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Bisnis Industri

La tarjeta Nomad Tracking funciona con Find My y se recarga a través de MagSafe

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Tarjeta de seguimiento nómada con iPhone

Puede usar Find My en su iPhone u otro dispositivo para rastrear la tarjeta.
Foto de : Al-Badawi

La nueva y delgada tarjeta de seguimiento Nomad funciona con la aplicación Find My de Apple en sus dispositivos y se recarga a través de MagSafe para ayudarlo a controlar su billetera, bolso u otros objetos de valor, dijo Nomad el martes.

El rastreador del tamaño de una tarjeta de crédito cuesta 40 dólares individualmente o 120 dólares por un juego de cuatro.

Tarjeta de seguimiento beduino

Podrás colocar la nueva tarjeta de seguimiento entre tus tarjetas o pertenencias Seguilos en secreto. Puede localizar la tarjeta utilizando la aplicación Find My en su iPhone, iPad o Mac. La nueva tarjeta se suma a la extensa colección de Nomad de accesorios.

La tarjeta de seguimiento Nomad se une a las filas de alternativas populares de AirTag como Tile Slim y cuenta con su propia batería recargable, una característica de la que carecen muchos competidores.

Si bien es posible que no ofrezca las capacidades de búsqueda precisas del AirTag de Apple porque carece del chip U1 de Apple, el conveniente factor de forma de la etiqueta de seguimiento es útil para rastrear lo básico.

La recarga se realiza mediante MagSafe o Qi

Tarjeta de seguimiento nómada en el remitente
Cárgalo con el mismo cargador que usas para tu iPhone.
Foto de : Al-Badawi

Fabricada con policarbonato duradero, la tarjeta de seguimiento tiene una clasificación IPX7, lo que garantiza protección contra el agua y el polvo. Su batería recargable dura hasta 5 meses con una sola carga y puedes cargarla fácilmente con cualquier cargador inalámbrico Qi o MagSafe.

Aunque es compatible con la carga MagSafe, Nomad señala que la tarjeta de seguimiento no tiene imanes. Por lo tanto, no hay riesgo de desmagnetización o interferencia con otras tarjetas en su billetera.

Aunque puede que no ofrezca el mismo nivel de precisión que los dispositivos Apple equipados con el chip U1, la tarjeta de seguimiento Nomad aún aprovecha la función Buscar mi red, lo que la convierte en una herramienta valiosa para localizar sus pertenencias en un área más amplia. De esta manera, podrás identificar el área general donde lo extraviaste.

Puede comprar su nueva tarjeta de seguimiento Nomad directamente desde Nomadgoods.com. Nomad ofrece un paquete inicial de cuatro tarjetas de seguimiento por un precio con descuento de $120, lo que le permitirá ahorrar $20.

Donde comprar: beduino



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Featured

Smart ring vs smartwatch: Which fitness tracking wearable is best for you?

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Smart rings vs. smartwatches: which is the best choice? As smart rings become more popular and enter the public consciousness, many people are asking this question. but there’s no straightforward answer. Both devices serve similar purposes, but are made for different people with different preferences. In this guide, I’ll help you decide which is right for you.

Many tech reviewers have suggested that smart rings could signal the end of smartwatches. But based on my extensive testing of the best smart rings, I can confidently say they won’t suit everyone. While it might seem that wearing a tracker on your finger could be more comfortable and convenient, this isn’t always the case. You’ll also need to consider accuracy, design, price, and tracking features.

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Entertainment

Blackmagic’s DaVinci Resolve 19 arrives with AI-powered motion tracking and color grading

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Blackmagic Design released its annual NAB 2024 update and announced over a dozen new products, including a new version of its popular DaVinci Resolve editing suite. Other key products include the Micro Color Panel for DaVinci Resolve on iPad, a 17K 65mm camera and the Pyxis 6K cube camera.

Davinci Resolve 19

DaVinci Resolve has become a popular option for editors who don’t want to pay a monthly subscription for Adobe’s Premiere Pro, and is arguably more powerful in some ways. The latest version 19 takes a page from its rival, though, with a bunch of new AI-powered features for effects, color, editing, audio and more.

Blackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color gradingBlackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color grading

DaVinci Resolve 19 ‘Color Slice’ tool (Blackmagic Design)

Starting with the Edit module, a new feature lets you edit clips using text instead of video. Transcribing clips opens a window showing text detected from multiple speakers, letting you remove sections, search through text and more. Other features include a new trim window, fixed play head (reducing zooming and scrolling), a window that makes changing audio attributes faster and more.

The Color tool introduces “Color Slice,” a way to adjust an image based on six vectors (red, green, blue, yellow, cyan and magenta) along with a special skin tone slider. For instance, you can adjust any of those specific colors, easily changing the levels of saturation and hues, while seeing and adjusting the underlying key. The dedicated skin slider will no doubt make it attractive for quick skin tone adjustments.

Blackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color gradingBlackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color grading

DaVinci Resolve 19 Intellitrack (Blackmagic Design)

Another key feature in Color is the “IntelliTrack” powered by a neural engine AI that lets you quickly select points to track to create effects or stabilize an image. Blackmagic also added a new Lightroom-like AI-powered noise reduction system that quickly removes digital noise or film grain from images with no user tweaking required.

“Film Look Creator” is a new module that opens up color grading possibilities with over 60 filmic parameters. It looks fairly easy to use, as you can start with a preset (default 65mm, cinematic, bleach bypass, nostalgic) and then tweak parameters to taste. Another new trick is “Defocus Background,” letting users simulate a shallow depth of focus via masking in a realistic way (unlike smartphones), while Face Refinement tracks faces so editors tweak brightness, colors, detail and more.

The Fusion FX editor adds some new tools that ease 3D object manipulation and on the audio (Fairlight) side, BMD introduced the “Dialogue Separator FX” to separate dialogue, background or ambience. DaVinci Resolve 19 is now in open beta for everyone to try, with no word yet on a date for the full release. As usual, it costs $295 for the the Studio version and the main version is free.

Micro Color Panel

Blackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color gradingBlackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color grading

Blackmagic Design

BMD’s DaVinci Resolve for iPad proved to be a popular option for editors on the go, and now the company has introduced a dedicated control surface with the new Micro Color Panel. It’ll offer editors control that goes well beyond the already decent Pencil and multitouch input, while keeping a relatively low profile at 7.18 x 14.33 inches.

