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Shares of engineering and design software company Autodesk (ADSK 1.10%) declined by 17.2% in June, according to data provided by S&P Global Market Intelligence. The move comes in a weak month for industrial software companies, with the market rife with worries over rising interest rates' impact on economic growth. Slowing growth is an issue for Autodesk because it makes software for the design-and-build (architecture, engineering, and construction) and design-and-make (manufacturing) end markets. As a result, when its customers see more challenging times ahead, they typically cut back on development spending.

The market speculating that economic growth will slow doesn't mean it will necessarily happen. However, it's understandable if investors sell off Autodesk on these concerns. After all, the company could be seen as at risk due to management lowering expectations for free cash flow (FCF) in recent years. For example, it previously told investors to expect $2.4 billion in FCF in fiscal 2023 (the company's 2023 second quarter will end this July) and investors priced that in with assumptions of more growth to come.

However, back in September, management told investors to expect lower FCF in fiscal 2024 because of a change in how it bills customers resulting in more long-term cash flow with less upfront.

That's fair enough, but Autodesk missed its original FCF forecast for fiscal 2022 of $1.575 billion to $1.65, reporting just $1.48 billion.

Fast-forward to fiscal 2023 (most of which is in calendar 2022), and management shaved its long-held target of $2.4 billion in FCF toward a new range of $2.13 billion to $2.21 billion due to a deteriorating economic environment and adverse foreign exchange movements.

Come the first quarter of fiscal 2023 and management cut fiscal FCF 2023 guidance to a range of $2 billion to $2.08 billion.

Given this recent history of guidance cuts, the market is obviously bracing itself for more of the same in the coming (second quarter of 2023) results.

It's never good news when a company lowers guidance. Still, it's important to keep a clear head and reflect that Autodesk is still an exciting growth business with plenty of long-term growth ahead. Moreover, its current market cap is just $37.8 billion. So even if Autodesk lowers full-year FCF guidance by, say, $200 million to around $1.8 billion, it will trade on a price-to-FCF multiple of 21 times FCF -- an excellent valuation for a growth company.

Read the rest here:
Why Shares of Autodesk Slumped in June - The Motley Fool

The conducted research showed that adopting four variants in the models of the activity of agents in the selected test powiats enabled the obtainment of statistically significant results. It also enabled the precise determination of the impact of the level of restrictions on the numbers of cases, hospitalizations, and deaths. By using a geographic information system and multiagent modeling in the modeling process and a detailed database of topographic objects, it was possible to simultaneously investigate when and where an infection occurs and determine the impact of spatial location and land cover on the development of a pandemic.

To ensure the credibility of the research, the base case model was calibrated independently for all three test powiats based on sanitary and epidemiological data of the number of cases on individual days. The proposed model has many parameters set individually for each powiat, for instance, housing and population density, the residents' level of mobility, and the level of public transport use. Since each of these parameters may affect the accuracy of calculations in various ways, it was necessary to recalibrate the model for each powiat using source epidemiological data.

As an objective function during calibration, we assumed the minimization of the difference between the number of infected according to the real data and the model's result after 1 month of simulation. During the calibration, the assumption was that the only variable of the objective function would be the probability coefficient of infection during agent interaction. The adopted method was the bisection algorithm.

For the Godap powiat, the accuracy (MPE error) was 1.64%; for Ropczyce-Sdziszw, the accuracy was 0.68%; and for Pruszkw, the accuracy was 0.64%. Due to the models high computational complexity, the calibration process was nontrivial; thus, the researchers did not choose complete automation of the process. The probability coefficient of infection during agent contact was calibrated. Because of the nature of the model, this coefficient required only minor adjustments to calibrate to actual data. However, depending on the powiat, the calibration process took several days to 2 weeks. Tables 3, 4 and Figs.5, 6, 7, 8 and 9 present the numerical results obtained by the multiagent model, while Figs.6, 8 and 10 show the spatial distribution of the number of cases, their locations, and differences between individual models.

Number of symptomatic infected agents in the Ropczycko-Sdziszowski powiat.

