Trending February 2024 # Arbico Family I3 4185 Review # Suggested March 2024 # Top 2 Popular

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Our Verdict

The Arbico Family i3 4185 doesn’t go overboard with processor or graphics performance, but offers a complete system with a balanced set of features at a modest price. While not fast in benchmark results, it’s certainly fast enough for day-to-day use and even for a bit of gaming. An ideal family PC.

At £675, Arbico’s Family i3 4185 desktop PC looks like a reasonably-priced option, offering a significant saving over many of the family PCs we’ve reviewed of late.

The Arbico Family i3 4185 features a modest 3.4GHz Core i3-4130 dual-core processor, teamed up with 8 GB of memory, a 120 GB SSD and 1TB hard disk. It’s not the fastest PC you can buy, but it’s all you need in a computer intended for family use. See all PC reviews. 

Performance from the Arbico Family i3 4185 is as you might expect – perfectly adequate, but noticeably lower in benchmark tests than many rival family PCs. Keep an eye out in the next few days for our Wired2Fire Diablo Reactor family PC review – this machine undercuts the Arbico on price while delivering far greater performance. It’s performance you may not need, but if you don’t have to pay any extra to get it you may as well take it. 

An MSI-branded AMD Radeon R7 250 is fitted inside the Arbico Family i3 4185, which gives the PC around double the gaming performance you would get from integrated graphics alone and opens up more gaming possibilities. However, it’s still a reasonably weak graphics card, so if gaming is something that matters to you, this family PC will not be your best choice. 

One area where the Arbico Family i3 4185 really puts in a decent performance is storage: a 120GB SSD is fitted inside this family PC, alongside a 1TB hard drive. 

The Arbico’s CIT Templar system case is one of the more compact we’ve seen, but it also feels rather cheap. It lacks any front-facing USB 3.0 ports, so you’ll have to rummage around at the Arbico Family i3 4185’s rear to make the most of up-to-date peripherals and flash drives. 

A smart, but basic 23.6in AOC monitor is supplied, which keeps down the cost, but this panel can’t match the quality of an IPS display. You also get a DVD writer with Nero and CyberLink Power DVD software thrown in, both of which are useful additions to the Arbico Family i3 4185. 

Logitech’s wired MK120 keyboard and mouse combo is included. This saves a few pounds over the wireless version, and saves you money on batteries, plus it prevents your peripherals from going missing as they remain conveniently tied to the Arbico Family i3 4185 PC at all times.

Specs Arbico Family i3 4185: Specs

3.4GHz Intel Core i3-4130

Arctic Cooling Freezer Pro 7 CPU cooler

Windows 8.1

8GB 1600MHz DDR3 RAM



650W Arctic Red PSU

MSI B85M-E45 motherboard

2x USB 3.0

6x USB 2.0

23.6in AOC E2470SWHE (1920×1080) TN display

MSI AMD Radeon R7 250 graphics with 2GB VRAM

onboard sound

gigabit ethernet

wireless connectivity optional




CIT Templar case

Logitech MK120 peripherals

Samsung DVD+/-RW drive


CyberLink PowerDVD

2-year return-to-base warranty

PCMark7 overall score: 4800

PCMark 8 Home score: 3777

PCMark 8 Work score: 4669

PCMark 8 Creative score: 4459

PCMark 8 Storage score: 4711

power consumption (idle/load): 44/133W

Alien vs Predator (720p/1080p): 22/11fps

Sniper V2 Elite (Low/High/Ultra): 62/22/5fps

Final Fantasy XIV (720p/1080p): 66/16fps

You're reading Arbico Family I3 4185 Review

2024 Bmw I3 Review

2024 BMW i3 Review – The electric time machine

The 2024 BMW i3 is the DeLorean of our times. No, I’m not talking about gullwing doors (that’s the exclusive province of its bigger brother, the i8), nor am I referring to sketchy Renault mechanicals (you’ll still have to travel to France for that pleasure). What I mean is, the BMW i3’s future-tense design and real-world electric car performance qualify it as a time machine of the first caliber, a glimpse into tomorrow along the lines of what we were once promised in the breathless prose and scintillating images published in the pages of Popular Science magazine.