A slot at the top front lets you slide in your iPad, and from there you can connect via Bluetooth or USB-C. The company promises a “professional” feel to the controls, which consist of three weighted trackballs, 12 control dials and 27 buttons. With those, you can perform editors, tweak parameters like shadows, hues and highlights, and even do wipes and other effects.

“The old DaVinci Resolve Micro Panel model has been popular with customers wanting a compact grading panel, but we wanted to design an even more portable and affordable solution,” said Blackmagic Design President Grant Petty. It’s now on pre-order for $509.

Pyxis 6K camera

Blackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color gradingBlackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color grading

Blackmagic Design

Blackmagic Design is following rivals like RED, Sony and Panasonic with a new box-style camera, the Pyxis 6K full-frame camera. The idea is that you start with the basic brain (controls, display, CFexpress media, brain and sensor), then use side plates or mounting screws to attach accessories like handles, microphones and SSDs. It’s also available with Blackmagic’s URSA Cine EVF (electronic viewfinder) that adds $1,695 to the price.

Its specs are very similar to the Blackmagic Cinema Camera 6K I tested late last year. The native resolution is 24-megapixels (6K) on a full 36 x 24mm sensor that allows for up to 13 stops of dynamic range with dual native ISO up to 25,600. It can record 12-bit Blackmagic RAW (BRAW) directly to the CFexpress Type B cards or an SSD.

It also supports direct streaming to YouTube, Facebook, Twitch and others via RTMP and SRT either via Ethernet or using a cellular connection. Since the streaming is built into the camera, customers and csee stream status and data rate directly in the viewfinder or LCD. The Pyxis 6K arrives in June for $2,995 with three mounts (Canon EF, Leica L and Arri PL).

Blackmagic URSA Cine 12K and 17K

Blackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color gradingBlackmagic's DaVinci Resolve 19 arrives with AI-powered tracking and color grading

Blackmagic Design

Along with the Pyxis, Blackmagic introduced a pair of cinema cameras, the URSA Cine 12K and 17K models. Yes, those numbers represent the resolution of those two cameras, with the first offering a full-frame sensor 36 x 24mm with 12K resolution (12,888 x 6,480 17:9) at up a fairly incredible 100 fps. The second features a 65mm (50.8 x 23.3 sensor) with 17,520 x 8,040 resolution offering up to 16 stops of dynamic range.

Both models will come with features like built-in ND filters, an optical low pass filter and BMD’s latest gen 5.0 color science. The URSA Cine 12K will come with 8TB of internal storage, or you can use your own CFexpress media. Other features include live streaming, a high-resolution EVF, V-battery support, wireless Bluetooth camera control and more. The URSA Cine 12K model is on pre-order for $14,995 or $16,495 with the URSA Cine EVF, with April availability. The URSA Cine 17K is under development, with no pricing or release yet announced.

This article contains affiliate links; if you click such a link and make a purchase, we may earn a commission.

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Categories
Life Style

Climate velocities and species tracking in global mountain regions

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Mountainous regions represent 25% of Earth’s land surface and are rich in biodiversity, owing in part to their steep climatic gradients and complex topography1,2. The assumption that mountain species are responding faster to anthropogenic climate change through rapid upward range shifts leading to potential mountaintop extinctions has attracted extensive research3,4,7,8,9. Whether species are closely tracking the rate of climate warming is assessed chiefly by comparing the velocities of species range shifts with the velocities of climate change; that is, the rates at which isotherms move through the geographical space3,4,10,11,12. Past studies that assessed climate velocities have focused mainly on horizontal velocities, in km per year; that is, how fast isotherms are moving along the latitudinal and longitudinal clines of the horizontal plane (see the seminal work from Loarie et al.12 for terrestrial systems; this was then applied to marine systems by Burrows et al.13). Because isotherms are located closer to one another in mountainous regions, horizontal velocities of isotherm shifts are much slower and potentially omnidirectional in mountains, whereas they are much faster and oriented mainly poleward in the lowlands13. However, we know that climate warming also causes terrestrial species to shift along mountain slopes and thus not only horizontally but also ‘vertically’ when projected along elevation gradients—moving at very different speeds (usually expressed in m per year), and mainly upward but sometimes downward3,14,15. Despite this knowledge, global maps of the velocities of isotherm shifts projected along the vertical dimension of elevational clines in mountain regions still do not exist. This shortfall stems partly from the complex topography and the scarcity of weather stations in most mountain ranges globally5,16, which makes it difficult to accurately measure vertical velocities of climate change in mountain regions worldwide. Therefore, it is still an open question whether mountain species better track isotherm shifts vertically in elevation rather than horizontally in latitude.

Because we still lack global maps of the velocities at which isotherms are shifting vertically along elevation gradients as the climate warms, most local studies compute a rough estimate of this vertical projection of climate velocities by relying on a constant lapse rate of temperature (LRT). The LRT is defined here along mountain slopes as the normalized temperature difference at approximately 2 m above ground level between a low-elevation and a high-elevation weather station and thus it differs from a sensu stricto vertical lapse rate measured above a single geographical position. According to the laws of thermodynamics6, the LRT is 9.8 °C per km in the case of dry air1,6. Nonetheless, given that Earth’s atmosphere is not entirely dry, the LRT experienced by terrestrial organisms in reality will be less steep than 9.8 °C per km. Because of that, most studies that have compared the observed velocities of species range shifts along elevation gradients with the velocities of climate change inside a given mountain range inferred the vertical shift of isotherms by relying on a constant rate of 5.5 °C per km for the LRT11—a constant that is borrowed from limited ground observations concentrated in Europe7,17. Using this fixed rate, one can assume that if the temperature increases by 1 °C over a given period of time, then it is expected that isotherms will move upslope by about 181.8 m during that same period, which gives a vertical velocity that varies depending only on the magnitude of temperature change per unit of time. However, the LRT is not constant and varies across elevation gradients among mountain ranges as well as within a single mountain range18,19,20,21. For instance, by using long-term climatology (30-year means) from 269 weather stations in northern Italy, 205 in the Tyrol area and 166 in the Trentin–upper Adige region, covering a wide range of elevations, one study21 found that the annual mean of the LRT ranges between 5.4 and 5.8 °C per km in the Alps. In the southeastern Tibetan Plateau, the LRT was estimated22 to reach 8.5 °C per km. This large variation in the LRT partly stems from water vapour pressure because if the air condenses moisture as it cools—for example, in cloud forests—it gains some heat from condensation, which slows the cooling rate. Thus, moisture and surface temperature generate spatial variability in the LRT and consequently also generate spatial variation in the velocities at which isotherms may shift along mountain slopes as the climate warms by a given amount of temperature increase. Assessing mountain climate velocities by explicitly considering the determinants of the LRT is a crucial step in improving our understanding of species range shifts under anthropogenic climate change. Here, instead of relying on a constant LRT value of 5.5 °C per km in the Alps or of 8.5 °C per km in the Himalayas, we propose two different methods to map the spatial variation in the LRT, and we generate more meaningful estimates of the vertical velocities of isotherm shifts in mountain systems worldwide. First, we use satellite observations of land surface temperatures at fine spatial resolution to compute a satellite-derived version of the LRT (SLRT), based on local slope estimates of the relationship between temperature and elevation (Fig. 1a and Extended Data Fig. 1); and second, we use a more mechanistic approach based on the moist adiabatic LRT (MALRT), building on the laws of thermodynamics6 (Fig. 1c and Extended Data Fig. 2a,b). By combining information on the spatial variation of the SLRT and the MALRT at relatively fine spatial resolution worldwide with data on the magnitude of temperature change over time per spatial unit, we then compute maps of the vertical velocities of isotherm shifts in mountain systems: one that is based on satellite observations (SLRT); and one that mechanistically accounts for water vapour pressure conditions (MALRT). These two global maps of the vertical velocities of isotherm shifts in mountain regions are also compared to a third naive map that is based on a constant LRT of 5.5 °C per km. By using these global velocity maps, we subsequently identify the mountain regions with the highest vertical velocities of isotherm shifts in the world, and we quantify the variation in velocity values along several elevation gradients worldwide. Finally, we relate those vertical velocities of isotherm shifts, in m per year, to empirical observations of species range shifts, also in m per year, along several elevation gradients in mountain systems worldwide.