Spatial distribution of the number of cases, their locations, and the differences between individual models in the Ropczyce-Sdziszw powiat. (a) Ropczyce-Sdziszw powiat: land cover. (b) The number of cases in 4 models in the gminas in relation to the number of inhabitants of these gminas. (c) The number of cases in a 11km grid (pie chart) and the percentage of cases (intensity of red color): the base case model. (d) The number of cases in a 11km grid (pie chart) and the percentage of cases (intensity of red color): the no restrictions model. (e) The location of cases (black dots) and the density of cases by place of infection: the base case model. (f) The location of cases (black dots) and the density of cases by place of infection: the no restrictions model. (g) The Pearson correlation coefficient between the place of infection and the number of inhabitants in the area (white<0.5, yellow<0.75, red0.75): the base case model. (h) The Pearson correlation coefficient between the place of infection and the number of inhabitants in the area (white<0.5, yellow<0.75, red0.75): the no restrictions model (developed by the authors in QGIS ver. 3.22.5).

Number of symptomatic infected agents in the Pruszkw powiat.

Spatial distributions of the number of cases, their locations, and differences between the individual models in the Pruszkw powiat. (a) Pruszkw powiat: land cover. (b) The number of cases in the 4 models in the gminas in relation to the number of inhabitants of these gminas. (c) The number of cases in a 11km grid (pie chart) and the percentage of cases (intensity of red color): Stringency Index+20 model. (d) The number of cases in a 11km grid (pie chart) and the percentage of cases (intensity of red color): No restrictions model. (e) The location of cases (black dots) and the density of cases by place of infection: Stringency Index+20 model. (f) The location of cases (black dots) and the density of cases by place of infection: No restrictions model. (g) The Pearson correlation coefficient between the place of infection (developed by the authors in QGIS ver. 3.22.5).

Number of symptomatic infected agents in the Godap powiat.

Spatial distributions of the numbers of cases, their locations, and the differences between the individual models in the Godap powiat. (a) Godap powiat: land cover. (b) The number of cases in the 4 models in the gminas in relation to the number of inhabitants of these gminas. (c) The number of cases in a 11km grid (pie chart) and the percentage of cases (intensity of red color): Stringency Index 20 model. (d) The number of cases in a 11km grid (pie chart) and the percentage of cases (intensity of red color): No restrictions model. (e) The location of cases (black dots) and the density of cases by the place of infection: Stringency Index 20 model. (f) The location of cases (black dots) and the density of cases by place of infection: No restrictions model. (g) The Pearson correlation coefficient between place of infection and the number of inhabitants of the area (white<0.5, yellow<0.75, red0.75): Stringency Index 20 model. (h) The Pearson correlation coefficient between the place of infection and the number of inhabitants of the area (white<0.5, yellow<0.75, red0.75): No restrictions model (developed by the authors in QGIS ver. 3.22.5).

It is worth noting that the process of multiagent modeling, which included tens of thousands of agents interacting with each other in a virtual topographic space with a level of detail (LoD) corresponding to analog maps at a 1:10,000 scale, was lengthy and computationally demanding. The calculations were performed in the CENAGIS computing cluster with 16 Intel (R) Xeon (R) Silver 4216 CPU @ 2.10GHz processors with 128GB RAM. The calculations for a single case for the base variant took 5h 25m for the Godap powiat, 10h 34m for the Ropczyce-Sdziszw powiat, and 33h 45m for the Pruszkw powiat. It should be stressed that the spatial interpretation of the obtained results required complex SQL querying of the database with spatial operators. To highlight various aspects of the obtained results for individual models, the authors provide a summary for entire powiats, gminas constituting powiats, and 1 km2 units, as used in the official statistics. Additionally, to analyze the spatial relationship between individual parameters, e.g., the number of people living in a given region and the number of cases in this area, the authors used proprietary tools to determine the Pearson correlation coefficient in a moving (circular) window of a given size. The authors obtained discrete results (e.g., point information on the percentage of incidence in a 1 km2 area) and interpolated them to show a continuous statistical surface illustrating the spatial distribution of individual phenomena (Figs.6, 8 and 10).