Doc Brown would be proud – but more to the point, so would Marty McFly, because here, finally, is a dedicated compact EV that dares to wear its stylish 21st-century design on its elegantly-sculpted steel-and-glass sleeve. In other words, it’s a car worth leaving your hoverboard at home for.

That BMW’s i division is far and away the most risk-tolerant arm of the German automaker’s luxury empire is clear after spending any time inside or alongside the 2024 BMW i3. Rarely does such a small car make such a big impression on everyone within a 50-foot radius, as the bubble-topped subcompact’s curvy snout, upright stance, blacked-out hatch door, and blocky side panels give it a profile unlike anything else in the brand’s line-up.

This arresting collection of styling cues continues within the i3’s cabin, which is surprisingly open and airy for a small automobile – a testament to its tall roofline and the decision to use light-colored woods and accentuate the gaps between screens, panels, and dashboard throughout the vehicle. Although the materials used throughout the interior of the BMW i3 walk the line between recycled-chic and upscale niche, it’s their presentation that seals the deal and makes driver and passengers feel as though their are riding along inside something special.

It’s also worth noting that the concept of carting around more than just a pair of people in the BMW i3 isn’t an unrealistic one. Not only does the rear seat offer legitimate comfort for actual adults, but getting back there is made easier by the presence of a pair of rear-hinged half-doors that carve a useful hole into the side of the hatchback when in used. You’ll have to crack open the front doors to release the rear set from their shackles – a minor annoyance, especially in crowded parking lots – but it’s a small price to pay for not having to clamber over a seat when seeking the back bench. Cargo room is also surprisingly generous for a subcompact EV, with just under 37 total cubes available in an easy-to-fill format.

Back-from-the-future design aside, what really sets the BMW i3 apart from even its similarly-sized EV brethren is the decision to outfit the model with a rear-wheel drive layout. Every other battery-powered hatchback rival – the Volkswagen e-Golf, the Ford Focus Electric, the Nissan Leaf, and compliance cars like the Fiat 500 EV and the Chevrolet Spark EV – are front-pullers, which gives the i3 somewhat of an edge when it comes to handling and overall vehicle dynamics.

The electro-kinetic heart of the 2024 BMW i3 is a 125-kW electric motor, which is paired with a 22-kWh battery. This translates into 170 horsepower and 184 lb-ft of torque – numbers that are not all that far off from the E30-generation BMW 325is, which has since been canonized for its fun-to-drive character. The i3 carries over the 325is’ penchant for acceleration, tapping into its instant-on electric torque to sprint to 60-mph in less than seven seconds.

The vehicle also features a very aggressive regenerative braking system, which engages as soon as you lift your right foot. You can even rely on the regen feature to bring the car to a complete stop, and while it takes some getting used to it’s actually fairly effective at topping up the i3’s juice while on the road. Cabled, you’re looking at charge times of about four hours on a 240-volt connection or 30 minutes with a DC Fast Charger (standard household 110-volt current balloons charging times up to 20 hours).

This was in fact the version of the BMW i3 I drove for a week, and I was curious to see what kind of impact the REX had not only on range, but also my enjoyment of the car. You see, the extra weight of the generator and its fuel tank (a tiny 2.4-gallon unit) actually knocks a fair amount of battery-only range out of the equation. I’d also had several i3 owners tell me that the harshness of the gas-powered genny’s operation (it’s a two-cylinder design) sapped a fair amount of pleasure from piloting the BMW while negatively impacting performance in a straight line.

Determined to run the battery down to the point where the REX would kick in, I discovered that careful, but not over-zealous attention being paid to regenerating electricity extended the electric-only range well past the EPA projection around town. I ended up doing 91 miles in total on battery power alone – almost 20 miles more than a REX-equipped car should be capable of – with several 10-mile highway stints thrown in for good measure.

As for NVH, it’s true that the REX generator is perhaps the least-refined gasoline assist you’ll get on the EV market, projecting a buzzy ruckus from the rear of the vehicle that thankfully was easy enough to drown out with either the stereo system or the noise of traffic around me. Still, I never found acceleration to be noticeably blunted by the weight of the REX, as compared to past versions of the i3 I have driven that didn’t feature the system, nor did it push back all that hard in terms of power drop when driving with it on.