Fig. 1: Assessing the adiabatic LRT either through satellite observations (SLRT) or by using a mechanistic approach that accounts for water vapour (MALRT).
figure 1

a, An example mountain range in Taiwan with a series of elevation transects, in red, defined by the highest peak at one end of the gradients and several foothills and valleys at the other end of the gradients. The background raster layer depicts the mean elevation (in m above sea level) for each spatial unit of 0.05° (around 5 km at the equator) resolution. Details can be found in the Methods and in Extended Data Fig. 1. b, Global map of the SLRT, generated at 0.5° (around 50 km at the equator) resolution across all mountain regions worldwide (except Antarctica) using satellite observations from 2011–2020. c, Three-dimensional plot showing the effect of mean annual temperature and mean annual water vapour pressure on the absolute magnitude of the MALRT (in °C per km). d, Global map of MALRT, generated at 50-km resolution across all mountain regions worldwide (except Antarctica) using climatic data from 2011–2020. Note that the colour scheme does not show the full range of data to prevent highly skewed visualization driven by extreme outliers.

Source Data

We found that there was very large spatial variation when mapping the lapse rate at a global extent (Fig. 1), either from satellite observations (SLRT; Fig. 1b) or from the laws of thermodynamics (MARLT; Fig. 1d), with values ranging (at the 5th and 95th percentiles) from −5.14 to 8.45 °C per km and from 2.94 to 8.09 °C per km, respectively. Although the two global maps show a certain degree of spatial agreement (Supplementary Results), the SLRT shows much shallower lapse rates than does the MALRT in mountain regions that are located at higher latitudes, such as in northeastern Siberia, Alaska and northwestern Canada (Fig. 1b,d). The mountain regions showing the steepest lapse rates are located in the Himalayas, with values that are very consistent with the values recently reported for the southeastern Tibetan Plateau, which range between the values of free-air dry (10 °C per km) and moist (6.5 °C per km) adiabatic lapse rates22. For comparison purposes and external validation, we also extracted data from the Global Historical Climatology Network23, focusing on empirical field data recorded by weather stations situated in mountain regions worldwide. We manage to obtain temperature lapse rates from 144 weather stations (station-based LRT; see Methods) across a total of 48 mountain sites from 2011 to 2019 (Extended Data Fig. 3a). This validation exercise confirms that there are very few mountain regions worldwide in which the network of weather stations is dense enough along mountain slopes (n > 2) to compute the LRT. Nevertheless, we found a positive relationship between the station-based LRT calculated from these very limited networks of weather-station data and our computations of the MALRT (linear regression, F1, 46 = 5.54, p = 0.02, R2 = 0.108, n = 48, Extended Data Fig. 3a). By contrast, the relationship between the SLRT and the station-based LRT did not reach statistical significance (linear regression, F1,46 = 0.774, P = 0.38, R2 = 0.017, n = 48; Extended Data Fig. 3b). Owing to the relative scarcity of weather-station data and the fact that these data are concentrated mainly in North America and Europe, our subsequent analyses will focus solely on our computations of the MALRT and the SLRT.

After combining maps of the spatial variation in the LRT with data on the rate of temporal changes in mean annual temperature (Extended Data Fig. 2c), we found notable differences in the vertical velocities (in m per year) of isotherm shifts depending on the approach we used (Fig. 2), with the constant LRT-based and MALRT-based estimates generally yielding conservative climate velocities and the SLRT-based climate velocities showing the greatest variability. Velocity values for the SLRT-based map ranged from highly negative (−26.01 m per year; at the 5th percentile) to highly positive (34.08 m per year; 95th percentile) (Fig. 2g–i). By contrast, the MALRT-based map shows velocity values ranging (at the 5th and 95th percentile) from 1.81 m per year to 10.83 m per year. When we combined the SLRT-based velocity map with the MALRT-based velocity map to reach a consensus map on the mountain regions most threatened by climate change (Methods and Fig. 3a,b), we found that 32% of the surface area covered by mountains worldwide, Antarctica excluded, is exposed to high vertical velocities of isotherm shifts that exceed the 80th percentile by either the MALRT (80th percentile: 8.25 m per year; Fig. 3) or the SLRT (80th percentile: 11.67 m per year; Fig. 3). We delineated 17 mountain regions that are partly exposed to high vertical velocities, including those in the Alaska–Yukon region, western America and Mexico, Appalachia, the Brazilian highlands, Greenland, Scandinavia, the Mediterranean basin, southern Africa, the Ural mountains, the Iran–Pakistan region, the Putorana mountains, Mongolia, northern Sumatra, the Kodar mountains, Yakutiya, northeast Asia and Kamchatka (Fig. 3c and Supplementary Data 1). Intuitively, higher rates of warming lead to higher vertical velocities of isotherms shifting faster along elevation gradients. This is the case chiefly in dry regions with a low water vapour pressure, such as Greenland, the Putorana Plateau in northern Siberia, Kamchatka, Mongolia and the Alaska–Yukon region—owing probably to the limited heat capacity of these arid areas24,25 (Fig. 3d). In addition, by relying on laws of thermodynamics, we can also anticipate that regions with higher surface temperatures and/or higher water vapour pressure might also generate high vertical velocities because of shallower lapse rates: isotherms will shift faster along such elevation gradients for the same amount of temperature change over time. Notably, these regions are not necessarily those showing significant surface warming over time. For instance, northern Sumatra, the Brazilian highlands, southern Africa and Iran–Pakistan are typical representatives of such shallow lapse rates with little surface temperature increase (Fig. 3c,d). These are mountain regions threatened by high vertical velocities of isotherm shifts that have been difficult to detect in the past by surface temperature change alone, and thus are particularly worthy of further investigation.