When analyzing the data in Table 3, it should be emphasized that due to the specificity of the multiagent model used, the analysis of "exposed" cases is of crucial importance. "Exposed" means contact with the potential to cause infection; some infected agents will develop symptoms characteristic of infection only after the incubation period (median incubation period of 5.1days27). This is important because not only the moment when the symptoms arise but also the moment of infection are considered, and the duration between such events may be several to several dozen days. Naturally, the numbers of symptomatic infected, asymptomatic infected, hospitalized, and deceased agents are also essential for analysis. The results (Table above) indicate that changing the level of restrictions significantly affects the number of cases. Obviously, due to the different number of inhabitants in individual powiats, it is crucial to compare the values between particular models. In almost all cases, the incidence rate in the PLUS model is several percentage points lower than that in the base case. The opposite was observed for the MINUS model with a lower level of restrictions. The no restrictions model showed very significant differences, sometimes exceeding the reference value by threefold. The results show the importance of restrictions such as social distancing, remote learning, movement limitations, and mask use.

It is interesting to compare the spatial distribution of the "exposed" locations. While the analysis indicates the role of the level of restrictions, it also reveals the impact of topography, building density, recreational areas, quality of public transportation, and resident mobility. The three analyzed powiats represent individual regions of Poland and enable the determination of the impact of topographic factors on the course of the pandemic. As the table below (belowTable 4) shows, the inhabitants of the Godap powiat have a level of mobility close to zero, and there is almost no public transportation. As a result, the number of cases related to traveling does not exceed 0.3%. In the Pruszkw powiat, where a approximately 12% of inhabitants commute to the capital every day (mainly by suburban railway), the level of infection connected to public transportation reaches 20.1% in the PLUS model. In the MINUS model for the Ropczyce-Sdziszw powiat, which has good connections to the city of Rzeszw, the percentage of cases related to public transportation exceeds 24%.

Workplaces account for the highest percentages in the number of cases:

73.4% in the Godap powiat (the PLUS model),

65.2% for the base case in the Pruszkw powiat,

66.2% in the Ropczyce-Sdziszw powiat (the PLUS model).

It should be emphasized that depending on the model, the number of people who become infected at their workplace differ significantly. Mobility restrictions and requisite remote work may increase the number of infections related to recreational areas (city parks and forests). In the Pruszkw powiat, characterized by a relatively small area of parks, applying more significant restrictions (the PLUS model) causes a percentage decrease in the number of cases in particular places from 6% (buildings) to 75% (clinics), except for recreational facilities, which are associated with an increase in the number of cases (compared to the base case) by 45% (!). What is equally significant is the percentage increase in the number of cases related to commercial facilities in this powiat in the MINUS model (156% of the base case model) and the no restrictions model (970% of the reference value), demonstrating the vital role of shops and malls in spreading COVID-19 when there are no social distancing restrictions in place.

The conducted analysis also shows that infection occurs in residential buildings in nearly 10% of cases. Introducing restrictions leads to almost complete elimination of cases related to public health care facilities (0.1% in the PLUS model in the Pruszkw powiat), with very low absolute values (5 people in the Ropczyce-Sdziszw powiat in the PLUS model and 13 people in the Pruszkw powiat in the same analytical variant). Closing schools and transitioning to remote learning are also of great importance; lack of such restrictions resulted in 43 infected students in the Godap powiat, 47 in the Ropczyce-Sdziszw powiat, and 635 in the Pruszkw powiat, with 0, 7, and 91 corresponding values in the base model and 0, 3, and 25 in the PLUS model, respectively.

The maps show the results of the no restrictions model and one of the models including the spatial distancing policy (Figs.6, 8 and 10), illustrating the differences resulting from topographic or demographic differentiation and from adopting a specific restrictive policy for each of the three analyzed powiats. For the Ropczyce-Sdziszw powiat, the maps show the base case. For the Pruszkw powiat, the maps show the Stringency Index+20 (the PLUS variant). For the Godap powiat, the maps show the Stringency Index 20 (the MINUS variant). Such an approach enables the analysis of the spatial differentiation of the development of the COVID-19 pandemic and the verification of research hypotheses indicating the crucial role of restrictive policies.

The no restrictions model for this powiat had the highest increase in the total number of infections (326% compared to the base model) out of all the analyzed powiats. The greatest increase in the number of infections occurred in recreational areas (as high as 1594%). The bar chart in Fig.6b shows the absolute values of the exposed agents in the individual models in relation to the number of inhabitants of individual gminas comprising the powiat. The values related to the no restrictions model are dominant; the bar sizes indicate two towns, Ropczyce and Sdziszw Maopolski, with the highest number of cases.