With a starting MSRP of $42,400, the 2024 BMW i3 isn’t the most affordable EV on the market, but it’s far from over-priced, particularly once you factor in any state or federal income tax credits that may be available to you for purchasing an electric vehicle. After a week behind the wheel of the i3, it’s easy to see how this fun, and stylish hatchback could find a role in the daily life of city-bound drivers seeking to shirk off their petro-dependency. To REX or not to REX will most likely be a question of one’s individual comfort level with disconnecting from the network of fuel stations that link the country from coast to coast, but either way BMW’s dream factory has engineered a star-making role for the i3 that seems destined for at least one, if not several, sequels.

The Ultimate Swiss Army Knife Of ‘Apply’ Family In R


Data manipulation is one of the most crucial steps one has to perform in the machine learning lifecycle

Let’s learn to use  the most widely-used set of apply functions for transforming data in R


Data manipulation is one of the most crucial steps one has to perform in the machine learning lifecycle. It entails transforming the provided data so that it can be used for building predictive models.

Additionally, it is here that a skilled data scientist applies their intuition and experience to extract as much information from the data as possible. Thus, it should come as no surprise that there exists a plethora of functions and tools in both Python and R to help us in this task.

Today, we will be using R and learning about the most widely-used set of ‘apply‘ functions for transforming data in R. This family of functions offers efficient and quick operations on data. This is particularly useful when we want to work only with certain columns. This set of functions is called the apply() functions. Along with its variants like sapply(), mapply(), etc., we are provided a multipurpose swiss-army knife for data manipulation.

If you are interested in having a career in Data Science and learning about these amazing things, I recommend you check out our Certified AI & ML BlackBelt Accelerate Program.

Table of Contents

The various set of functions in this family are:

Setting the Context







Setting the Context

I will first cover how each function above works by using simple datasets and then we will take up a real dataset to use these functions.

So fire up your Notebooks or R studio, and let us get started!

We don’t need to install any other libraries to use the apply functions. So let us start by creating a simple matrix of numerical values from 1 to 20 distributed among 5 rows and 4 columns:

data <- matrix(c(1:20), nrow = 5 , ncol = 4) data

This is how our matrix looks. Now, let us start with the apply() function


The general syntax of the apply() function can be obtained using the help section. Just execute this code to get a detailed documentation


As we can see, the apply function has the structure of apply(X, MARGIN, FUN, …). Here,

X refers to the dataset(in our case the matrix) we will be applying the operations on

MARGIN parameters allow us to specify if we want to apply the operation by the row or by the column.

MARGIN = 1 for rows

MARGIN = 2 for columns

FUN refers to any user-defined or in-built function we want to ‘apply’ on X

Let us look at the simple example of calculating the mean of each row:

mean_rows <- apply(data, 1, mean) mean_rows

That was fairly simple! We can see how the apply() function can be used to summarise our data. In the same vein, Let us try finding e sum along each column:

sum_cols <- apply(data, 2, sum) sum_cols

If we want to apply the function on all the elements, we just write the apply function like this:

all_sqrt <- apply(data, 1:2, sqrt) all_sqrt

What if we want to apply a user-defined function to the data? For example, I have a function that finds the square root of(x – 1) for each row:

fn = function(x) { return(sqrt(x - 1)) }

We then apply this function along each row:

apply(data, 1, fn)

So far, we have used functions that take only 1 parameter and applied them to the data. The best part about the apply family is that they work with functions having multiple arguments as well! Let us apply a user-defined function that takes 3 arguments:

fn = function(x1, x2, x3) { return(x1^2 + x2 * x1 + x3) }

We take x1 as each value from ‘data’ and x2, x3 as other arguments that will be first declared and then passed through the apply function:

b = 2 c = 1 # apply along each row: row_fn <- apply(data, 1, fn, x2 = b, x3 = c) # apply along each column: col_fn <- apply(data, 2, fn, x2 = b, x3 = c)

Let us check row_fn and col_fn



The rest of the apply() family follows a similar structure with similar arguments except for a few changes. Let us next use the lapply() function.


