In response to the prevailing energy crisis, this research focuses on elevating the potential of lithium niobate (LN) thin films for advanced optoelectronic applications. Employing electron beam evaporation, films undergo precise annealing (700°C to 1100°C), revealing a structural evolution through X-ray diffraction—crystallite sizes transition from 69.34 nm (unannealed) to 47.90 nm (1100°C). Scanning electron microscopy captures the transformation from coarse grains to photonic crystal clusters, while energy dispersion X-ray analysis discloses LN's composition (97.27 wt.% oxygen, 2.73 wt.% niobium). Rutherford backscattering spectroscopy illustrates surface damage post-Helium ion implantation, proportionate to depth. UV-VIS spectrophotometry highlights a significant blue shift in the optical band gap (3.70 eV to 2.52 eV), with further reduction at 700°C (2.48 eV) and a climactic shift at 1100°C (2.68 eV). This study not only addresses the pressing energy crisis but also emphasizes the indispensable role of lithium niobate in shaping the future of optoelectronics. It provides insights into tailoring LN properties for sustainable advancements in optoelectronic devices, marking a crucial chapter in our collective journey towards energy resilience. The urgency of innovation in the face of global challenges is underscored, marking a crucial chapter in our collective journey towards energy resilience.

This study meticulously explores the crucial elements precipitating corporate failures in Taiwan during the decade from 1999 to 2009. It proposes a new methodology, combining ANOVA and tuning the parameters of the classification so that its functional form describes the data best. Our analysis reveals the ten paramount factors, including Return on Capital ROA(C) before interest and depreciation, debt ratio percentage, consistent EPS across the last four seasons, Retained Earnings to Total Assets, Working Capital to Total Assets, dependency on borrowing, ratio of Current Liability to Assets, Net Value Per Share (B), the ratio of Working Capital to Equity, and the Liability-Assets Flag. This dual approach enables a more precise identification of the most instrumental variables in leading Taiwanese firms to bankruptcy based only on financial rather than including corporate governance variable. By employing a classification methodology adept at addressing class imbalance, we substantiate the significant influence these factors had on the incidence of bankruptcy among Taiwanese companies that rely solely on financial parameters. Thus, our methodology streamlines variable selection from 95 to 10 critical factors, improving bankruptcy prediction accuracy and outperforming Liang's 2016 results.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

Oil spills (OS) in waters can have major consequences for the ecosystem and adjacent natural resources. Therefore, recognizing the OS spread pattern is crucial for supporting decision-making in disaster management. On 31 March 2018, an OS occurred in Balikpapan Bay, Indonesia, due to a ship's anchor rupturing a seafloor crude oil petroleum pipe. The purpose of this study is to investigate the propagation of crude OS using coupled three-dimensional (3D) model from DHI MIKE software and remote sensing data from Sentinel-1 SAR (Synthetic Aperture Radar). MIKE3 FM predicts and simulates the 3D sea circulation, while MIKE OS models the path of oil's fate concentration. The OS model could identify the temporal and spatial distribution of OS concentration in subsurface layers. To validate the model, in situ observations were made of oil stranded on the shore. On 1 April 2018, at 21:50 UTC, Sentinel-1 SAR detected an OS on the sea surface covering 203.40 km². The OS model measures 137.52 km². Both methods resulted in a synergistic OS exposure of 314.23 km². Wind dominantly influenced the OS propagation on the sea surface, as detected by the SAR image, while tidal currents primarily affected the oil movement within the subsurface simulated by the OS model. Thus, the two approaches underscored the importance of synergizing the DHI MIKE model with remote sensing data to comprehensively understand OS distribution in semi-enclosed waters like Balikpapan Bay detected by SAR.

This paper explores the role of the agile approach in managing interorganizational relationships in innovation networks. Design/methodology/approach. Relevant literature related to agile team management, network theory, innovation theory and knowledge management was studied. Based on collaboration between different approaches, a conceptual model for agile management of an innovation network was generated. Conceptual modeling was supplemented with graphical notation (diagram) of the main elements of the model. At the stage of testing the conceptual model, the action research method was applied, which provides an opportunity for organizational innovations to be carried out with the participation of researchers. The object of the pilot implementation of the conceptual model is the Bulgarian division of a global non-governmental organization (NGO) dedicated to community service. The organizational innovation applied in the testing of the model is related to improving the communication environment between individual teams (clubs), which are autonomous, but in the conditions of a network can generate projects for common, large-scale initiatives for community service. Findings. The pilot testing of the model shows its applicability, insofar as a traditionally managed structure switches to an agile communication model, in which horizontal connections become more frequent and knowledge between individual participants is transferred more efficiently. The possibility of decentralized decision-making creates the potential for generating numerous new and larger-scale initiatives for the benefit of the final beneficiaries. The participants in the network have also outlined some shortcomings, such as the need for better preliminary preparation when introducing organizational innovations in order to adequately explain and accept them.

The Republic of Moldova is a state with a small, but dynamic economy and which, with the help of competitiveness in the IT industry, is looking for a place on the economic market in the Eastern European region. The research approaches this topic from an economic, historical, but also geopolitical point of view. This analysis of economic data and figures from the last period, combined with government policies and that of the National Bank of Moldova, means that in the near future the software economic area of Moldova will become an important regional player in this part of Europe.