Carroll Gardens and Park Slope" is inaccurate. It doesn't even loll, really. The Gowanus Canal just sort of sprawls, slack and flat and brown and grease-slicked and awash in pesticides, PCBs and metals, across 1.8 miles in South Brooklyn. It is one of the most polluted waterways in the United States, which is a fact that essentially no one disputes. So in one sense, it's not a surprise to read that the Gowanus Canal was tagged a Superfund site by the EPA on Tuesday. If there has ever been a more deserving Superfund site it… well, it would've had to have been pretty freaking nightmarish. But while the Gowanus is certainly apocalyptic enough in its noxiousness to deserve the Superfund label, there were some in New York City — including Mayor Bloomberg and several big real estate developers — who fought against that designation. Some of this is easily explained — the Toll Brothers at one point planned a 600,000-square-foot mixed-use development (with a freaking esplanade) along the Gowanus; Michael Bloomberg has almost certainly never been to Brooklyn — but what's the debate, here?
It's not quite as one-sided as it might seem. Yes, to a certain degree this comes down to Mega-Developers and Bloomberg looking to find a faster, less-binding way around the very obvious need to clean up the Gowanus before the frontier-y neighborhood around it can be developed. In the New York Times, Mireya Navarro notes that the city had already committed to $175 million in "upgrades of its sewer system to reduce overflows into the canal," which is nice, and was working with the Army Corps of Engineers in formulating a cleanup strategy. The Superfund-grade cleanup, which is expected to come to about $300 million and take over a decade — five years to formulate a plan, five years to do the dredging and such — will enfold those city efforts. It will, however, lose what EPA administrator Judith Enck termed "financial uncertainty" of the city's plan, which involved polluters "voluntarily" paying for the cleanup and copious government funds. Given that New York City itself is among the polluters named as responsible for the state of the Gowanus in the first place — the U.S. Navy and Con Ed are among the others — it does seem like something of a conflict.
No, it doesn't. A cleaner Gowanus canal is difficult to imagine, naturally, but good things are already happening there, and the prospect of it remaining one of Brooklyn's last under-overdeveloped frontiers — or at least waiting a decade or so for those esplanade-enhanced luxury condos — doesn't really seem like anything to mourn. Unless the Superfund cleanup impacts the Gowanus Yacht Club in any way. Then I'll see you on the barricades.


The University of Texas Health Science Ctr.
North Hills Medical Research, Inc.
A Multicenter, Double-Blind, Active-Controlled, Two-Arm Parallel Group study of the Efficacy of Oral HMR3647 800mb once a day for 5 days versus Oral Clarithromycin 250mg twice a day for 10 days in subjects Group AB Hemolytic Streptococcal Pharyngitis/Tonsillitis.
(1999) – G.D. Searle & Co.
Dr. Mark A. Rodgers, M.D.
Wife (Anastacia) and 3 children and 4 grandchildren and one in the oven due in Aug.2015.
Tori Ledesma is a Board Certified Family Nurse Practitioner. She completed her Master's degree in Nursing at the University of Texas at Arlington (UTA) Graduate School of Nursing in 2008. Tori also holds a certificate as a Nurse Educator. Prior to that, in 2003, she received her license as a Registered Nurse also at UTA. Tori joined Arlington Family Health in June 2011. Tori lives in Mansfield with her husband Dr. Gilbert Ledesma, and her 3 beautiful children. She enjoys spending her free time with her family traveling, seeing movies, reading, and taking care of her horses. We are very excited to have Tori join our practice and she looks forward to meeting you.
Nancy Davis is a Board Certified Adult Clinical Nurse Specialist. She graduated from Texas Christian University in 1987 with her Bachelor of Science in Nursing and with her Masters of Science in Nursing in 2010.
When she is not at work she enjoys traveling, riding her bicycle, doing Jazzercise, hiking and outdoor photography with her husband. She has one daughter who is in college and two spoiled dogs.
We are so happy and fortunate to have Nancy as one of our Nurse Practitioner with AFHP family. Please welcome her and say hello when visiting our office.


Atatürk showed himself to be an iconoclast and an innovator in switching his capital from Istanbul to Ankara. In this, as in much else, Mustafa Kemal's bold action was eventually emulated, by Australia, Brazil and Pakistan, among others. A spate of new public buildings resulted – the Grand National Assembly, the ministries, the presidential palace itself – which remain as monuments to late-1920s and 1930s taste. So, too, in their more individualistic ways, do the principal embassy buildings of the period.
In 1919 Mustafa Kemal's Nationalists moved their headquarters to the old farm school in Keçiören, on the outskirts of Ankara – or Angora as it had been known since classical times – from Sivas, a day's journey to the east. Ankara, an important trading post in Hittite times, was then just a small town in Anatolia, 300 miles from Istanbul. Although the Baghdad railway had reached there as early as 1893, it had only intermittent contact with the outside world. Its sense of isolation had been exacerbated by a fire during the First World War which had destroyed swathes of the old lath-and-plaster houses that lined the steep cobbled streets huddled about the citadel.
Colourful mountains of melons are a common sight at weekly street markets. Connoisseurs examine them, sniff them, weigh them in their hands. The stalk should have dried slightly, the bottom yield gently if pressed and the fruit should feel heavy and full.
In Turkey the tireless George Maw was able to indulge in both his loves. He found inspiration for the decorative tiles made by his family pottery. And he discovered the plants that inspired his magnificent book on crocuses.