Fig. 2: Mapping the vertical velocities of isotherm shifts across mountain regions globally.
figure 2

ai, Vertical velocities of isotherm shifts (m per year) in mountain regions worldwide using a constant LRT (ac), the MALRT (df) or the SLRT (gi) (1971–1980 versus 2011–2020). b,e,h, Normalized value from the corresponding panel (a,d,g) to show clear spatial variation in each panel. c,f,i, Histograms of the velocity values across all mountain regions for the constant LRT, the SLRT or the MARLT, respectively. Note the log10 scale for the histogram displaying the range of velocity values for the SLRT. The SLRT values were rescaled using the function sign(x) × log10(abs(x) + 1) to ensure that the shifting direction is preserved and to avoid interference from the value range of logarithmic transformation. Black dashed lines indicate the median; yellow solid lines show the 80% quantile; red solid lines show the 90% quantile. The corresponding values are labelled above. Note that the colour scheme does not show the full range of data to prevent highly skewed visualization driven by extreme outliers.

Source Data

Fig. 3: Identifying mountain regions threatened by high vertical velocities of isotherm shifts and underlying mechanisms.
figure 3

Consensus map of the vertical velocities of isotherm shifts as estimated from the SLRT or from the MALRT (see Fig. 2). ac, Mountain regions in which velocities are greater than the 80% quantile (that is, retaining 20%) in the calculation of either the MALRT or the SLRT are labelled as critically threatened (a,b) and displayed in red (c). d, Orange points and segments represent the mean annual temperature change between the periods 1971–1980 and 2011–2020; blue bars represent the mean water vapour pressure during 2011–2020 for each of the 17 mountain regions affected by relatively fast vertical velocities of isotherm shifts. Error bars represent s.d. See Supplementary Data 1 and ‘Data availability’ for a comprehensive breakdown for each region, including sample size information. Considering that near-zero SLRT values result in extremely high climate velocity, we removed 1% outliers that are close to zero in c. Data with alternative levels of outlier removal (0.5%, 2% and 5%) are shown in Supplementary Fig. 2. Supplementary Data 3 provides a high-resolution map.

Source Data

We further compared the effects of high warming rates and steep temperature lapse rates, which act as compensatory effects on climate velocities, between arid and more humid regions. We found that in arid mountain regions with a low water vapour pressure, the temperature lapse rate accounts for 3.6% of the observed variation in climate velocity, whereas changes in surface temperature account for 96.4% of the observed variation, on the basis of the random forest analysis we performed. A detailed analysis using the Shapley value further revealed that steeper lapse rates have a smaller negative effect on climate velocities compared with higher warming rates, which increase climate velocities (Extended Data Fig. 4a). In humid regions, the temperature lapse rate accounts for 11.32% of the observed variation in climatic velocity, whereas changes in surface temperature explain 88.68% of the observed variation, on the basis of the random forest analysis we performed. The Shapley value analysis showed that steeper lapse rates still have a smaller negative effect on climate velocities than do higher warming rates (Extended Data Fig. 4b). Of note, the explanatory power of the lapse rate in wet mountains is nearly four times higher than it is in arid mountains. This difference is likely to be due to the lower magnitude of the surface temperature increase in wetter mountains (Extended Data Fig. 4c,d). Although the explanatory power of the lapse rate is, in general, relatively much lower than that of the warming rate, the striking differences that we found between arid and humid regions, in terms of the relative importance, affects the spatial variation that we report in the vertical velocity of isotherm shifts.

Focusing on the MALRT-based velocity map, we found a complex pattern of elevation-dependent velocities for isotherm shifts (also known as climate velocities; Fig. 4), with the highest vertical velocities of isotherm shifts being concentrated at low elevations. This was especially the case in the Northern Hemisphere and at a latitude of 20–30° S in the Southern Hemisphere, whereas the lowest vertical velocities were located at high elevations in the Himalayas and the Andes. Statistical results indicate that isotherm velocities are significantly higher at lower elevations (slope: −0.285 m per year∙km, degrees of freedom (df) = 12,028, t = −4.243, P < 0.001) and higher absolute latitudes (slope: 0.048 m per year∙deg, df = 12,028, t = 24.163, P < 0.001) in the Northern Hemisphere, whereas the magnitude of the effect significantly changed in the Southern Hemisphere (P < 0.001 for all interaction terms composed of elevation, latitude and hemisphere; see Methods). In the Southern Hemisphere, the elevational effect is stronger with a more negative slope estimate (slope: −1.178 m per year∙km), but the latitudinal effect was completely reversed compared with the Northern Hemisphere (slope: −0.040 m per year∙deg). The reversed latitudinal effect we detected here is likely to be due to the reduction of land area towards higher absolute latitudes in the Southern Hemisphere, where oceans predominate over landmasses, leading to a relatively higher water vapour pressure (Extended Data Fig. 2b) and consequently a lower temperature rate (Extended Data Fig. 2c). We further analysed the effects of changes in surface temperature and the MALRT on the rates of isotherm shift with elevation (Supplementary Fig. 1). We found no significant linear correlation between the rate of surface temperature change and elevation when the effect of latitude was statistically controlled. However, the MALRT becomes steeper with increasing elevation, leading to lower vertical velocities of isotherm shifts at higher elevations compared with lower elevations (that is, a steeper MALRT corresponds to lower vertical velocities of isotherm shifts). On islands in the Northern Hemisphere, we found higher vertical velocities of isotherm shifts (7.46 ± 2.33 m per year) exceeding, on average, the mean vertical velocity we found across all main continents in the Northern Hemisphere (6.29 ± 2.61 m per year; Fig. 4d,e; df = 3, F = 352.9, P < 0.001). These results suggest that mountain islands in the Northern Hemisphere are even more threatened by the effects of climate change than are mountains on the mainland, and this poses a high threat to island biodiversity given that mountain islands have many endemic species26,27. However, mountain islands in the Southern Hemisphere do not show vertical velocities of isotherm shifts that are as high as those in the Northern Hemisphere (Fig. 4e).

