Jawaharlal Nehru Technological University, Hyderabad

In preterm neonates of ≥ 30weeks' gestation with RDS, NHFOV delivered through NM at equivalent pressures is non-inferior to CPAP when used as primary NRS. The finding of shorter NRS duration with NHFOV needs to be proven in adequately powered trials. Use of NM interface with equivalent pressures addresses the lacunae in the current literature on NHFOV and provides a rigorous comparison between the two NRS modes. www.ctri.nic.in , id CTRI/2024/10/074939, registered on 8 October 2024. • CPAP as initial respiratory support for preterm neonates with RDS is the standard of care in LMICs. • NHFOV is more efficacious than CPAP when used as a post-extubation respiratory support modality, evidence for the same being uncertain when used as primary support. • NHFOV is non-inferior to CPAP as primary support in preterm neonates ≥ 30weeks with RDS for the outcomes of treatment failure and IMV requirement with equivalent pressures after lung recruitment and nasal mask interface. • A superiority design RCT comparing these two non-invasive respiratory support modalities in this subgroup of preterm neonates may not be feasible. The low baseline event rate of IMV necessitates an impractically large sample size to achieve adequate power.

Abstract—Nutrient deficiencies in rice plants, especially nitrogen (N), phosphorus (P), and potassium (K), can cause significant effects on rice yield and quality. Conventional methods for diagnosing nutrient deficiencies in rice leaves, including visual observation and soil analysis, are time-consuming and often unreliable. This research aims to develop an online rice leaf nutrient deficiency diagnosis system using machine learning and image processing techniques. The proposed system uses a DenseNet121 convolutional neural network (CNN) model that is fine-tuned on rice leaf images to achieve high accuracy in diagnosing nutrient deficiencies. The system is built using Flask as the backend framework, SQLite as the database management system, and role-based access control for users (farmers, agronomists, and administrators). The proposed system has been shown to achieve an average accuracy of 94-98% across datasets, providing a scalable and user-friendly solution for precision agriculture

Type of the article: Research ArticleIn the era of digital transformation, organizations are increasingly integrating digitalization and artificial intelligence to enhance employee behavior and organizational outcomes. This study examines the associations among digital leadership, AI-based performance assessment, employee empowerment, work engagement, and organizational performance in a Chennai-based IT company in India. Specifically, the study investigates the direct and indirect effects of digital leadership and AI performance assessment on organizational performance through employee empowerment and work engagement. Data were collected from 373 IT employees using an online survey conducted between June and August 2025 and analyzed using partial least squares structural equation modelling (PLS-SEM). A stratified random sampling technique based on organizational job levels (entry, mid, and senior) was adopted to ensure adequate representation of hierarchical positions within the organization. Hypothesis testing revealed that digital leadership and AI performance assessment significantly enhance employee empowerment and work engagement (β = 0.490, 0.415, 0.527; p < 0.001), which in turn positively influence organizational performance (β = 0.383, 0.477, 0.195, 0.287; p < 0.001, 0.033). Furthermore, employee empowerment and work engagement significantly mediate the relationships between digital leadership, AI performance assessment, and organizational performance (β = 0.135, 0.199, 0.265, 0.244; p < 0.001). In addition, a multi-group analysis was conducted to examine differences across employee hierarchical levels. The findings highlight that transformations in modern workplaces and digitalized HR practices contribute to organizational performance across the workforce, while ensuring that employees from different hierarchical levels are adequately represented in the sample.

Abstract—Honeypots play an important role in modern cybersecurity by acting as decoy systems designed to attract and analyze malicious activities without affecting real network resources. They help security researchers and administrators study attacker behavior, identify vulnerabilities, and understand common cyber-attack patterns. This paper focuses on developing a web-based honeypot monitoring and threat analysis system that simulates vulnerable endpoints to capture suspicious interactions. The system records important parameters such as IP address, request data, attack patterns, and timestamps, and processes this information to calculate the threat probability associated with each interaction. Additionally, the platform provides controlled user access with administrative verification and displays the analysis results through an interactive dashboard. The system’s result enable better identification of suspicious activities, visualization of attack patterns, and classification of risk levels, thereby improving cybersecurity awareness and supporting effective threat monitoring strategies. Keywords— Honeypot, Cybersecurity, Threat Detection, Network Security, Attack Monitoring, Threat Probability Analysis, Intrusion Detection, Honeypot Simulation, Risk Classification, Security Monitoring.

