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More than 85 per cent of India's Internet runs on Free and open-source software (FOSS). It is ubiquitous in our lives and serves as the backbone of operating systems, digital public infrastructure, communication platforms and the Internet. However, due to its decentralised nature, open-source is underappreciated, and we are often unaware of its existence.

The recent log4j security vulnerability showed the extent of our dependence on FOSS. The White House National Security Council even had a meeting in January 2022 with tech giants and open-source organisations to identify paths to prevent such incidents in the future. It is not surprising that it takes a security breach that threatens the most powerful governments and companies for us to think about the extent of our dependence on open-source software.

Free and open-source software is software where the source code is openly shared, and it is free to use, copy, study and change. As Richard Stallman puts it, "Think free, as in free speech, not free beer." Open-source principles can be applied across verticals such as software, hardware, content, algorithms and standards. The gains from these various open-source technologies far outweigh the costs associated with them, and they accrue to stakeholders across the market, society, individual and government categories. Some of the gains for different stakeholders are explored below.

Also Read:Can we imagine life without the World Wide Web?

A recent report sponsored by the EU found that companies located in the EU invested around 1 billion in FOSS in 2018. The authors estimate that a 10 per cent increase in contributions would lead to a 0.4 per cent to 0.6 per cent increase in GDP and generate more than 600 ICT start-ups annually.

FOSS is closely interlinked with software development, and it is estimated that 97 per cent of code bases contain open-source. It helps companies access high-quality code while avoiding vendor lock-in and lowering costs. There are various monetisation models to build a business case around open-source. These include providing paid services such as training and technical support, adopting a freemium model, and crowdfunding desired features.

Sharing non-differentiating features as open-source also have several advantages for companies. When Google open-sourced their machine learning framework TensorFlow in late 2015, they benefited from the increased adoption of the framework leading to crowd-sourced innovation. It is now the most ubiquitous AI platform, and Google benefits from the talent pool in a niche technology.

Wikipedia, the world's largest encyclopaedia, is funded by donations and maintained by unpaid volunteers. The infrastructure that powers the Information Age rests on a foundation built on open-source software such as Linux and Apache, among many others. According to GitHub, India has the third-highest number of active developers. The number of developers contributing to open-source is growing fast, and India is poised to become one of the major contributors to FOSS.

A recent study finds that individuals contributing to open source projects are intrinsically motivated by feelings of altruism, fun, or kinship. Many contributors also seek the reputation and career advancement to be gained from working on open-source projects. Although there exist avenues for funding, it often relies on donations based on the goodwill of others. This issustainable for only the most popular projects. The maintenance cost is minuscule compared to the cost of the damages incurred due to the vulnerabilities such as log4j.

Some interesting aspects of FOSS are visible in the popular open-source chess platform lichess.org. It is a free-to-play, crowd-sources learning module and is funded by donations. Unlike other platforms, it has features such as requesting your opponent for a move to be taken back and granting your opponent extra time in a timed game. The non-zero-sum approach that open-source principles espouse percolates into various aspects of open-source products.

Also Read:Govt working to provide high-speed internet to every village, says PM

From the government's perspective, the economic motivations for promoting open-source can be broadly classified under cost savings, avoiding switching costs and network effects, underproduction due to weak incentives, and technology neutrality. These are especially important in the Indian context. The Kerala government has been a pioneer by officially supporting FOSS in the State IT Policy in 2001. In a phased manner, Linux and other FOSS have been adopted by various government departments and schools. The government of Kerala has also set up an autonomous nodal agency (ICFOSS) to work on improving the adoption and innovation around FOSS. It also works closely with FOSS communities such as SPACE to build open digital infrastructure.

The desire for "Digital Sovereignty" unconstrained by state intervention, technology oligopoly, and international geopolitics is also a major motivation for governments. The open nature of open-source implies it is more customisable and available. This leads to reduced dependence on proprietary technologies from companies based elsewhere.