Fig. 4: The velocities of climate change (1971–2020) along latitude–elevation gradients and in mountain islands.
figure 4

a, Mean climate velocity of mountains worldwide. Mountain summits are labelled for reference. b,c, The corresponding s.d. (b) and sample size (c) for a. d, Mean climate velocity of mountain islands. The s.d. and sample size for d can be found in Supplementary Fig. 3. The colour legend in d is the same as in a. e, The comparison between mainland and islands in the Northern and Southern hemispheres relies on ANOVA and post-hoc Tukey HSD tests. Other than the P = 0.002 between Southern Hemisphere mainland (S. Mainland) and Southern Hemisphere island (S. Island) (by Tukey HSD test), P < 10−16 is shown in all statistics (labelled as ***). The centre line of the box plot represents the median; box limits, upper and lower quartiles; whiskers, 1.5 times the interquartile range. The sample sizes for S. Mainland, S. Island, Northern Hemisphere mainland (N. Mainland) and Northern Hemisphere island (N. Island) are 1,222, 199, 10,331 and 284, respectively. f, Observed species range shifts against the vertical velocities of isotherm shifts. Areas labelled as ‘not applicable’ (in grey) denote instances in which the number of records in a taxonomic group falls below the stipulated minimum (in this case, 30) required to conduct a meaningful statistical comparison to the predicted environmental climate velocities. g, The different probabilities of species tracking climate velocities under a P = 0.05 threshold. Only mean values are shown. Upward and downward shifts are shown together with their absolute values. For results based on different P value thresholds, see Extended Data Fig. 6d,e. A total of 83 taxon–region pairs are plotted. Each plot represents 1 to more than 400 raw data points. See Extended Data Fig. 6b,c for details and Supplementary Fig. 4 for raw data points. All statistics used a two-tailed approach without adjustment for multiple comparisons.

Source Data

Next, we used our estimates of the vertical velocities of isotherm shifts in mountains and linked them to empirical data on the velocities of species range shifts along mountain slopes. We used a carefully curated dataset—BioShifts4—which provides the vertical velocities of species range shifts (in m per year along elevation gradients) per taxonomic unit after standardizing the raw range shift estimates reported by authors in their original studies. Because our analysis shows that the MALRT has a much greater explanatory power for predicting the velocities of species range shifts than does the SLRT (Supplementary Results and Extended Data Fig. 5), we report only on the relationship between the velocities of species range shifts along elevation gradients and the vertical velocities of isotherm shifts in mountains as calculated by the MALRT. Indeed, the Akaike information criterion (AIC) values from our models are 35,887, 37,016 and 51,398 for the MALRT, constant LRT and SLRT, respectively, ranking from best to worst in terms of model fit. This discrepancy between the MALRT and the SLRT is likely to be due to the fact that the satellite (MODIS) data measure the actual land surface temperature, which is influenced by microscale surface properties such as albedo, emissivity, rock type and vegetation cover. Hence, for the SLRT, the calculated lapse rate is characterized by considerable noise. Moreover, the SLRT data are available mainly in cloud-free conditions, which intensify these spatial variations. As a consequence, satellite data present several limitations, and thus have a limited capacity to explain species range shifts compared with insights obtained from theoretical calculations of the MALRT. Comparing the vertical velocities of isotherm shifts based on the MALRT with the observed rates of species range shifts, the probability that a given taxonomic unit tracks the vertical velocities of isotherm movements decreases sharply with increasing absolute velocities of isotherm shifts (Fig. 4f,g). Thus, we found that species seem to track climate change only at lower velocities along the elevational gradients, irrespective of the taxonomic group (Fig. 4g, Extended Data Fig. 6d,e and Extended Data Fig. 7). These results reveal the potentially catastrophic effects of rapid climate change on mountain biodiversity. Although the MALRT will probably undergo changes over time owing to temporal variations in the spatial distribution of temperature and water vapour along elevation gradients, it is important to note that the effects resulting from a shallow MALRT are expected to be worrisome.

Our assessment of mountain climate velocity yields a mechanistic understanding of the variability in mountain climate change globally. The thermodynamic theories of the MALRT, which consider water vapour and latent heat release, suggest that threats to mountain biodiversity can occur in the absence of rapid surface warming. As our range shift analysis shows, species are unlikely to track isotherms quickly enough to match the high velocities at which isotherms are moving along some elevation gradients. Our results suggest that the vertical distance between isotherms in mountains is a crucial factor driving species migration. Likewise, on the basis of thermodynamic theory, colder and drier conditions at higher elevations make temperature lapse rates steeper, which, in turn, leads to a contraction of the vertical distance separating isotherms (that is, isotherm spacing contracts when projected on the vertical axis), generating lower vertical velocities of isotherm shifts. This suggests that in many mountain regions, the vertical shift of isotherms decreases with increasing elevation. From the perspective of isotherms shifting upslope owing to warming, higher elevations will experience a slower rate of isotherm shift, meaning that organisms can reach habitats with suitable temperatures by moving shorter vertical distances. However, a steeper temperature lapse rate also means that the environment changes more rapidly with elevation. Therefore, in the case of mountains with a broader base and narrower peaks28, warming might result in a reduction of habitat area for organisms. Because the shape of a mountain affects the amount of habitat available to organisms28, understanding the velocity of climate change, as well as quantifying the suitable habitat area under warming conditions, will be essential for understanding the effects of climate change on mountain biodiversity.