The apply() function above has a constraint the data needs to be a matrix of at least 2 dimensions for the apply() function to be performed on it. The lapply() function removes this constraint. Short for list-apply, you can use the lapply function on a list or a vector. Be it a list of vectors or just a simple vector, the lapply() can be used on both. Since we are now dealing with vectors/lists, the lapply function does not need the MARGIN parameter either. That being said, the return type of lapply is also a list.

It takes only the data and the function as the basic parameters:

lapply(X, FUN)

Let us look at some examples:

# define a list cart <- c("BREAD","BUTTER","MILK","COOKIES") # use lapply to convert all to lower case cart_lower <- lapply(cart, tolower) #output cart_lower

We will now take a more complex list of lists:

data <- list(l1 = c(1, 2, 3, 4), l2 = c(5, 6, 7, 8), l3 = c(9, 10, 11, 12)) # apply the 'sum' function on data: sum_list <- lapply(data, sum) #output sum_list sapply()

The sapply() function(short for simplified-apply) is similar to the lapply function. The only difference is the return type of the output – sapply() simplifies the output based on the values returned. I have created a simple table that tells us what type is returned:

Return Value Length of each element Output

List 1 Vector

List > 1 and of the same length Matrix

List > 1 and of the varying length List

We will see examples of all the above scenarios:

Scenario 1: Length of each element = 1 data <- list(l1 = c(1, 2, 3, 4)) # apply the 'sum' function on data: sum_sapply1 <- sapply(data, sum) #output sum_sapply1

Using lapply to see the difference in outputs:

sum_lapply1 <- lapply(data, sum) sum_lapply1

data <- list(l1 = c(1, 2, 3, 4), l2 = c(5, 6, 7, 8), l3 = c(9, 10, 11, 12)) # apply the 'sum' function on data: sum_sapply2 <- sapply(data, sum) #output sum_sapply2

What output does lapply() give us?

sum_lapply2 <- lapply(data, sum) sum_lapply2 data <- list(l1 = c(1, 2, 3), l2 = c(5, 6, 7, 8), l3 = c(9, 10)) # apply the 'sum' function on data: sum_sapply3 <- sapply(data, sum) #output sum_sapply3

Let us compare it with the output of lapply() on the same data:

sum_lapply3 <- lapply(data, sum) #output sum_lapply3

You can see how the output differs from lapply above which returned a list


Coming to the vapply() function. The trio of lapply(), apply(), and vapply() are specially tailored for vectors of all types. Unlike lapply() and sapply() which decide the data type of the output for us, vapply() allows us to choose the data type of the output structure. Thus, the parameters for vapply() are:

vapply(X, FUN, FUN.VALUE)

Here FUN.VALUE is used to provide the data type you want.

This is most useful when our lost/vectors contain a mix of numbers and strings:

data <- list(l1 = c(1, 2, 3, 4), l2 = c(5, 6, 7, 8), l3 = c(9, 10, 11, 12), l4 = c("a", "b", "c", "a")) # apply the 'max' function on data: sum_vapply <- vapply(data, max, numeric(1))

As expected, we got an error because it is not possible to compute the max value from a list of characters. The numeric(1) specifies that we want the output to be individual numeric values where the length of each element is 1. What if we use lapply() or sapply()?

lapply(data, max) sapply(data, max)

Thus, we can see that both lapply() and sapply() actually provided outputs for the same. In fact, sapply() even converted the output to a vector of type character. Ideally, this is not what we want. Generally, this is how we use the vapply() function

data <- list(l1 = c(1, 2, 3, 4), l2 = c(5, 6, 7, 8), l3 = c(9, 10, 11, 12)) max_vapply <- vapply(data, max, numeric(1)) max_vapply

Thus, it is always better to use vapply() when you are working with data frames that have different datatypes of features.


In the simplest terms, tapply() allows us to divide the data into groups and perform the operation on each group. Thus, when you provide a vector as input, tapply() performs the specified operation on each subset of the vector. The parameters it takes are:

tapply(X, INDEX, FUN)

where INDEX represents the factor you want to use to separate the data. Sounds familiar? Yes, tapply() is nothing but a simple way to perform a groupy operation and apply some function on this grouped data!

