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Various studies have investigated the business model (BM) in different dimensions. However, bibliometric analyses in this discipline are still limited. This study aims to conduct a bibliometric analysis of business model research, identifying the state of the art, trends, and other indicators by analyzing articles published on the Web of Science (WoS) platform from 2017 to 2022. The present study examines the simultaneous occurrence of publications by year, keyword trends, bibliographic coupling, analysis of co-authorship, cities, and institutions, and finds that the literature on business models rapidly expanded between 2017 and 2020. A total of 14,881 articles were obtained as samples. The present study employs VOSviewer software to analyze the data. A considerable amount of literature has been produced on BMs, but researchers have stopped developing further literature which is thought-provoking. Based on the findings of this study, it is concluded that there is a need to research BMs in different domains such as sustainability and digitalization.

The effects of climate dynamics on urban areas involve the aggravation of existing conditions and the potential for emergence of new hazards or risk factors. Floods are recognized as a leading source of consequences to society, including disruption of critical functions in urban areas, and to the environment. Consideration of the interplay between services providers ensuring urban functions is essential to deal with climate dynamics and associated risks. Assessment of resilience to multiple hazards requires integrated and multi-sectoral approaches embracing each strategic urban sector and interactions between them. A common limitation resides in the limited data and tools available for undertaking these complex assessments. The paper proposes a methodology to undertake the spatial characterization of the flood related hazards and exposure of both essential functions and services providers in urban areas, in the context of limitations in data and in ready-to-use tools. Results support the resilience assessment of these hazards, taking into account interdependencies and cascading effects. The approach is applied to Lisbon city as the study case. Results are promising in demonstrating the potential of combining data and knowledge from different sources with dual modelling approaches, allowing us to obtain trends on the magnitude of effects of climate scenarios and to assess potential benefits of adaptation strategies. Quantification of the effects is reached, but results need to be assessed together with the underlying levels of uncertainty. The methodology can facilitate dialogue among stakeholders and between different decision levels.

This chapter covers the interactions between the transport and routing protocols layers. The aim is to provide better planning and design for MANET (Mobile Ad hoc NETwork). Two previously proposed solutions are combined for a cross layer solution in order to ensure some QoS (Quality of Service) parameters. The first solution is called CL-TCP (cross layer TCP). It is an adaptation of the TCP congestion control mechanism with some parameters provided by the routing layer (mobility nodes and the size of routing length). The second solution is called EM-OLSR (Energy Efficiency in MANET by improving OLSR protocol), which is based on the OLSR routing protocol and adding a new energy fairness parameter to the multi-point relay (MPR) technique. The obtained cross layer solution is called CL-TCP-OLSR; varied network conditions such as the network density and the mobility of nodes are evaluated. After the evaluation of three parameters, the obtained results after evaluation are satisfactory, and they show that CL-TCP-OLSR improves TCP throughput, TCP end-to-end delay, and the energy consumption in the network.

Nanofluid is a new class of engineering fluid that has good heat transfer characteristics which is essential to increase the heat transfer performance in various engineering applications such as heat exchangers and cooling of electronics. In this study, experiments were conducted to compare the heat transfer performance and pressure drop characteristics in a plate heat exchanger (PHE) for 30° and 60° chevron angles using water based Al2O3 nanofluid at the concentrations from 0 to 0.5 vol.% for different Reynolds numbers. The thermo-physical properties has been determined and presented in this paper. At 0.5 vol% concentration, the maximum heat transfer coefficient, the overall heat transfer coefficient and the heat transfer rate for 60° chevron angle have attained a higher percentage of 15.14%, 7.8% and 15.4%, respectively in comparison with the base fluid. Consequently, when the volume concentration or Reynolds number increases, the heat transfer coefficient and the overall heat transfer coefficient as well as the heat transfer rate of the PHE (Plate Heat Exchangers) increases respectively. Similarly, the pressure drop increases with the volume concentration. 60° chevron angle showed better performance in comparison with 30° chevron angle.