India needs to leverage technology to overcome developmental challenges, and the various advantages of open source present it as a promising option. However, due to its nature, open-source suffers from market failures. The market does not adequately incentivise creators to keep developing and maintaining open-source projects. A comprehensive strategy to nurture and promote the adoption of open-source technologies is necessary for India.

(Bharath Reddy is a programme manager for the technology and policy programme at The Takshashila Institution)

Disclaimer: The views expressed above are the author's own. They do not necessarily reflect the views of DH.

Read more from the original source:
Nurturing open source is in our national interest - Deccan Herald

Article by Dynatrace vice president of A/NZ Hope Powers.

The use of DevSecOps practices is growing, as it is increasingly seen as the best way to produce high-quality and secure code. More than one-third (36%) of respondents to GitLabs 2021 Global DevSecOps Survey reported developing software using DevSecOps, up from 27% in 2020.

This growth is driven by organisations realising that application quality and security are essential to their ability to streamline continuous integration and delivery (CI/CD) and accelerate innovation. They need to balance pressure to develop software rapidly with the need to ensure it remains secure and is optimised for todays cloud environments. This can be quite a challenge.

GitLabs Fifth Annual Global DevSecOps Survey (2020) found 60% of developers are releasing code twice as fast by using DevOps. However, speed often comes at the expense of security. A survey of CISO leaders last year found that 71% of CISOs admit they are not fully confident code is free of vulnerabilities before going live in production.

To enable software to be developed rapidly and securely, DevSecOps teams need to automate all stages of the lifecycle. They need shared solutions and platforms that converge observabilitythe ability to measure a systems current state based on the data it generates, such as logs, metrics and traceswith security, so they can spot security gapsand identify poor quality code and other software development issues.

In a survey of 250 enterprises in the US and UK with more USD $1 billion in revenue, 96% of respondents expected to benefit by automating their compliance and security processes, a fundamental goal of DevSecOps.

As DevSecOps continues to gather momentum, here are some key trends.

1. Infrastructure as code (IaC) uptake is rising

Infrastructure-as-Code (IaC), aka software-defined infrastructure, is the management of hardware using code. It enables IT hardware resources to be configured, managed, monitored and provisioned using software rather than manual processes.

According to Gartner, 60% of organisations will be using infrastructure automation tools as part of their DevOps strategy by 2023, improving application deployment efficiency by 25%. In addition, defining infrastructure as code enables greater automation throughout the delivery pipeline, making it easier to replicate the testing and deployment process for new code. This is essential for accelerated DevSecOps adoption.

The same code can be used every time a particular infrastructure configuration is needed, so the benefits in time and effort saved are greatly increased. IaC can also benefit DevSecOps by reducing human error. Processes enshrined in code are secure and repeatable, lending themselves to automation and ensuring the correct execution ofhighly complex processes.

2. Attacks via vulnerable third-party code are growing

Many organisations make use of third-party code and software libraries in their development of new digital services. Any vulnerabilities in this code expose their applications to cyber attacks.

To guard against this, organisations must monitor their use of third-party code so they can patch any new vulnerabilities that are discovered. For example, in December 2021, a vulnerability known as Log4Shell was discovered in versions 2.0 and 2.14.1 of Log4j 2, a popular Java library. Log4Shell enables an attacker to use remote code execution to engage with software that uses Log4j and gain access to networks and sensitive data. Many organisations were forced to take devices and applications offline while they identified whether Log4j had been used in any stage of software production, from development to runtime.

In a blog, author and developer advocate Nicolas Frnkel wrote, Wise developers dont reinvent the wheel: they use existing libraries and/or frameworks. From a security point of view, it means users of such third-party code should carefully audit it. We should look for flaws: both bugs and vulnerabilities.

Log4Shell certainly will not be the last such vulnerability, as the more recent discovery of Spring4Shell has already shown. To guard against the next one, organisations should deploy observability platforms that can provide deep and broad insights into their applications to quickly identify any code flagged as vulnerable.