To observe how tapply() works let us create 2 simple vectors

item_cat <- c("HOME", "SNACKS", "BEVERAGE", "STORAGE", "CLEANING", "STORAGE", "HOME", "BEVERAGE", "ELECTRONICS", "SNACKS") item_qty <-c(25, 30, 45, 66, 15, 50, 35, 20, 15, 35)

Let us now use tapply to get the mean quantity of each item category:

tapply(item_qty, item_cat, mean)

What did the tapply() function do? We grouped the item_qty vector by the item_cat vector to create subsets of the vectors. We then calculate the mean of each subset.

What makes it so easy to use tapply() is that it automatically takes the unique values from the item_cat vector and applies the function we want on the data almost instantly. We can even get more than one value on each subset:

tapply(item_qty, item_cat, function(x) c(mean(x), sum(x)))

Now, we come to the last function in the apply() family of functions – the mapply() function.


mapply() stands for multivariate-apply and basically is a multivariate version of sapply(). The mapply function is best explained by examples – so let us use it first and then try to understand how it works.

Let us first take a function that generally does not take 2 lists or 2 vectors as arguments – for example, the max function. We take 2 lists first:

list1 <- list(a = c(1, 2, 3), b = c(4, 5, 6), c = c(7, 8, 9)) list2 <- list(a = c(10, 11, 12), b = c(13, 14, 15), c = c(16, 17, 18))

Now, what if we want to find the max values between each pair of lists elements?

max(list1$a, list2$a)

Now, this function cannot be applied at the same time across all elements of list1 and list2. In such cases, we use the mapply() function:

mapply(function(num1, num2) max(c(num1, num2)), list1, list2)

Thus, the mapply function is used to perform functions on data that don’t generally accept multiple lists/vectors as arguments. It is also useful when you want to create new columns. Let us first create a dataframe from the matrix we defined initially:

df <-

We will now create a new variable that contains the product of columns V1 and V3:

mapply(function(x, y) x/y, df$V1, df$V3)

Thus, we see that mapply is a really handy function when working with data frames.

Now, let us see how to use these functions on real-life datasets. For simplicity, let us take up the iris dataset:

iris_df<-datasets::iris head(iris_df)

We can now compute the mean of sepal length and sepal width of each row using the apply() function:

iris_df['Sepal_mean'] <- apply(iris_df[c("Sepal.Length", "Sepal.Width")], 1, mean)

Similarly, we can obtain summary values of each column for each species in the dataframe:

tapply(iris_df$Sepal.Width, iris_df$Species, mean)

We can also create a new column showing the sum of petal length and petal width using the mapply() function:

iris_df['Sum_Petal'] <- mapply(function(x, y) x+y, iris_df$Petal.Length, iris_df$Petal.Width) End Notes

So far, we learned the various functions in the apply() family of functions in R. These set of functions provide extremely efficient means to apply various operations on data in a split second. The article covers the basics of these functions for the purpose of making you understand how these functions work.

I encourage you to try more complicated functions on more complicated datasets to fully understand how useful these functions are. How have these functions helped you in working with datasets in R? Please share your replies and any questions below!


Slashgear Week In Review

SlashGear Week in Review – Week 47 2010

Welcome to this week’s edition of the SlashGear Week in Review! I hope you had a good Thanksgiving and all those irritating family members you really didn’t want at your house have finally gone home. Early in the week Cox Communications unveiled a new whole home DVR solution that was sure to make fans of TV and movies with packed DVRs happy. The service lets you watch and play DVR programs on any TV in your home.

Apple iSO 4.2 for iPad, iPhone and iPod touch landed this week. The update adds some really nice new feature to the iPad like AirPrint, AirPlay, Game Center and more. Audi unleashed its sweet TTS autonomous racecar to attack Pikes Peak. The car went up the legendary mountain racecourse in 27 minutes. A car with a driver is expected to make it in at least 17 minutes.

The NVIDIA dual GPU GTX 595 video card leaked and the thing looks very impressive. The leak claims that the card may be using dual GF110 GPUs inside. We grabbed some hands on time with the cool Dell Inspiron duo convertible tablet. First impressions are that it’s a heavier tablet than we are used to and we figure it’s more for the at home user than the mobile type.