The maps in Fig.6c,d show a different approach: the number of cases in individual 1 km2 units (the size of the pie chart) and the percentage of people who fell ill in a given areal unit in relation to the number of inhabitants of a given square. One should note that this analysis shows the number of infected people living in a given square unit, regardless of the place of infection. Comparison of the base case and the no restrictions models shows considerable differentiation in the disease prevalence and incidence. In the absence of restrictions, over 40% of the inhabitants who fell ill occupied approximately 1/3 of the powiat's area. In the reference model (base case), the value exceeds 15% for only a dozen areal units. Black dots on the maps in Fig.6e,f show where infections occurred, while the statistical surface layer indicates the number of cases in a given region, represented by varying intensities of red. This map also shows the primary role of areas with dense industrial or residential development in the progression of the pandemic. The maps (Fig.6g,h) show a linear Pearson correlation between where infections occurred and the number of inhabitants in that area. The correlation coefficient value is calculated in a moving window with a radius of 2.5km, which serves as a spatial filter. In a given areal unit, the values of the number of cases and the number of inhabitants in the individual squares of the official statistical grid are analyzed. The obtained point values (discrete) are then interpolated to a continuous statistical surface. White indicates no correlation, yellow indicates a weak linear correlation (Pearson's correlation coefficient of 0.5), and red indicates a strong correlation (correlation coefficient>0.75). The strongest spatial correlations occur in densely populated areas where many people fall ill. When analyzing the obtained results, the level of spatial generalization of the results should be considered; each dot on the map represents a value assigned to its circular surroundings with a radius of 2500m (nearly 20 km2).

The map (Fig.6) reveals interesting conclusions: in both models, infection cases in Sdziszw Maopolski are more concentrated, while infection cases in Ropczyce are more dispersed. One way to explain this is that Sdziszw Maopolski is smaller and less populated than Ropczyce, but the population density is double (Sdziszw Maopolski: 838 people/km2, Ropczyce: 336 people/km2), with a railway station in its center.

The Pruszkw powiat is inhabited by the largest number of people, with high labor mobility. A significant number of the inhabitants commute to work in neighboring areas (mainly Warsaw) via public transportation (Warsaw Commuter Railway, train); therefore, public transportation and work constitute the most significant infection sources. Additionally, in this powiat, the highest percentage increase in infections in all the analyzed models occurred further from the house (more than 3km); accordingly, the proportion of people who become infected at home is lower than those in the other analyzed powiats. In the no restrictions model, the highest increase in infections was recorded in recreational areas (1219% of the base case model value), while in the MINUS model, the highest increase in infections was recorded in trade-related areas. On the other hand, in the PLUS model, despite a significant decrease in the number of infections compared to the base variant (by an average of 21.4%), there was an increase in the number of infections in recreational areas (45%). The reason is the increase in professional restrictions (remote work and learning) and the population's willingness to visit open natural areas.

As seen in the maps in Fig.8, in the case of Pruszkw powiat, the authors present the results of the no restrictions model and the most restrictive PLUS model. The pie charts show that the highest number of cases occur in the main urban centers, Pruszkw and Piastw, and more than half of the Pruszkw powiat contains areas in which over 50% of residents will fall ill in the no restrictions model. Moreover, in the no restrictions model, regions with high infection densities are strongly correlated with places with high population densities (Brwinw, Raszyn, Nadarzyn, Michaowice). Notably, the largest numbers of railway stations are in the gminas of Pruszkw, Piastw, and Brwinw. Interestingly, there were many infections in the former two gminas, while the latter gmina (Brwinw) had the smallest number of infections. Brwinw has a low degree of industrialization, and the existing industrial centers (mainly warehouses) are located outside the city.

Comparison of the maps in Fig.8 indicates that the model in which the level of safety was increased by 20% in relation to the restrictions implemented in Poland (the PLUS variant) showed a significant reduction in the number of cases and complete elimination in areas with an incidence rate higher than 50%. The no restrictions model has a stronger correlation between the number of cases in a given area and the population density, which is evident in the eastern (Michaowice and Raszyn), western (Brwinw), and southern (Nadarzyn) parts of the powiat. In the PLUS model, the correlation is almost zero, while it exceeds 0.5 in the no restrictions model.

