Predicting Forest Areas Susceptible to Fire Risk Using Convolutional Neural Networks

Wildfires pose a grave danger and threat to both human health and the environment, which is why early detection of wildfires is crucial. In this study, a convolutional neural network, which is a deep learning technique for computer vision, that is capable of classifying satellite imaging of forest cover in Canada as either being prone to wildfires or not being prone to wildfires is created. This model achieved an accuracy of 95.06% and is not only accurate but also reliable and unbiased in terms of the training set and the test set. We also review an existing model for the same dataset. Furthermore, this study discusses the application of this model in the real world, its feasibility, its future scope, and strategies to improve it

A Traffic Control Framework for Uncrewed Aircraft Systems

The exponential growth of Advanced Air Mobility (AAM) services demands assurances of safety in the airspace. This research a Traffic Control Framework (TCF) for developing digital flight rules for Uncrewed Aircraft System (UAS) flying in designated air corridors. The proposed TCF helps model, deploy, and test UAS control, agents, regardless of their hardware configurations. This paper investigates the importance of digital flight rules in preventing collisions in the context of AAM. TCF is introduced as a platform for developing strategies for managing traffic towards enhanced autonomy in the airspace. It allows for assessment and evaluation of autonomous navigation, route planning, obstacle avoidance, and adaptive decision making for UAS. It also allows for the introduction and evaluation of advance technologies Artificial Intelligence (AI) and Machine Learning (ML) in a simulation environment before deploying them in the real world. TCF can be used as a tool for comprehensive UAS traffic analysis, including KPI measurements. It offers flexibility for further testing and deployment laying the foundation for improved airspace safety - a vital aspect of UAS technological advancement. Finally, this papers demonstrates the capabilities of the proposed TCF in managing UAS traffic at intersections and its impact on overall traffic flow in air corridors, noting the bottlenecks and the inverse relationship safety and traffic volume.Comment: 6 pages, 7 figure

Leadership Development Programs and Their Impact on Healthcare Institutions

Introduction:Leadership development is essential to preparing healthcare organizations to respond to transformational changes in the industry. Many organizations have started such programs to help their employees grow and mature into better leaders. This study seeks to assess the effect of these programs on healthcare institutions.Methods: A systematic literature review was conducted to identify relevant literature guiding leadership development programs in healthcare institutions. Studies were included based on pre-defined inclusion and exclusion criteria, and the articles searched were between 2010 and 2020. A total of twenty studies were included in the final analysis.Results: Leadership Development Programs Significantly and Positively Impact Healthcare Institutions. They are also typically proven to sharpen leadership competencies, enhance employee motivation and engagement, and contribute to a culture of ongoing learning. Moreover, organizations that emphasize leadership development programs in healthcare have been associated with improved patient outcomes, higher quality of care, and enhanced financial performance.Conclusions: Healthcare Leadership Development Programs Summary: Healthcare leadership development programs are now standard in healthcare organizations and have implications not only for the individual leader but also for the organization as a whole. They are essential for creating a culture of innovation, agility, and resilience, which are all needed in the face of the changing healthcare environment. As a result, healthcare organizations must prioritize investment and ensure the consistency of leadership development programs. Additional studies are needed to examine the effectiveness of such programs in the longer term and identify optimal approaches for their delivery and evaluation

Exploring the Influence of Communication Mechanisms on Organizational Effectiveness and Employee Engagement

Introduction: Effective communication mechanisms are vital for organizational success, influencing employee engagement and overall performance. Despite their importance, many organizations face challenges due to unclear or infrequent communication, which undermines trust and productivity. This research investigates the influence of communication mechanisms—including frequency, medium, formality, and clarity—on employee engagement and organizational effectiveness, with employee trust as a mediating factor.Methodology: Data were collected through a structured survey administered to 171 employees across various sectors. The survey measured communication practices, employee trust, and engagement using a 5-point Likert scale. Statistical analyses, including regression analysis, were conducted using IBM SPSS version 25 to examine the relationships among variables.Results: The analysis revealed that communication frequency and clarity significantly enhance Economic success (ES), and Operational efficiency (OE). Regression results indicated a strong positive correlation between clear communication and employee engagement (p < 0.01). Two-way communication (TWC), Transparency of communication (TC), Communication consistency (CC)and Employee engagement (EE) also contributed to improved trust and engagement.Conclusion: Findings highlight that communication clarity and frequency are critical drivers of employee engagement, facilitated by trust in communication. Organizations should prioritize enhancing communication quality and consistency to build trust, thereby fostering higher engagement and improved organizational effectiveness

Investigating the occurrence and predictability of pitch angle scattering events at ADITYA-Upgrade tokamak with the electron cyclotron emission radiometer

This paper describes the experimental analysis and preliminary investigation of the predictability of pitch angle scattering (PAS) events through the electron cyclotron emission (ECE) radiometer signals at the ADITYA-Upgrade (ADITYA-U) tokamak. For low-density discharges at ADITYA-U, a sudden abnormal rise is observed in the ECE signature while other plasma parameters are unchanged. Investigations are done to understand this abrupt rise that is expected to occur due to PAS. The rise time is as fast as 100 μs with a single step and/or multiple step rise in ECE radiometer measurements. This event is known to limit the on-axis energy of runaway electrons. Being a repetitive event, the conditions of its repetitive occurrence can be investigated, thereby exploring the possibility of it being triggered and surveyed as an alternate runaway electron mitigation plan. Functional parameterization of such events with other discharge parameters is obtained and the possibility to trigger these events is discussed. PREDICT code is used to investigate the possible interpretations for the PAS occurrence through modeling and supporting the ECE observations. The trigger values so obtained experimentally are set as input criteria for PAS occurrence. Preliminary modeling investigations provide reliable consistency with the findings.</p

The Cosmic Evolution Early Release Science Survey (CEERS)

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient ex-tragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R∼100) and medium (R∼1000) resolution spectroscopy, and NIRCam slitless grism (R∼1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers "First Light" and "Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z ≳ 10 from ∼90 arcmin 2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 6 3; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z ∼ 1-3. As a legacy product for the community , the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data

Effect of Hotel Green Service Encounters: Evidence from India

Green marketing is an important research area in the marketing literature, with researchers exploring how businesses can balance customer satisfaction, through outstanding services, with minimal environmental impact. Yet the independent impact of specific green elements of hotel services on the consumer–hotel brand relationship remains largely unexplored in the hospitality and tourism literature. Based on a review of relevant literature corroborated with qualitative in-depth interviews, followed by empirical validation, a framework is proposed for brand loyalty towards green hotel services, measured by attribute-based green service encounters and shaped through positive experiences.</jats:p