Moreover, our findings suggest that all taxonomic groups will be similarly affected in their abilities to track isotherms along mountain slopes. Considering that the distance of climate tracking is several orders of magnitude shorter in elevation compared with latitudinal gradients, the moving capability of organisms is less likely to be the key constraint in mountain systems. Mountainous regions, with their complex topography, occupy a relatively smaller proportion of landmasses compared with other terrains in the lowlands28. As described above, the available habitat area for organisms in mountain regions is influenced by the shape of the mountain, and many mountains exhibit a reduction in area with increasing elevation. This, combined with biotic interactions such as interspecific competition29,30, might collectively limit the ability of mountain species to track isotherm shifts in the future. Mountains that we identified as facing high risks under climate change are particularly threatened by biotic attrition17, biotic homogenization31, population extirpation32,33,34 and changing ecosystem properties35. Many of these mountains are located in biodiversity hotspots (for example, Sundaland, Irano-Anatolia, southern Africa, the Mediterranean basin, the Atlantic forest, Mesoamerica, the California Floristic Province and Japan)36,37, reinforcing the need to develop climate-change adaptation strategies for the conservation of mountain biota. Other climatic drivers and mechanisms such as precipitation, snow albedo, radiation flux variability, aerosols and land-use changes can also influence energy balance regimes and further mediate mountain climates5,38,39. Despite many efforts to collect data on species range shifts in mountainous regions, the vast majority of data on species range shifts are still concentrated in Europe and North America4. This also creates uncertainty in assessing the biological effects of climate change at a global extent.

We emphasize that our results are crucial for assessing the vulnerability of mountain regions to climate change globally. By integrating surface temperature and water vapour pressure data with a thermodynamic model, we are able to make effective qualitative comparisons of global lapse rates and identify regions with comparatively higher or lower climate velocities. In particular, this approach enhances the explanatory power of our methodology over other existing methods (such as satellite data analysis) for assessing global species range shifts. However, it is important to recognize that our thermodynamic model still suffers from a low predictive accuracy when compared with field measurements of temperature lapse rates, and we cannot accurately quantify local-scale lapse rates solely on the basis of thermodynamic models. This highlights the need for refined mountain meteorological networks along elevational gradients to improve our holistic understanding of the processes that underlie local temperature lapse rates along mountain slopes. Furthermore, some studies have shown that changes in precipitation patterns can affect the range shifts of mountain species15,40, but historical data on precipitation patterns along mountain slopes are extremely scarce compared with data on temperature lapse rates. For that reason, establishing weather stations that also monitor precipitation patterns along mountain slopes remains key for assessing the large-scale effects of precipitation changes on mountainous organisms. We call for the establishment of networks to monitor climate change and its effects in mountain biodiversity hotspots, especially in mountains that are threatened by high velocities of isotherm shifts, such as those we have identified in our study.

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minee habit tracking Pomodoro timer from $69

minee habit tracking Pomodoro timer

If you’re looking to boost your productivity and manage your time more effectively, the minee Habit Tracking Pomodoro Timer might be just what you need. This tool combines the well-known Pomodoro Technique with modern technology to help you stay focused and achieve your goals.

The Pomodoro Technique is a time management method that breaks work into intervals, usually 25 minutes long, separated by short breaks. This approach is known for creating a sense of urgency and keeping your mind sharp, which can lead to increased productivity. The minee timer takes this method to the next level by using a visual display that shows time as color-coded segments. This makes it easy to see how much time you have left in your work session.

minee Pomodoro timer

First-come, first-served early bird pledges are now available for the interesting project from roughly $69 or £55 (depending on current exchange rates).With the minee timer, you can set and track your long-term goals, making sure that every Pomodoro interval brings you closer to achieving them. The device’s ability to log and analyze your focus patterns provides valuable insights that can help you optimize your daily schedule for maximum efficiency.

minee Pomodoro timer

In today’s world, where screens are a central part of our lives, it’s important to monitor screen time and sleep patterns. The minee timer helps with this by keeping track of these crucial factors, allowing you to make informed decisions that support your overall well-being.

The minee timer is designed to fit seamlessly into your daily life. It comes with wireless and Bluetooth capabilities, so you can connect it to your smartphone. The accompanying app lets you customize focus durations, activate a deep focus mode, and keep a detailed history of your tracking. The device itself can store up to 80 sessions, making your data easily accessible.

Assuming that the minee funding campaign successfully raises its required pledge goal and production progresses smoothly, worldwide shipping is expected to take place sometime around February 2024. To learn more about the minee habit tracking Pomodoro timer project audit the promotional video below.

2024 habit tracker

This timer is user-friendly, featuring a USB-C rechargeable battery and a simple start button to begin focus periods. It also stores data independently, ensuring that your information is safe even when not connected to another device.

One of the key benefits of the minee timer is its adaptability. It allows you to adjust work and rest intervals to suit your personal needs and can be used continuously for up to 1440 minutes. This makes it suitable for a wide range of tasks and projects.

minee Pomodoro timer app

How to use the Pomodoro Technique

To effectively use the Pomodoro Technique, follow these steps:

  • Choose a Task: Identify the task you want to work on. It can be anything that requires your focus.
  • Set a Timer for 25 Minutes: Use a timer to divide your work into 25-minute intervals, known as “Pomodoros.”
  • Work on the Task: Focus solely on the task during the 25-minute period. Minimize interruptions and distractions.
  • End Work When the Timer Rings: Once the timer goes off, stop working on the task, regardless of your progress.
  • Take a Short Break: After each Pomodoro, take a 5-minute break. This is crucial for maintaining mental agility and preventing fatigue.
  • Repeat the Process: After the short break, start another Pomodoro. Continue this cycle through your work period.
  • Take a Longer Break After Four Pomodoros: Once you complete four Pomodoros, take a longer break, typically 15-30 minutes. This helps to recharge and maintain high productivity throughout the day.

Remember, the key to the Pomodoro Technique is the balance between focused work sessions and frequent short breaks, which aids in sustaining concentration and staving off mental fatigue.

Overall, the minee Habit Tracking Pomodoro Timer is more than just a basic time management tool. It’s a comprehensive device designed to help you manage your goals, track your progress, and enhance your productivity. With its customizable features, analytical capabilities, and smartphone integration, the minee timer is a valuable resource for anyone looking to make the most of their potential.

For a complete list of all available early bird pledges, stretch goals, extra media and detailed specs for the habit tracking Pomodoro timer, jump over to the official minee crowd funding campaign page by navigating to the link below.

Source : Kickstarter

Disclaimer: Participating in Kickstarter campaigns involves inherent risks. While many projects successfully meet their goals, others may fail to deliver due to numerous challenges. Always conduct thorough research and exercise caution when pledging your hard-earned money.