The Godap powiat has the smallest population and the lowest level of resident mobility; most residents work on their own farms. Consequently, this powiat had the lowest total number of infections in all the analyzed scenarios. In relation to the base model, there is an increase in the number of infections in the no restrictions model by 204% (the smallest increase among all the analyzed powiats). Contrary to the other gminas, there is no significant increase in the number of infections in recreational areas (0.5%). The MINUS model shows the smallest increase in infections (8.3% in total) compared to the base model. On the other hand, only in the Godap powiat is the PLUS model characterized by a slight increase (0.4% on average) in infections (in the other powiats, the total number of infections in this model decrease).

The conducted analyses show that among the three gminas that make up the Godap powiat, a significant increase in the number of cases occurs mainly in the town of Godap (Fig.10b). It should be emphasized that the comparison of the modeling results in Fig.10 relates to the analysis of two models with low levels of restrictions: the Stringency Index -20 (MINUS) and the no restrictions models. In both models, the primary infection outbreaks occur in the powiat's capital, where infections occur at home, at work, in shops and in schools. Due to the agricultural nature of this powiat, characterized by scattered housing developments and low resident mobility, the overall number of cases is relatively low, even in the model without restrictions. However, the percentage of infections in some units of the 1 km2 statistical grid exceeds 40%, indicating significant roles of topography, scattered single-family housing, the level of economic development of the region, and the mobility of residents over the course of the pandemic.

See the rest here:
Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland | Scientific Reports - Nature.com

New Jersey, United States,-Mr Accuracy Reportspublished new research on GlobalCAD Libraries Softwarecovering micro level of analysis by competitors and key business segments (2022-2029). The Global CAD Libraries Software explores comprehensive study on various segments like opportunities, size, development, innovation, sales and overall growth of major players. The research is carried out on primary and secondary statistics sources and it consists both qualitative and quantitative detailing.

Some of the Major Key players profiled in the study are3D ContentCentral, PARTsolutions, Thomas, GrabCAD Library, CAD Blocks Free, HALFEN, CUI, 3D Warehouse, 3DModelSpace, IntrinSIM, TraceParts

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Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global CAD Libraries Software market. This report is a consolidation of primary and secondary research, which provides market size, share, dynamics, and forecast for various segments and sub-segments considering the macro and micro environmental factors. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market.

Global CAD Libraries Software Market Segmentation:

CAD Libraries Software Segmentation by Type:

Web-based, On-premise.

CAD Libraries Software Segmentation by Application:

Large Enterprises, SMEs

Key market aspects are illuminated in the report:

Executive Summary:It covers a summary of the most vital studies, the Global CAD Libraries Software market increasing rate, modest circumstances, market trends, drivers and problems as well as macroscopic pointers.

Study Analysis:Covers major companies, vital market segments, the scope of the products offered in the Global CAD Libraries Software market, the years measured and the study points.

Company Profile:Each Firm well-defined in this segment is screened based on a products, value, SWOT analysis, their ability and other significant features.

Manufacture by region:This Global CAD Libraries Software report offers data on imports and exports, sales, production and key companies in all studied regional markets

Market Segmentation: By Geographical Analysis

The Middle East and Africa(GCC Countries and Egypt)North America(the United States, Mexico, and Canada)South America(Brazil etc.)Europe(Turkey, Germany, Russia UK, Italy, France, etc.)Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The cost analysis of the Global CAD Libraries Software Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Key questions answered in the report include:

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Table of Contents

Global CAD Libraries Software Market Research Report 2022 2029

Chapter 1 CAD Libraries Software Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global CAD Libraries Software Market Forecast

If you have any special requirements, please let us know and we will offer you the report as you want. you can also get individual chapter wise section or region wise report version like North America, Europe or Asia.

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CAD Libraries Software Market is expected to reach a value of USD 1,721,231.5 Million By 2027- 3D ContentCentral, PARTsolutions, Thomas, GrabCAD...

At a glanceExperts RatingPros

ShadowMaker is fast, easy, reliable backup and the free version nicely takes care of the basics. A Pro version with more features is available via subscription and perpetual licenses.