AbstractIn aviation, accurate wind prediction is crucial, especially during takeoff and landing at complex sites like Gran Canaria Airport. This study evaluated five Deep Learning models: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), One-Dimensional Convolutional Neural Network (1dCNN), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), and Gated Recurrent Unit (GRU) for forecasting wind speed and direction. The LSTM model demonstrated the highest precision, particularly for extended forecasting periods, achieving a mean absolute error (MAE) of 1.23 m/s and a circular MAE (cMAE) of 15.80° for wind speed and direction, respectively, aligning with World Meteorological Organization standards for Terminal Aerodrome Forecasts (TAF). While the GRU and CNN-LSTM also showed promising results, and the 1dCNN excelled in wind direction forecasting over shorter intervals, the vRNN lagged in performance. Additionally, the autoregressive integrated moving average model underperformed relative to the DL models, underscoring the potential of DL, particularly LSTM, in enhancing TAF accuracy at airports with intricate wind patterns. This study not only confirms the superiority of DL over traditional methods but also highlights the promise of integrating artificial intelligence into TAF automation.

This study breaks down carbon emissions into six effects within the current 28 European Union (EU) countries group, thereafter, they are divided into two different groups (the first 15 countries (EU-15) and the last 13 entering the EU (EU13)). Country-specific highlights are also examined. It analyses the evolution of the effects using a data span that runs from 1990 to 2014, to determine which of them had more impact on the intensity of emissions, while also breaking down the complete period into two distinct periods (before the Kyoto protocol (1990-2004) and after Kyoto (2005-2014)). In order to add more knowledge to the current literature, both the additive and multiplicative decomposition techniques were used to examine carbon dioxide (CO2) emissions and the selected six components: carbon intensity, fossil fuel consumption, energy intensity, oil imports intensity, oil dependence, and population effect. Results point to different adapting velocities for Kyoto targets and necessary compromises. The different velocities were translated into different positive and negative impacts in the change of behavior of CO2 emissions throughout Europe. A stress in the fluctuations in CO2 variations before and after Kyoto and between the two different groups of EU countries could be noticed. Moreover, energy intensity and per capita dependence of oil products were identified as the major responsible components for the total and negative changes of emissions in recent years. A decrease in total changes of emissions is observed due to the fossil fuel energy consumption effect and total petroleum products effects. It is possible to infer from here that increased renewable capacity is contributing in a positive way to eco-efficiency, and should therefore be accounted for in national policymakers' decisions in the strongest way possible. Results also seem to indicate that per capita dependence of oil products has decreased, despite oil imports intensity constancy and increased renewable capacity, however, with clear heterogeneous effects, worthy of consideration when defining policies.

As the effects of global climate change become more apparent, animal species will become increasingly affected by extreme climate and its effect on the environment. There is a pressing need to understand animal physiological and behavioural responses to climatic stressors. We used the reactive scope model as a framework to investigate the influence of drought conditions on vervet monkey ( Chlorocebus pygerythrus ) behaviour, physiological stress and survival across 2.5 years in South Africa. Data were collected on climatic, environmental and behavioural variables and physiological stress via faecal glucocorticoid metabolites (fGCMs). There was a meaningful interaction between water availability and resource abundance: when food availability was high but standing water was unavailable, fGCM concentrations were higher compared to when food was abundant and water was available. Vervet monkeys adapted their behaviour during a drought period by spending a greater proportion of time resting at the expense of feeding, moving and social behaviour. As food availability decreased, vervet mortality increased. Peak mortality occurred when food availability was at its lowest and there was no standing water. A survival analysis revealed that higher fGCM concentrations were associated with an increased probability of mortality. Our results suggest that with continued climate change, the increasing prevalence of drought will negatively affect vervet abundance and distribution in our population. Our study contributes to knowledge of the limits and scope of behavioural and physiological plasticity among vervet monkeys in the face of rapid environmental change.