The KdV is one of the most widespread nonlinear models, commonly used by coastal engineers, to describe wave propagation in weakly nonlinear and weakly dispersive shallow water problems. In this paper, we investigate the solutions of the extended KdV equation, previously formally derived for both the velocity and the free-surface elevation, by analyzing its consistency with the extended Boussinesq equations. Combined with a stability result, this leads to a convergence result. This analysis enables us to derive new linear asymptotic models based on this equation. We then explicitly solve these new linear models and illustrate the residual behavior of the resulting approximate solutions through numerical computations for representative initial data. Finally, we analyze solitary-wave solutions of the extended KdV model and quantify its improved accuracy.
Myasthenia gravis (MG) is a heterogeneous autoimmune neuromuscular disorder with distinct serological subtypes, including antibodies against acetylcholine receptors (AChRs), MuSK, and LRP4. Despite increasing recognition of these subtypes, clinical practice still lacks a standardized, subtype-specific approach to diagnosis, characterization, and management. Current treatment strategies are often applied uniformly, without fully accounting for differences in disease phenotype, prognosis, and therapeutic response among antibody-defined groups. This review provides a comprehensive overview of adult MG, focusing on the various autoantibody subtypes and their implications for pathogenesis, clinical features, diagnosis, and management. We highlight how combining serological findings with clinical subtyping can inform a more personalized approach to therapy and better align treatment decisions with disease biology. By emphasizing the clinical relevance of serological classification, this review aims to bridge the gap between immunopathological understanding and individualized patient care in MG.
Greenhouses are considered the best practice for protected cultivation. Crops require a certain amount of heating to thrive and withstand the effects of climate change. This study develops a comprehensive, general numerical model to determine heating loads for three Lebanese climate zones: the Coastal area (Akkar), the Bekaa Valley (Zahle), and Mount Lebanon (Aley), capturing sophisticated, realistic physical phenomena. The uncontrolled inside temperature profile model is iteratively developed to represent the possible diseases crops may face without the recommended control. The low-cost experimental setup is explained to validate the uncontrolled temperature variation inside an eggplant and green pepper greenhouse in the 'Berkayel/Akkar' zone. Results identify the representative days with the highest probability over the 10 years (2014-2024) for each region. The uncontrolled temperature analysis successfully predicted frost damage in the 'Zahle' zone. Results show that the heating load for tomatoes is the highest in 'Zahle', around 95 kW (60 kW), and the lowest in 'Akkar', up to 60 kW (28 kW), to maintain 18 degrees C (12 degrees C) inside the greenhouse. The air-source heat pump is sized accordingly and the annual heating demands are computed for different balance point (10-16 degrees C) and set point (12 and 18 degrees C) temperatures. The developed model for uncontrolled temperature is validated using a cost-effective experimental setup, with error ranges of 1-11%. This developed approach will help companies/farmers in any country estimate the required control system, tailored to their goals, for any crop or climate zone, to ensure food safety and sustainable agriculture.
Asthma remains a major health challenge affecting over 300 million people worldwide, with severe, steroid-resistant phenotypes affecting 5–10
PurposeThis paper aims to evaluate energy efficiency in the Gulf Cooperation Council (GCC) countries between 2000 and 2023, taking into consideration some factors that determines energy consumption level in this region.Design/methodology/approachThis study contributes to the ongoing debates on energy efficiency through a nonparametric Data Envelopment Analysis (DEA) approach, based on the Slack Based Measure (SBM) model with CO2 emissions as undesirable output. Moreover, to improve the robustness of the results, the analysis was complemented with the Bootstrap-DEA approach.FindingsThe results showed that the average inefficiency level in the GCC region was approximately 10%, with significant variation between countries. As a second stage analysis, the fixed-effects panel data model also reveals crucial economic and environmental factors that may affect efficiency.Originality/valueThe novelty of this study lies in the use of SBM model, which is a non-radial DEA approach that enables the authors to identify inefficiencies with higher precision as compared to traditional radial DEA models. The SBM model enables the inclusion of CO2 emissions as a "bad output" and leads to a more accurate and realistic measure of energy efficiency, considering environmental negative externalities.
