Background: Although laparoscopic splenectomy (Lap-Spleen) has become the standard surgical approach for normal-sized spleens, open splenectomy (Open-Spleen) is still recommended by many in the setting of splenomegaly. We set out to compare the impact of spleen size on Lap-Spleen and Open-Spleen outcomes using a national database. Study Design: We reviewed the American College of Surgeons' National Surgical Quality Improvement Program database to identify patients who had undergone non-emergency splenectomy during 2005–2010. To evaluate the impact of spleen size on outcomes, we considered patients with diagnoses of splenomegaly and hypersplenism as those having large spleens (Large-Sp group) and those with diagnoses of primary thrombocytopenia and immune thrombocytopenic purpura as having normal spleens (Normal-Sp group). Patients were also categorized based on surgical approach into Lap-Spleen and Open-Spleen groups. Results: We identified 639 patients in the Large-Sp group and 879 patients in the Normal-Sp group. During 2005–2010 laparoscopy was used in 84.2% of cases in the Normal-Sp group (annual range, 77.8%–90.8%). However, the rate of laparoscopy in the Large-Sp group remained consistently below 50% with an average of 41.8% (annual range, 20%–47%). In the Lap-Spleen group, those with Large-Sp had longer operative time and length of stay and higher blood transfusion and morbidity compared with the Normal-Sp group. However, when looking specifically at the Large-Sp group, patients with Open-Spleen had more transfusion requirements, longer length of stay, and higher morbidity, compared with those with Lap-Spleen. Conclusions: Lap-Spleen leads to significant improvement in outcomes. These advantages were believed to be limited to normal-sized spleens, but this study demonstrates that laparoscopy can still be advantageous in patients with splenomegaly. We hope such data encourages wider utilization of laparoscopy in the setting of splenomegaly, especially among surgeons who are experienced with the technique.

The outbreak of the COVID-19 pandemic has led to significant alterations in people’s social and economic behaviour. This paper aims to study the pandemic’s influence on online shopping and travel behaviour and discover how these phenomena are related. To this end, eight variables were identified that describe socio-demographic status, COVID-19 variables, online shopping variables, and travel behaviour. The structural equation modelling (SEM) approach was adopted to analyse the relationships between these variables. A conceptual model was formed by devising hypothetical relationships, and then the validity and reliability of the model were evaluated using SEM tools. Among the 19 theoretical relationships, 17 were verified. It was found that socio-demographic status directly affects the COVID-19 variables, influencing online shopping variables. As a result, it was inferred that during the pandemic, people’s daily travel habits had been affected by their inclinations toward online shopping, and the more people are aware of COVID-19 and feel responsible about the pandemic, the more they are persuaded to shop online rather than in-person shopping. Policymakers can use the findings of this study to change the public’s travel and shopping behaviour to tackle the pandemic.

Changing people’s shopping behavior from face-to-face to online shopping during the COVID-19 pandemic led to reduced shopping trips, and this decrease directly affects traffic congestion and air pollution. Identifying the factors influencing the increase of online shopping behavior during the pandemic can be helpful for policymakers in the post-COVID-19 era. This study aims to discover the effect of factors related to the COVID-19 pandemic and demographic characteristics on shopping attitude and, consequently, on shopping trips. Based on the interviews of ten experts, factors associated with COVID-19 and demographic characteristics are selected as influential factors on shopping attitude and shopping trips. For pairwise comparisons between these factors, a web-based questionnaire was designed and given to thirty experts. The relationship between all factors is examined using interpretive structural modeling (ISM) and Microscopic–Macroscopic (MICMAC) analysis. In addition, to prioritize factors, the IAHP model is employed. Based on the results, five levels of influential factors affect shopping attitude, which affects shopping trips: level 1, age and gender; level 2, income and education; level 3, the household size and the COVID-19 awareness; level 4, COVID-19 attitude and COVID-19 practice; and level 5, norm subject and shopping personal control.

Accurate traffic speed prediction is crucial for efficient traffic management and planning in urban areas. Traditional traffic prediction models often fall short due to their inability to capture the complex and dynamic nature of traffic flow. There is a need for more advanced models that can effectively handle dynamic traffic conditions. This study introduces the multialgorithmic ensemble model (MAEM), a novel framework designed to improve traffic speed prediction accuracy by integrating graph neural networks (GNNs), bidirectional gated recurrent units (Bi‐GRUs), and long short‐term memory (LSTM) networks, to effectively analyze the spatiotemporal characteristics of the traffic network. The methodology involves constructing a virtual graph based on road segment correlations and applying a combination of spatial and temporal feature extraction techniques. The model is further enhanced with an attention mechanism to focus on critical time intervals. The dataset used for this study consists of one‐year aggregated probe vehicle traffic data of 4788 road segments in the City of Hamilton, Ontario. The results demonstrate significant performance, achieving the mean absolute percentage error (MAPE) of 3.5% and root‐mean‐square error (RMSE) of 2.4 km/h, indicating the potential of the proposed framework to significantly enhance traffic speed prediction accuracy and provide a reliable tool for urban traffic management and planning.

The car fleet's growth has caused many problems, such as environmental problems, uncontrolled energy consumption, and traffic congestions. To resolve the issues caused by transport-related activities, transportation planners should implement policies that reduce car use and move forward to sustainable transportation. The prerequisite of the efficiency of the policies that reduce car use is the acceptance of them by the entire community members. Previously a few studies have tested individuals' environmental attitudes using the Value-Belief-Norm (VBN) theory. This research aims to analyze and examine car use reduction policy using a comprehensive VBN and Norm-Activation-model (NAM) theories to consider more variables and provide a model with more analytical capability. To address this issue, 500 questionnaires were filled by travelers' in Tehran, Iran, in the restricted traffic area to describe their behavior. The result shows that the biospheric value significantly affects car use reduction policy, associated with considerable environmental concerns. Besides, it was found that a high ascription of responsibility (A.R.) appeared as the strongest predictor of the policy. The findings have represented initial support for the VBN theory and revealed that the theory could explain the car use reduction policy's Intention, according to Tehran's respondents' environmental behavior. This study's findings can assist policymakers in adopting appropriate policies aiming at reducing car use based on environmental advantages and could targeting biospheric values and ascription of responsibilities.