Microsovision unveiled another of its tiny pico projectors early in the week called the SHOWWX+ laser projector. The thing is able to directly connect to Apple devices. Google Chrome OS notebooks have been delayed according to Google’s Eric Schmidt and won’t land for a “few months”. However it appears that the beta version of the OS is set to land soon.

The official website for the Notion Ink Adam tablet has gone live. The site gives you an easy to navigate area that tells all about the machine for those interested in getting hands on one. If you updated your iPhone to iOS 4.2 this week and want to jailbreak, Dev-Team has the steps you need to take. The bad news is that iPhone 4 users need to tether each time you reboot or turn the device off.

A really cool Acer 4.8-inch screen Android smartphone was unveiled with a screen resolution of 1280 x 480 and we are excited about the thing. It has a 1GHz CPU, 8MP camera, and a lot more. Acer also debuted a cool dual-screen laptop called the Iconia that is really awesome. The thing runs Windows 7 and I want one pretty bad.

If you like to take your iPad with you everywhere and want to keep it dry and safe from dust and more the Drycase was revealed this week. The Drycase is sort of like a big zip lock baggie for your iPad and will keep liquids and more at bay. Scientists have devised a special food that can be fed to pigeons. Once the birds eat the food, their poo is sort of like soap that will clean your car and the things they crap on. This is cool and really gross all at once.

Moshi has unveiled a cool iPhone dock called the MM03i that has a Bluetooth phone attached that you can use for making and receiving calls. It reminds me of one of those old phones from back in the day. Google TVs from both Toshiba and Vizio are expected to surface at CES 2011 according to some rumors. That really is no surprise that the offerings are coming, whether or not people will be interested since networks have killed the best features of Google TV remains to be seen.

Russia is planning to spend about $2 billion to clean up some of the space junk in orbit around the Earth right now. The plan is for a pod that will knock stuff out of orbit where it would crash into the ocean or burn up in the atmosphere. Tokyoflash unveils a new watch called the Kisai RPM that looks really cool. The watch has a black stainless steel case and blue LEDs, and I can actually read the thing.

The TSA is the source of a lot of ire over their security practices and the penchant for fondling people. If you want to show them what you think on that full body scan you need these 4th Amendment underwear. Rumors are circulating that the long talked about Sony Ericsson PlayStation Phone will land at MWC 2011. If the thing does land then it had better be more interesting than the PSP or the PSP Go.

Some awesome space tires surfaced Friday that were granted a 2010 R&D Award and were designed by NASA and Goodyear. The tires are built out of 800 interwoven load-bearing springs and look like they were stolen off the lunar lander from the 60’s. That’s all for this week’s edition, have a great weekend!

Philips Fidelio M2Bt Review



Our Verdict

The Philips Fidelio M2BT Bluetooth headphones are well made and comfortable, and most importantly sound good. From a pure audio point of view we did find them rather bassy with the high treble slightly subdued. If you like the sound from Beats headphones you’ll probably like these too. From a portable point of view we prefer the more foldable Sennheiser MM 400-X for walking commuters as they’re lighter and suffer less from vibration interference, but the Fidelio M2BT are a fine choice for train, plane and home/office environments.

Searching for the best Bluetooth headphones we have tested the Philips Fidelio M2BT, alongside others from Sennheiser, AKG and Creative. Read our pick of the best headphones available today.

Apple’s swinging white headphone cables may be iconic but they’re also impractical and unnecessary. Who hasn’t  groaned at yet another untangling of headphone cables when pulled out of their bag?

Bluetooth headphones free you from the horror of hanging cables and the terror of yanked, broken jacks.

Simply pair your music device – be it smartphone, MP3 player, laptop, etc – with the Bluetooth headphones, and you’re free of those old-fashioned cables. If you own an old-school iPod with no Bluetooth, fear not – here’s how to add Bluetooth to an iPod.

The Fidelio M2BT can pair with your Bluetooth device either manually or via NFC.

As with other quality Bluetooth headphones the Philips Fidelio M2BT come with a 1.2m cable for those times you want the ultimate sound sensation or your headphones have run out of juice.

