St. Joseph University, Nagaland

Plants with complex floral morphologies are adapted to be pollinated by restricted pollinator assemblages and may suffer pollinator limitation. Understanding how floral traits and other variables relate to plant vulnerability can provide a powerful tool for predicting the conservation status and prioritizing the assessment of plants with scarce field data. Using circa 3000 records of rare and threatened (sensu IUCN) entomophilous plant taxa from seven Mediterranean countries, we evaluated how six floral traits and other eight intrinsic and extrinsic variables were related to plant vulnerability (less vs. more threatened plants). Besides, we analyzed 29 experts' opinions regarding the floral traits most related to floral complexity. Floral shape, reproductive unit, and flowering duration were good vulnerability indicators. Taxa with lip- and flag-shaped flowers were the most threatened, which agrees with the opinion of experts who considered lip- and flag-shaped flowers to have more complex morphologies. Also, plants with cylindrical inflorescences or solitary flowers were more threatened than those with flat-spherical inflorescences; and longer flowering durations reduced the probability of being threatened. Regarding extrinsic variables, coastal and freshwater habitats, i.e. habitats heavily impacted by human activities, had the highest percentage of highly threatened taxa. Yet, plant vulnerability decreased with maximum elevation and total distribution range. These results may serve as a basis for managers and practitioners when field data are scarce or unavailable, so that, depending on their traits, species could be provisionally listed in Red Lists as deserving priority for assessment to ascertain conservation status and actions.

Fog Computing has been established as an important technology to meet the requirements of modern IoT applications, which need real-time response, less network latency and proper resource utilisation. Fog computing is primarily based on resource-constrained devices, which establishes the importance of a lightweight resource scheduling algorithm to minimise the operational cost. In this context, the current study represents a comprehensive simulation-based study of six different resource scheduling algorithms in Fog-Cloud architecture. The algorithm includes - Round Robin (RR), Load Balancing (LB), First Come First Serve (FCFS), Priority-Based Scheduling (PBS), Shortest Task First (STF), and Energy-Aware Scheduling (EAS). The experiment is carried out with the Simpy simulation tool, where a realistic Fog-Cloud environment has been created with heterogeneous Fog resources, multi-tier deployment, proper network latency, and bimodal workload distribution. We have considered key performance matrices, which include - task execution rate, average waiting and completion time, energy consumption, throughput, simulation time, and resource utilisation, to analyse the effectiveness of each algorithm with respect to these matrices. The simulated result shows that even though the STF algorithm is simpler, it shows almost equivalent results to the LB algorithm in a few performance matrices. This study unfolds the potential of simple, yet competent, rule-based heuristic algorithms compared to the modern LB algorithm.

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

This study aimed at assessing the mechanical properties and degradation of commercial bioactive materials. The bioactive materials (Activa Bioactive Restorative, Beautifil Flow Plus F00, F03, Predicta Bulk Bioactive) and composite resin Filtek Supreme Flow were submitted to flexural and diametral tensile strength tests (FS, DTS), modulus of elasticity (ME) evaluation, and analysis of aging in 70% ethanol and saliva on their hardness and sorption. The results for DTS ranged from 33.16 MPa (Beautifil Flow Plus F03) to 47.74 MPa (Filtek Supreme Flow). The highest FS was 120.40 MPa (Predicta Bulk Bioactive), while the lowest values were 86.55 MPa (Activa Bioactive Restorative). Activa Bioactive Restorative showed the lowest ME, as well as the highest water sorption both in alcohol and artificial saliva. Moreover, aging in saliva induced a significant decrease in hardness for Activa Restorative (p < .01). Alcohol storage caused a significant decrease in hardness for all materials (p < .0001). All tested materials met the basic requirements for light-curing materials in terms of DTS and FS. However, all materials showed higher sorption in alcohol than in saliva, while hardness decreased significantly after 30 days. Predicta Bulk Bioactive presented the highest mechanical parameters, initial hardness, and the lowest sorption of alcohol and saliva.

The degradation of the environment, driven by the reliance on fossil fuels, has prompted governments to shift towards renewable energy sources. While the topic of energy transitions is gaining researchers' attention, the contribution of factors related to policy, the environment, the economy, finance, and technology is still unclear. One such factor is environmental policy stringency that potentially reduces the ecological footprint and encourages green energy use. Another noteworthy factor is economic complexity, which supposes that countries dispose of resources to facilitate the green energy transition. Finally, FinTech (Financial Technology) is worth considering as it facilitates access to funds and promotes sustainable investments thus aiding energy transitions. This study therefore aims to empirically investigate the contribution of environmental policy stringency, economic complexity, and FinTech financing to countries' energy transitions and tests for the mediating role of FinTech. It uses panel data for 29 countries between 2010 and 2020 and employs Instrumental Variables (IV) 2-step GMM and IV 2-stage least squares estimation techniques. Results show that economic complexity and environmental policy stringency foster energy transitions. FinTech financing positively influences energy transitions and mediates the relation between energy transition and economic complexity. These findings show the importance of FinTech in funding cleaner energy sources, the significance of economic complexity in achieving energy transitions, and highlight the role of governments' stringent environmental policies in encouraging the move away from fossil fuels.