$79

MiniTool ShadowMaker, a first-rate backup program with a competent free version has evolved quite a bit since our look at version 2.0. Its also now available as a subscription or a with a perpetual license. A rather pricey $79 three-seat, perpetual license is up $50 from the last time we looked. Theres a lot of competition at this price point. Just saying.

Note: This review is part of ourroundup of thebest Windows backup software.Go there for details about competing products and how we tested them.

ShadowMaker 3.6 occupies approximately 225MB of disk space, and is a particularly clean install, leaving only a single process running in the backgroundits scheduler. The interface is on the dark side and uses the squarish Zune design metaphor of Windows 8/10.

All major categories of functions are available from the main page, and the program is largely intuitive if youre even somewhat familiar with the backup process. I could argue some of the labels and language, but that would be nigglingthe program steps you through most operations in a logical, friendly manner.

As I hinted at, ShadowMaker is one of the more competent backup freebies out there. For basic imaging, file and folder copy, folder sync, and disk cloning it will get the job done quickly and easily. The major omission is disaster recovery, unless you count a cloned disk that you can swap in for a failed drive. Thats certainly a viable alternative. Otherwise, youll need to reinstall Windows then run ShadowMaker Free to get your data back.

To be honest, on those super-rare occasions Windows has gone belly-up on me, Ive always taken advantage of the opportunity to get rid of all the accumulated junk with a fresh install. Just a thought.

There are several additional features available in the $79 ($6 a month/$36 a year) Pro Ultimate version. First and foremost is the Windows PE-based disaster recovery media. PE allows the program to operate just like the installed version. Other additions include support for command-line backups, incremental and differential backups, automatic culling, network PXE booting, as well as SSL encryption.

But my favorite pay feature is backup of remote computers. Enter the IP address (see below) of the remote computer running ShadowMaker, the program reboots, and all the disks, partitions, and files from the remote computer are now available as backup choices. You can access and back them up using the same wizards you use to back up the local machine.

This means I can keep my lazy toukus at my main machine, and back up any other PC on the network. Sweet, and possibly the reason youll want to pay for the three-seat licenses. Now if only ShadowMaker were available for the Mac and Linux.

ShadowMaker 3.6 was exceptionally fast at all normal operations: creating images, syncing folders, mounting images, etc. It was unbelievably fast backing up the main partition (with 75GB of stuff) on my test rig. Indeed, I thought it was failing until I mounted the images and checked the result. The compression rate was quite high as well, with the backup weighing in at a mere 18GB.

On the other hand, the clone disk function lacks any resizing/restore to fit capability, and even when I provided an identically-sized SSD, it balked. The process with ShadowMaker requires a larger-capacity disk.

To be fair, not restoring or cloning to smaller-capacity drives is a common issue (Windows own backup wont do it). But this was the first time Ive seen a like-sized drive disqualified. If you want to adjust sizes of partitions during backup or restore, look to the Mac daddy of imaging: R-Drive Image.

Also, when youre backing up an entire disk, make sure youve manually selected all the partitions. ShadowMaker wont select all of them by default, even omitting the main data partition in one case.

The free version of ShadowMaker is a very competent free backup program with few peers at the price (there are ads). However, when it comes to paying for ShadowMaker.

I can understand (if not like) subscriptions for software that is continually evolving and acquiring new features. But its difficult to fathom the logic in monthly payments for backup software thats largely feature complete. If you only use it once a year, a month of rental could make sense, and the free version can access the images it created should you need to restore in the future. Or you could rent it again when you need to restore.

But largely Im left weighing the value of the $79 (three-seat license) ShadowMaker Pro Ultimate. Theres stiff competition from products such as Acronis Cyber Protect Home Office and the aforementioned R-Drive Image, which cost less. I love ShadowMakers remote backup trick and its a possible deal-maker, but the program is still a hard sell at the price.

Irrespective of monetary outlay, ShadowMaker has matured nicely since our previous looks. It was very reliable in testing and its very fast. Download the 30-day trial of Pro Ultimate and give it a whirl. It might just suit your needs.