Hi-Fi fanatics will point out that for the ultimate audio experience you can’t beat a cable, and they’re right – using Bluetooth will reduce audio quality, but not enough for most of us to truly tell the difference. We have a resident audio boffin here at PC Advisor and for an all-round review we publish his thoughts here too.

Philips has a good reputation for its headphones and other audio products. Don’t forget it’s these guys that invented the cassette and co-developed the CD. Audio experts at the likes of What Hi-Fi are impressed, so the Fidelio M2BT Bluetooth headphones came with high expectations.

Philips Fidelio M2BT sound quality

These headphones are built with 40mm high-magnetic intensity neodymium speaker drivers, and use the high-quality aptX codec for high-resolution, “CD-quality” audio transmission.

From a layman’s point of view I found the sound quality to be higher than some Bluetooth headphones I’ve tested, but what does a real audio expert make of the Fidelio M2BT?

Our audio guru is more demanding than most but it’s worth hearing his thoughts on the audio quality from an expert point of view.

Wired sound   We first tested the headphones with the supplied cable for ultimate audio testing.

The Fidelio M2 headphones have a warm, bass-forward sound, which will be familiar to owners of Beats headphones. They are highly damped, giving a sense of punchiness at the expense of revealing the longer decay of notes. High treble detail has been subdued, reined right in, with the focus moved instead to upper bass and lower midrange.

Stereo width is contained, with more attention drawn to what’s happening at each ear rather than between and all around them. Vocals are soft and smooth, typically a little reduced in level but at least never strident or shouty.

The relaxing, if muffled sound, won’t cause much listening fatigue through any overly energetic upper mid and treble – but you do lose some of the music in the process.

Wireless sound   Next we tried the headphones using no cable, as its wireless Bluetooth connection is why you’re interested in these headphones in the first place. We tested with an iPhone, so using Apple’s AAC codec rather than aptX, which you’d get using an Android phone or laptop.

Over Bluetooth there was a similar presentation but with noticeable further reduction in fidelity. Cymbals sound more splashy than when wired, and guitars sounds little more crunchy and gritty.

Overall the filtering effect that takes out the worst excesses of low bitrate digital compressed audio, can also subtract somewhat from the music. But Beats lovers will find an affinity in these Bluetooth headphones.

Philips Fidelio M2BT comfort

The woven-texture band is easy to adjust. The headphones feel robust and unlikely to break is given the rough treatment.

Sound leakage is reduced with an acoustic seal, so you shouldn’t overly annoy fellow passengers or other people around you when listening to your favourite music.

Philips Fidelio M2BT controls

The right earcup houses the integrated volume and track controls, plus on/off functions. Push the toggle up or down to vary the volume, and press in once or twice to skip forwards and backwards. These work well, and leaving your music player in your pocket is a real benefit.

Philips Fidelio M2BT portability

The headphones are reasonably lightweight (190g) but don’t fold to a coat pocket-size shape like the lighter Sennheiser MM 400-X Bluetooth headphones (105g). They aren’t giant cans so you won’t look like a Cyberman.

One complaint we have is that while walking with the headphones on we could hear the vibration of our steps through the headphones. The Sennheisers were quieter in this regard, and so remain our Editors’ Choice for portable Bluetooth headphones.

The Fidelio are perhaps better suited to interior setting or just less walking environments. In a car or on a train or plane the Fidelio M2BT are recommended.

They come with a soft travel pouch.

Philips Fidelio M2BT battery life

Philips claims that the M2BT’s LI-Polymer batteries have a music play time of 10 hours. They are charged using the supplied Micro USB cable.

Philips Fidelio M2BT mics

The M2BTs feature dual built-in microphones – one for voice and another that measures ambient noise so can automatically adjust for the best call quality. Some might prefer the voice mic to be on the cable but that would nullify the whole point of Bluetooth headphones.

Philips Fidelio M2BT price

Priced at £250 the Fidelio M2BTs certainly aren’t cheap but the price is fair for the build and audio quality, unless you’re not keen on a Beats-like bassiness. We’ve seen them available for under £180 online so shop around for the best deal. See below this review for the latest, best online prices.