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How to build a drone tracking radar

How to build a drone tracking radar from scratch

If you are looking for a new project to keep you busy for the next few months all weekend you might be interested in building your very own drone tracking radar. If you are interested you’ll be pleased to know that this guide and tutorial series created by John Kraft will help you start your project.

Imagine the enjoy of creating a drone tracking radar from scratch, a device that can pinpoint the location of drones in the sky. This is not just a theoretical exercise; it’s a practical project that you can undertake with the guidance of John Kraft, an expert in the field. This series of articles will take you through the intricate world of radar technology, giving you the tools and knowledge to build a fully functional radar system. You’ll learn about the emission and reflection of radio waves, and how these principles enable us to track objects in motion.

Radar technology is fascinating because it allows us to detect and locate objects using radio waves. As we embark on this journey, we’ll start by unraveling the core principles of radar operation. Understanding these principles is crucial for tracking drones, and you’ll gain valuable insights into how they work. The series will guide you through the complexities of radar, ensuring that you grasp the fundamental concepts before moving on to more advanced topics.

DIY drone tracking radar

Building a radar system requires careful selection and assembly of hardware. This guide will provide instructions on choosing the right components, with a focus on the Analog Devices hardware used in our demonstrations. However, we will also suggest alternative options to accommodate different budgets and resources. You’ll be taken through the assembly process step by step, learning about the role of each component in the radar system.

Here are some other articles you may find of interest on the subject of drones :

The ultimate goal of the series create by John Kraft  is to enable you to track a drone using a radar system that you’ve put together yourself. You’ll dive into the workings of Continuous Wave (CW) radar, which is capable of transmitting a constant signal for real-time tracking. You’ll also learn about modulation techniques that enhance the radar’s precision and enable it to differentiate between targets.

One of the challenges in radar technology is distinguishing the target from other objects, often referred to as clutter. This series will provide you with strategies for target recognition and clutter reduction. These techniques are essential for ensuring that your radar can focus on the drone, even when there are other objects in the vicinity.

As you become more proficient, you’ll learn about range-Doppler plots, which are crucial for tracking the position and speed of multiple targets simultaneously. This knowledge is vital for scenarios where you need to track several drones or navigate environments with numerous moving objects.

A comprehensive overview of the components that make up a radar system will give you a deeper understanding of the mechanics behind your build. You’ll learn about the differences between pulsed and CW radar, and discuss their respective advantages and applications.

For those embarking on this DIY project, the “Phaser” kit has been selected for its functionality and ease of use. You’ll receive a detailed explanation of the kit’s components and how they work together to create a functioning radar system.

Initially, the series will focus on non-beamforming radar techniques. However, it will also lay the groundwork for future discussions on beamforming, a method that can significantly improve the radar’s tracking capabilities by directing radio wave energy with precision.

This educational series is designed to encourage community learning and hands-on involvement. Whether you’re a hobbyist, a student, or an industry professional, you’ll gain valuable expertise in radar systems. You’ll enjoy the practical experience of constructing your own drone tracking radar. Prepare to dive into the fascinating world of radar technology as we guide you through this enlightening series.

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Samsung Health gets new Medications Tracking Feature

Samsung Health.

Samsung has announced that it is adding a new Medications tracking feature to Samsung Health for its Galaxy range of devices, this new featurer will allow uyou to track your medication and manage your health.

Upon entering the name of a select medication into Samsung Health, the Medications feature will provide users with detailed information that includes general descriptions as well as possible side effects. Adverse reactions that could occur from drug-to-drug interactions or if taken alongside certain food and substances such as caffeine and alcohol, are also provided. One example of this is, if a user is taking the prescription drug Simvastatin, Samsung Health will warn the user that the drug has been linked to serious side effects when combined with grapefruit juice. Users can even log the shape and color of their medications, allowing them to easily differentiate between the pills they are taking. Dosage, time of consumption and other details can also be added to avoid any potential confusion.

Users can set up alerts that remind them both when to take their medications and when they should consider refilling them. These alerts are fine-tuned to the individual user so the Medications feature is able to prioritize medications depending on their importance, with Samsung Health sending reminders ranging from “gentle” to “strong” depending on how important or urgent a given prescription is. For crucial medications, users can set a “strong” reminder that will display a full screen alert on their smartphone accompanied by a long tone. For supplements like vitamins, a simple pop-up reminder will appear that will not disturb the user. Galaxy Watch users will also receive reminders right on their wrist so they can stay on top of their medication schedules, even when away from their phones.

This could be a really useful feature and could help older users remember to take their medication when it is required, you can find out more details abotu the new Medications feature for Samsung Health at the link below.

Source Samsung

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How to stop your iPhone tracking your in iOS 17 (Video)

iPhone tracking

iPhone tracking with iOS 17 takes user consent to a new level with its App Tracking Transparency feature. By disabling the “Allow Apps to Request to Track” option, you effectively stop apps from monitoring your online behavior. This change not only enhances your digital privacy but also puts you in charge of your digital footprint.

Apple’s iOS 17 comes with a range of privacy features that are designed to protect your data and security features to protect your device, the video below from Payette Forward gives us a look at some of these features. There is a range of tips that you can follow to enhance your privacy settings on your iPhone.

App Tracking Transparency iOS 17 takes user consent to a new level with its App Tracking Transparency feature. By disabling the “Allow Apps to Request to Track” option, you effectively stop apps from monitoring your online behavior. This change not only enhances your digital privacy but also puts you in charge of your digital footprint.

App Privacy Report Imagine having a personal watchdog for your data. The App Privacy Report in iOS 17 does just that. It provides detailed insights into how frequently apps access your location, camera, and microphone, offering a clear picture of which apps might be overstepping their boundaries.

Revolutionized Password Settings With the introduction of an automatic verification code cleanup, iOS 17 makes managing passwords and passkeys more secure and efficient. This feature automatically removes used verification codes, minimizing the risk of unauthorized access.

Call and Message Blocking Unwanted calls and messages can be a nuisance. iOS 17’s enhanced blocking features act as a digital “Do Not Disturb” sign, giving you peace of mind and control over your communication channels.

Private Browsing in Safari For those who value discretion in internet usage, the private browsing mode in Safari is a godsend. It ensures your browsing history remains just that – private, not saved or shared across devices.

Clearing Browsing History Taking a step further, iOS 17 allows users to wipe their digital footprints clean by clearing their browsing history and website data in Safari. This feature is crucial for maintaining online privacy.

Location Services Management Balancing accessibility and privacy, iOS 17 lets users fine-tune which apps have access to their location. This customization ensures that your whereabouts are shared only with apps you trust.