3. Root-cause analysis using AIOps will be essential

Gartner defines artificial intelligence for IT operations (AIOps) as the combination of big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.

Such automation is becoming essential to enable DevSecOps teams to manage cloud environments whose complexity is putting them beyond the capabilities of manual processes. AIOps can analyse data on activity in real-time, helping to prevent DevSecOps teams being overwhelmed by alert storms and providing precise answers that enable them to innovate more rapidly.

According to a Forbes article, AIOps is moving from marketing hype to a useful tool being adopted across the enterprise. It explains that the AI algorithms underpinning AIOps are becoming increasingly sophisticated. They enable AIOps tools to discover data relationships more rapidly, identify the root cause of IT issues in real-time and, in some cases, remediate them automatically.Such abilities are becoming essential to enable DevSecOps teams to test code while it is being developed and to identify new vulnerabilities during pre-production before code is deployed.

4. MLOps is no match for AIOps

Machine Learning Operations (MLOps) is a set of management practices designed to aid the effective and efficient deployment and maintenance of machine learning in production environments. It is often confused with AIOps but is quite different.

MLOps can only suggest a relationship between a problem and a possible solution. AIOps identifies problems precisely and provides actionable answers. MLOps systems must be trained to distinguish normal from abnormal behaviour. Data models must be verified, which requires time and effort from DevSecOps teams - time that could be spent on more strategic priorities.

In contrast, AIOps automates these tasks by combining AI algorithms with data analytics. It can accurately identify many common IT issues such as unexpected downtime or unauthorised data access and suggest appropriate remedies. These algorithms do not need to be trained, freeing IT teams from routine monitoring tasks and enabling them to focus on tasks that directly support business priorities and drive better outcomes.

Dynatrace vice president of A/NZ Hope Powers.

5. GitOps gains wide acceptance

GitOps is a set of practices for infrastructure management based on DevOps best practices for application development: version control, collaboration, compliance, CI/CD tooling. It is based on Git, an open-source tool developed for source code management in DevOps.In GitOps, Git becomes a single source of truth and a control mechanism to support dynamic creation, including updating and deleting system architecture specifications.

It automates and centralises the deployment and verification of infrastructure modifications via pull requests, giving teams greater control over their environment and enabling them to deliver better digital services faster.

6. The role of Kubernetes grows

Kubernetes, the open-source platform built to orchestrate the management, deployment, and scaling of microservices architectures, underpins all these aspects of DevSecOps and digital transformation.

Kubernetes enables a microservices-based application to be moved quickly and reliably between environments, for example, from a development to a production environment. It also makes application developers more productive. With microservices-based deployments supported by Kubernetes, multiple teams can simultaneously deal with different aspects of a project, accelerating development and identifying and fixing problems faster.

Kubernetes has been a game-changer for application development. It has enabled developers to better accommodate customer requirements, share resources across cloud platforms, and accelerate the building, testing and deployment of DevSecOps pipelines.

7. Serverless uptake soars

Serverless computing is a cloud-based, on-demand execution model where customers consume resources solely based on their usage by applications.It greatly appeals to developers wanting to build and scale out applications without worrying about the underlying infrastructure. The cloud service providers take care of this and supply the tools that enable app developers to create their applications in modules according to the cloud infrastructure they require. Serverless computing can also reduce costs and improve disaster recovery and resilience because the resources used are supported by the cloud providers inbuilt redundancy and availability features.

8. DevSecOps comes of age

Ultimately, companies undertaking digital transformation will struggle to succeed without DevSecOps.

However, to successfully exploit DevSecOps, development teams need platforms that streamline the entire software development lifecycle, facilitate cross-team collaboration and automate processes wherever possible.

See original here:
Eight top DevSecOps trends to support IT innovation in 2022 - IT Brief Australia

What happened

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.

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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...