Abstract— Tuberculosis (TB) continues to be one of the leading infectious diseases affecting millions of people globally, particularly in developing countries. Timely diagnosis plays a critical role in controlling transmission and reducing mortality. Chest X-ray imaging is widely used for pulmonary tuberculosis screening; however, manual interpretation depends heavily on expert radiologists and may lead to delays or subjective errors. In this paper, an automated tuberculosis detection system based on machine learning techniques is presented. The proposed model combines Convolutional Neural Networks (CNN) for deep feature extraction with Support Vector Machine (SVM) for classification. Preprocessing techniques such as resizing, normalization, and augmentation are applied to enhance dataset quality and improve generalization. The hybrid approach allows effective extraction of visual patterns associated with TB infection and produces reliable classification results. Experimental findings demonstrate that the system achieves high accuracy while maintaining computational efficiency. The proposed solution can serve as a supportive diagnostic tool in resource-limited healthcare environments. Index Terms-Tuberculosis Detection, Chest X-ray Analysis, Convolutional Neural Network, Support Vector Machine, Medical Image Classification

High-speed arithmetic operations are fundamental to modern digital systems, where multi-operand addition frequently determines the critical timing path. Conventional cascaded two-operand adders result in sequential carry propagation and increased latency. This paper presents an FPGA implementation employing carry computation to enhance timing performance. The proposed architecture decouples operand compression from carry resolution, thereby reducing critical path delay. Kogge– Stone, Han–Carlson, and Ladner–Fischer prefix structures are implemented in Verilog HDL and synthesized on an Artix-7 FPGA platform. Experimental results indicate that structured prefix networks improve computational efficiency while providing scalable trade-offs among delay, area, and power for high-throughput arithmetic systems.

Coastal ecosystems play a crucial role in carbon sequestration, coastal protection, fisheries support, and marine biodiversity conservation in the Indian Ocean Rim(IOR) countries. In this study, globally consistent datasets were used to assess the spatial distribution of mangroves, coral reefs, and seagrass meadows across IOR countries at the basin scale. The Global Mangrove Watch (GMW) was used for Mangrove distribution, UNEP-WCMC: Global Distribution of Seagrasses was used to retrieve seagrass data, and coral reef distribution from UNEP-WCMCs global warm-water coral reef dataset. Quantification of Ecosystem areas was made to enable cross-country comparison within national coastal waters and Exclusive Economic Zones (EEZs).Mangroves have dominant coverage in Malaysia, Myanmar, Bangladesh, India, and Madagascar, contributing ~15-20% of the mapped coastal ecosystems, driven by monsoon climate, deltaic sediment supply, and sheltered estuarine settings. Mangroves are among the most carbon-dense ecosystems, providing effective shoreline stabilization and land sea ecological connectivity, although spatially limited. Mangroves deliver high carbon density and coastal resilience, coral reefs maximize biodiversity per unit area, and seagrasses dominate spatial extent and carbon sequestration. Overall, the analysis shows strong functional complementarity among IOR coastal ecosystems.

Aims: To evaluate the effectiveness of different climate change adaptation practices on the productivity and profitability of soybean cultivation in a semi-arid Adilabad of Telangana and to identify the most economically viable climate-resilient practice for enhancing farmers’ income. Study design: A comparative and analytical research design was adopted. Place and Duration of Study: The study was conducted in Adilabad district of Telangana state during the agricultural year June 2025 to October 2025. Methodology: A purposive sampling technique was used to select 90 soybean farmers, categorized into three groups of 30 farmers each based on adaptation practices: (A) proper sowing window June15th to July10th with medium-duration varieties such as JS 335 & AISB 50 with duration of 95-100 days, (B) late sowing with medium-duration varieties, and (C) late sowing with long-duration varieties such as KDS 726, KDS 756 etc. with duration of 100-115 days. Primary data were collected using structured interview schedules. The data were analyzed using descriptive statistics, standard cost concepts, Adaptation Strategy Use Index (ASUI) to assess the extent of adaptation, and partial budget analysis to estimate economic gains from alternative practices. Results: Farmers perceived significant climatic changes, particularly delayed monsoon onset and erratic rainfall, adversely affecting soybean production. Common adaptation strategies included timely sowing, intercropping, and improved irrigation scheduling. Among the three practices, farmers adopting proper sowing within the recommended window with medium-duration varieties (Practice A) achieved higher yields and superior economic returns. Partial budget analysis indicated that Practice A generated an additional net profit of ₹8,706.88 per hectare compared to late sowing practices (Practices B and C), demonstrating greater economic viability under climate variability. Conclusion: Sowing soybean within the optimal window using medium-duration varieties is the most effective climate-resilient strategy under semi-arid conditions. Wider promotion of this practice through targeted extension interventions and climate-responsive policy support can enhance farm income and strengthen resilience to climate variability.