University of the Cumberlands

Economic sanctions disrupt international business by increasing uncertainty and forcing companies to reassess their operations in the sanctioned market. Following Russia's invasion of Ukraine in February 2022, US companies faced critical strategic decisions regarding their business activities in Russia. This study investigates how market presence-measured through foreign direct investment (FDI) and international trade (export)-influenced the initial responses of 241 S&P 500 firms to the international economic sanctions against Russia. The findings reveal that companies with FDI in Russia were more likely to scale down or suspend their business operations rather than continue business as usual, freeze new investments or withdraw from Russia entirely. Firms exporting to Russia were more likely to suspend their exports. The study further identified a statistically significant relationship between the timing of 240 S&P 500 firms' initial response announcements and their market presence. Firms that exported to Russia were more likely to act as leaders, announcing their initial sanction response within 10 days after the invasion, while firms with FDI in Russia were more likely to act as followers, announcing their responses later. The study results underscore the importance of market presence in shaping corporate strategies amid geopolitical crises and economic sanctions.

Medical images contain sensitive and private health information of patients, which is crucial to be safeguarded from unauthorized access. Various encryption schemes are used for improving security; however, they are subject to adversaries associated with higher complexity, ineffective compression ratios, and slower responses in real time. Therefore, to address these limitations as well as to establish stronger security in medical image sharing, the research proposes a Biometric Fused Federated Generative Adversarial Network (BF2GAN) method. The federated learning (FL) concept is included in this research, which provides collaborative training while reducing the risks of gradient inversions. Moreover, the decentralized training strengthens data integrity and reduces the risk of data exposures. The biometric information is included for user verification and key generation, which assists in creating highly secure images that are difficult to decode without the correct keys. The method minimizes the reliance on key management algorithms and establishes a simplified encryption process that strengthens the overall system security. In the LUNA16 database, the BF2GAN achieves a significant performance in terms of 3.08 s encryption time, 0.975 structural similarity index measure, 0.82 Feature similarity index, 3.9 s decryption time, 58.38 decibels of peak signal-to-noise ratio, and minimum memory usage of 280.87 kilobytes, compared to the state-of-the-art methods.

Face Recognition is the process of identifying people by extracting their facial features, and it is widely utilized in several applications, including authentication, healthcare, and security. The traditional approaches faced troubles in providing better accuracy and computational efficiency due to the lack of identifying the facial patterns. Therefore, a Root Cause Analysis (RCA) is essential in a face recognition system to prevent failures in recognizing faces. Hence, the Channel and Spatial Attention-based Explainable Convolutional Network (CSA-ECNet) model is proposed to enhance the face recognition results through detecting the defects and analyzing the root causes. The incorporation of an explainable technique helps to provide insights to the CSA-ECNet model in detecting the root causes, thereby increasing the performance of the CSA-ECNet model in recognizing faces without any failures. The incorporation of the Channel and Spatial Attention (CSA) facilitates increasing the accuracy by enabling the CSA-ECNet model to selectively concentrate on the vital spatial regions and feature channels, which strengthens the model's ability to handle various aspects, including poor lighting. Experimental results demonstrate the exceptional performance of the CSA-ECNet model, reporting the high sensitivity of 97.79%, specificity of 98.44%, and accuracy of 98.11% for 90% of training data on Face Recognition Dataset.

Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments.

Agent safety benchmarks measure whether a monitor detects harm, not when. Yet timing is the difference between intervention and autopsy. We introduce StepShield, the first benchmark that treats detection timeliness as a first-class metric. On 9,429 incident-grounded code-agent trajectories, we define the Early Intervention Rate (EIR): the fraction of detected rogue trajectories where the alert fires within a k-step window after the divergence point, isolating timing quality from coverage. This metric exposes what we call the Forensics Trap: a pattern-based guardrail with 847 rules achieves 86