See original here:
ShadowMaker 3.6 review: Fast imaging, sync, and disaster recovery - PCWorld

Temporal analysis of megadisturbance impacts in the AHNP

Before 2016, very little impact was detected by the GFCC product (Table 1). Hernndez Rodrguez and Cruz Flores15 likewise reported very small spectral variations between 2000 and 2010 over the National Park, suggesting little impacts on the vegetation cover. In the 20002015 time period, seven tropical storms were registered whose track passed through the earternmost region of Cuba (Fig.6,27), including Tropical Storm Isaac (2012) whose trajectory crossed the Park. Maximum wind speed registered for Tropical Storm Isaac (2012) was about 50 knots, significantly lower than the 112 knots of Hurricane Matthew.

Track of the tropical storms registered in the 20002017 time period, easternmost region of the Republic of Cuba27. The Alejandro de Humboldt National Park is highlighted in red colour. The map was created using ArcGIS 10.7 software (https://support.esri.com/en/products/desktop/arcgis-desktop/arcmap/10-7-1).

Two tropical storms in this region reached hurricane category in the 20002015 time period: Hurricane Ike (2008, 111 knots) passed about 60km North of the National Park area, and Hurricane Sandy (2012, 100 knots) passed about 90km West of the National Park area (Fig.6). Wind speeds of these meteors in the National Park area were typical of tropical storms. The megadisturbance impacted area estimated for 2008 and 2012 were only 21.6ha and 9.8ha, respectively. A thorough visual inspection of high resolution imagery in the GoogleEarth archive confirmed this low visible impact in 2008 and 2012.

By contrast, Hurricane Matthew passed about 30km East of the National Park area (Fig.7,28), with close to maximum hurricane force wind speeds affecting the Easternmost part of the National Park.

Hurricane Matthews track and distribution of wind fields during its passage over Eastern Cuba on 5th of October 201616,28. Most of the Park area is within Hurricane force windfield. The map was created using ArcGIS 10.7 software (https://support.esri.com/en/products/desktop/arcgis-desktop/arcmap/10-7-1).

The significative decrease in spectral indices in the AHNP in 20162017 with respect to 20002015 (Table 4) is in contrast with the general increase in NDVI found by Cruz Flores et al.29 accross the national protected areas between 20112015 and 20162018. The latter study was based on NDVI maps at 300m spatial resolution, and was not meant to capture local trends. In our approach, the GFCC and NDVI products at 30m spatial resolution provide the means of quantifying megadisturbance at the scale of National Protected Areas.

In 2016, the estimation of the area impacted using NDVI and GFCC is similar (12.6% and 11.8% of the AHNP total area, respectively). NDVI tends to capture temporary vegetation impacts whereas GFCC, based on the wetness, brightness and greenness indices, should be sensitive to longer lasting impacts11. This similarity suggests that megadisturbance occurred in 2016 was mainly associated with defoliation caused by the Hurricane Matthew impact16, remaining after three month (early OctoberDecember 2016). A depression in NDVI three month after Hurricane Mara was likewise reported in Puerto Rico by Hu and Smith8.

By contrast, in 2017, no major extreme event occurred, and yet, megadisturbance was detected in an additional 1276 hectares (1.8% of the National Park area). Perturbance, possibly related to prolonged drought, was registered in Cuban national protected areas, including AHPN, before 201613,30 and in 201631. The degradation detected in the "forest loss" product in 2017 could relate to a long lasting effect of the megadisturbance combining Hurricane Matthew and prolonged droughts in previous years. This interesting finding could corroborate de Beurs et al.'s hypothesis12 that studies based on the change detection of appropriate remote sensing spectral indices at medium resolution (1030m) may detect persistent defoliation and degradation following the combination of several extreme events (in this case a hurricane event and previous prolonged droughts).

Hurricane Matthew passed a few kilometers East of the Alejandro de Humboldt National Park in the Atlantic ocean (Fig.7,28). The large area impacted in the easternmost part of the AHNP (especially in the Toa watershed and along the coast) seems largely related to the high wind speeds and the strength of the vortices in the vicinity of the hurricane trajectory.

Additionally, according to the exposition map in Fig.8, forests on slopes with exposition near to the South-east were most vulnerable to impacts, presumably because the general direction of Hurricane Matthew was from South-east to North-west. For example, slopes with predominant exposition to the South in the Jaguani watershed were particularly impacted (Fig.8). By contrast, few forests with exposition to the North were impacted.

