Hasan Mohammad

This study investigates the overlooked, yet critical, impact of climate risk on nonprofit organizations. Leveraging data from the IRS and the Spatial Hazard Events and Losses Database for the United States (SHELDUS), the study analyzes the relationship between climate risk and donations to nonprofit organizations across a sample of 1.23 million IRS Form 990 filings by U.S. nonprofits from 2010 to 2021. The findings show a significant positive relationship between climate risk and donations that is robust to alternative specifications incorporating organization fixed effects, alternative climate risk measures, instrumental variables, entropy balancing, and difference-in-differences analysis. Additional subgroup tests reveal that hospitals and human service organizations see the largest increases in donations under elevated climate risk, while others, particularly mutual benefit organizations and international-focused nonprofits, experience declines. Additional analyses also indicate that donor-oriented organizations in high-risk areas are more likely to benefit from increased donations. Finally, cross-sectional analyses suggest that government funding, in the context of heightened climate risk, appears to partially “crowd out” private donations. The results underscore the importance of understanding how environmental challenges influence donor behavior and provide ethical implications and practical insights for nonprofit financial planning in an era of increasing climate risks.

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.