Family and Location Sharing Controls The update enhances family sharing and location sharing management, letting you decide who makes it to your digital inner circle and who knows your location.

AirTag and Find My Accessory Security With concerns about the misuse of tracking devices like AirTags, iOS 17 provides guidelines on ensuring these devices aren’t being used to track you without consent.

Personal Safety Checklist The video highlights the importance of reviewing device access settings, Face ID/Touch ID configurations, and using lockdown mode in extreme security situations for maximum safety.

Safety Check Feature This feature in iOS 17 is pivotal for managing who has access to your information, allowing users to reset settings linked to their Apple ID for enhanced privacy.

Restoring to Factory Settings As a nuclear option, resetting your iPhone to factory settings can ensure complete privacy. However, it’s crucial to be mindful of the backups you restore from, as they might contain privacy-compromising data.

Summary
iOS 17 stands as a testament to Apple’s commitment to user privacy. By leveraging these features, you can significantly reduce your iPhone’s ability to track your activities, giving you not only enhanced security but also peace of mind in this digital age. Remember, in the realm of privacy, knowledge is power, and iOS 17 hands that power back to you, the user.

Source & Image Credit: Payette Forward

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Tracking the Digital Footprints: Investigating the Traceability of Stolen Cryptocurrencies

In the exciting realm of cryptocurrencies, where wealth can shift instantly, there’s a darker side hiding just out of sight. The theft of digital coins is sadly common, leading many to wonder if these vanished assets can be tracked down and reclaimed. Today, we’re going to delve into the intriguing realm of how crypto forensics companies track digital footprints to investigate the traceability of stolen cryptocurrencies.

The Cryptocurrency Crime Scene

Picture this: you wake up one fine morning, ready to check your crypto wallet, only to find it’s empty. Panic sets in, and you realize that you’ve fallen victim to a cryptocurrency theft. You’re not alone; cryptocurrency thefts have been on the rise. But here’s the twist – cryptocurrencies operate on a decentralized ledger called blockchain, which means every transaction is recorded. Can this help track down the culprits?

Understanding the Blockchain

Before we dive into the tracking process, let’s understand what makes it all possible – the blockchain. Think of it as a digital ledger, an open book where every cryptocurrency transaction is written down. These transactions are grouped into blocks and linked together in a chain, creating a secure and transparent record. But how can we use this to our advantage in tracking stolen cryptocurrencies?

The Tracking Process: Step by Step

Gather Information:

The first step in tracking stolen cryptocurrencies is gathering information about the theft. This includes details like the time of the theft, the wallet address involved, and any suspicious activities leading up to it.

Identify the Wallet Address:

Every cryptocurrency transaction involves sender and receiver wallet addresses. These addresses are alphanumeric strings unique to each wallet. To start tracking, you’ll need to identify the wallet address that received your stolen crypto.

Blockchain Analysis:

Here’s where the magic happens. The blockchain is public, meaning anyone can view transactions. You can use blockchain explorers like Etherscan (for Ethereum) or Blockchair (for Bitcoin) to search for the wallet address and trace its transactions.

Follow the Trail:

Once you’ve identified the thief’s wallet address, you can start tracing their digital footprints. Follow the trail of transactions to see where your stolen cryptocurrency went next. It might have changed hands several times.

Exchange Investigations:

Cryptocurrency thieves often try to cash out on exchanges. Keep an eye on popular exchanges for any deposits matching the stolen amount. Exchanges are required to follow KYC (Know Your Customer) procedures, making it harder for thieves to remain anonymous.

Law Enforcement Involvement:

If you’ve traced the stolen crypto to an exchange or identified the thief, it’s time to get law enforcement involved. Provide them with all the evidence you’ve gathered. They may work with international agencies to recover your funds.

Challenges in Tracking

While the process might sound straightforward, tracking stolen cryptocurrencies is no walk in the park. Here are some challenges you might encounter along the way:

Anonymity: Cryptocurrency transactions can be pseudonymous, meaning wallet addresses are not directly tied to real-world identities. This anonymity can make it difficult to pinpoint the culprits.

Mixers and Tumblers: Some thieves use mixing services or tumblers to obfuscate the trail of stolen cryptocurrencies. These services shuffle funds between multiple addresses, making tracking more challenging.

Jurisdictional Issues: Cryptocurrency operates in a borderless digital realm. Tracking and recovering stolen funds might involve navigating complex jurisdictional issues, especially if the thief is in another country.

Hacking Techniques: Sophisticated hackers might employ various techniques to cover their tracks, including using stolen or hacked wallets to launder the stolen crypto.

Success Stories

Mt. Gox Hack: In 2014, Mt. Gox, one of the largest cryptocurrency exchanges at the time, suffered a massive hack resulting in the loss of 850,000 bitcoins. Over the years, investigators managed to trace a portion of these bitcoins to various wallet addresses. While not all the stolen funds were recovered, this case demonstrates the potential for tracking stolen crypto.

Twitter Hack: In 2020, a high-profile Twitter hack saw several celebrity accounts promoting a bitcoin scam. While the initial investigation focused on the hack itself, cryptocurrency forensic experts were able to trace the stolen bitcoins to different addresses. A teenager was later arrested and charged with the hack.

Preventing Crypto Theft

While it’s intriguing to explore the world of tracking stolen cryptocurrencies, prevention is always better than cure. Here are some tips to keep your digital assets safe:

Use Physical Wallets: Hardware wallets are tangible devices that keep your digital currencies offline, shielding them from internet threats.

Turn On Two-Step Verification (2SV): Boosting your exchange and wallet security with an additional protective step can keep intruders at bay.

Stay Informed: Keep yourself updated on the latest security threats and scams in the crypto world. Knowledge is your best defense.

Beware of Phishing: Be cautious of phishing emails and websites that aim to steal your login credentials. Always double-check URLs and email sources.

Secure Your Private Keys: Your private keys are the keys to your cryptocurrency kingdom. Store them in a safe place and never share them with anyone.

Consider Insurance: Some cryptocurrency exchanges and custodians offer insurance coverage for digital assets. It’s worth exploring such options for added peace of mind.

Conclusion

The world of cryptocurrencies is a thrilling one, but it’s not without risks. Prevention remains the best strategy. By adopting secure practices and staying vigilant, you can significantly reduce the risk of falling victim to crypto theft. Remember, in this digital frontier, it’s not just about the hunt for stolen treasure but also about safeguarding your own.