Predominant slope expositions in megadisturbance impacted forests (mapped in Fig.3) of the Alejandro de Humboldt National Park. The map was created using ArcGIS 10.7 software (https://support.esri.com/en/products/desktop/arcgis-desktop/arcmap/10-7-1).

Temporary impact on lowland rainforest areas is illustrated in Fig.9 using Sentinel-2 colour composites and NDVI images before and after the Hurricane Matthew event. The NDVI map indicates redensification of vegetation 15months later in these areas (Fig.9).

Sentinel-2 colour composites and NDVI images of the Jaguan watershed (a tributary of the Toa river), Alejandro de Humboldt National Park, before and after the Hurricane Matthew event in October 2016.

Prolonged impact on the coastal swamp forest is illustrated in Fig.10 using Sentinel-2 colour composites and NDVI images before the Hurricane Matthew event and in December 2018 (15 Months after the event). On the right hand side of the images, persistent defoliation was observed in December 2018 in patches that showed dense vegetation in September 2016, just before the event. The local increase in sea level and the scattering of saline water during the hurricane event may both have caused high mortality of trees in parts of the coastal swamp forest.

Sentinel-2 colour composites and NDVI images of a coastal area (between Taco Bay to the Southeast, and the Jaragu Point to the Northwest), Alejandro de Humboldt National Park, before and after the Hurricane Matthew event in October 2016.

NDVI distribution has been statistically documented at the national level in the Republic of Cuba12,32 or national park level15,29. Additionally, remotely sensed spectral indices have been assessed in Cuba for forest loss versus forest persistence in forest management areas33. Based on a similar use of spectral indices, our study provides the first methodology for degradation assessment in national protected areas in the Caribbean. Our methodology and cartographic dataset could enrich the impact assessment framework of local forest management companies in charge of national protected areas in Cuba (e.g. in Baracoa for AHNP34).

In our study, the area impacted by megadisturbance was estimated applying good practices of area change estimation22,23 to the GFCC product. Accordingly, the "forest loss" layer only slightly underestimated (by 9%) the megadisturbed area (11,110 hectares). This result contrasts with results of studies on the forest loss layer over areas of anthropic deforestation, degradation and selective logging sites where much more underestimation was registered35,36,37. Megadisturbance events (e.g. hurricanes, prolonged droughts) may affect forests on a much larger extent and more homogeneously than anthropic intervention, which makes the GFCC forest loss layer more accurate at estimating impacted areas in the case of megadisturbance.

Recent degradation studies in the neotropical forests do not make use of the GFCC product to estimate degradation because most disturbance is due to shifting cultivation38. By contrast, in the case of little anthropic disturbance, our study suggests that the GFCC product can be useful for the assessment of megadisturbance impacts. In a long-term Typhoon study in Taiwan, Lin et al.39 document thatit took two years for litterfall to return to pre-Typhoon levels after a major event in 1994, and annual peak leaf area index only returned to pre-event levels after ten years. This recovery timescale corroborates that the yearly forest loss GFCC product could successfully capture the spatial distribution of megadisturbance impacts in subtropical settings. According to de Beurs et al.12, recovery from megadisturbance appeared much slower using the disturbance index (DI), than using NDVI. The GFCC "forest loss" product is partly based on the greenness, wetness and brightness indices of the Landsat sensor bands, which are used to compute the DI index.

Limitations of our study include difficulties in the accuracy assessment process: Verification sites, visible on the high resolution imagery of the Google Earth archive, are not necessarily identifiable in the Landsat imagery used to generate the GFCC "forest loss" product. As a consequence, in mountainous settings, geometric errors due to cumulated uncertainties in the georeferenciation of the Landsat imagery and of the high resolution imagery could generate errors in the accuracy assessment process. This difficulty is hard to overcome with the visual assessment of sites in some homogenous forested environmentson the Landsat imagery because of the (too coarse) 30m spatial resolution. Annual forest loss maps at 10m resolution (near to crown scale) derived from Sentinel-2, for example, should be more adapted to the application of forest degradation estimation in the future.

Read more:
Suitability of the global forest cover change map to assess climatic megadisturbance impacts on remote tropical forests | Scientific Reports -...