Specs Philips Fidelio M2BT headphones: Specs

Frequency response: 7 – 23,500Hz

Impedance: 16 Ohm

Sensitivity: 107dB

Speaker diameter: 40mm

Maximum power input: 40mW

Distortion: < 0.1% THD

Acoustic system: Closed Diaphragm: PET

Magnet type: Neodymium

Type: Dynamic

Needletail Sx Gaming Pc Review

When you pull the Needletail SX out of the box for the first time, you’re likely to be taken aback by how awesome everything looks. All of your hardware is enclosed in an excellent NZXT Switch 810 full case, which has a window on the right side so you can look inside and view your motherboard. The case itself is sleek, with more than enough room on the inside to add additional hardware should you ever want to. The case also comes equipped with a number of dust filters to make fan maintenance less of a chore. The case is glossy in most places, which means that it will attract fingerprints easily, but a little upkeep is a small price to pay to keep your Switch 810 looking great.

That sexy-looking case is filled with some of the best hardware around. Bringing everything together is an ASUS Rampage IV Extreme motherboard. This particular motherboard features an Intel x79 chipset, and enough PCI express 3.0 slots to support 4-way SLI or Crossfire. Even though the Needletail SX already comes with more than enough graphics power, it’s nice to know that you can continue to upgrade should you need any more power in the future. On the back of the unit, we’ve got 4 USB 3.0 ports, 8 USB 2.0 ports (one of which is reserved for ROG Connect), and two eSATA 3.0 ports. The ASUS Rampage IV Extreme is an excellent motherboard, but then again it would need to be with all of this high-end hardware attached to it.

Next up let’s talk about RAM. Surrounding the CPU in a rather nice looking display are 8 G.Skill Ripjaws Z 4GB DD3 cards at 2133 MHz. That means you have a whopping 32GB of RAM at your disposal – likely more than you’ll ever need, but there to ensure that everything runs extremely smooth. Indeed, that RAM helps make this computer incredibly fast, regardless of what you’re doing. You can play a game with all the settings maxed (more on that later) and never have to worry about stuttering, thanks partially to the amount of RAM you’ve got under the hood.

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The dual SLI EVGA NVIDIA GTX680 graphics cards help with that too, naturally. With 2GB of video RAM each, this SLI setup can handle anything you can throw at it, as far as gaming goes. The GTX680 is one of NVIDIA’s enthusiast-level cards, meant only for those who take PC gaming incredibly seriously, and this computer uses two of them. Of course, installing two of the best GPUs around makes for a pretty significant price hike, but with these two working together, you won’t have to worry about updating your graphics hardware anytime soon.

The CPU is cooled by a Corsair Hydro Serious H100 cooler, and what’s interesting about this particular water cooler is that comes with adjustable fan settings. There are three settings in total – low, medium, and high – and the computer comes set to medium out of the box. You’ll be able to use the medium settings for most anything you’ll be doing with the Needletail SX, as it isn’t too loud (though it isn’t exactly silent either), and provides more than enough air to keep the computer cool while playing even the most graphics-intensive game. AFS recommends that you install a CPU thermometer widget to ensure that your CPU never runs above 82 degrees Celsius for too long, but in all of my tests, I never managed to get the CPU to heat up hotter than the mid-50s range. In other words, this cooling system does its job wonderfully, even when you’re intentionally trying to push it to its limits.

You’ve heard enough about how great the hardware in this PC is, but the benchmarks prove that it isn’t just talk. With Geekbench 2.0, the Needletail SX managed to post a score dangerously close to 25,000. The processor was the star of the test, pulling in ridiculously high numbers, especially with the processor floating point test. Running Cinebench 11.5, we get impressive results once again. Cinebench is a benchmark tool that tests both the CPU and the graphics power, and both came back with excellent scores. The CPU test showed a score of 12.71, while the OpenGL test ran at a smooth 60.34 fps. It isn’t that often you get a computer that can put out scores like that – be it in Cinebench or Geekbench – which just goes to show that AFS was serious about building an enthusiast-level gaming PC when they put the Needletail SX together.

[sgbenchmark id=173 show=score]

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