Journal of Forensic Research

Background: Abdominal injuries due to seatbelt have often occurred in motor vehicle passengers. Although seatbelt-induced moderate and severe abdominal injuries have been reported, factors associated with the occurrence of severe abdominal injuries were not examined.

Objectives: To clarify the pattern and severity of seatbelt-induced abdominal injuries and determine the predictive factors of severe abdominal injuries.

Methods: Among the data set of the National Automotive Sampling System/Crashworthiness Data System of collisions that occurred from 1995 to 2011, we chose right front-seat passengers with a height of >140 cm who developed seatbelt-induced abdominal injuries with an Abbreviated Injury Scale score of ≥ 2 during a frontal collision.

Results: The study population comprised 79 persons (24 male, 55 female; mean age, 37.5 years) with 136 abdominal injuries. Spleen, kidney, and liver injuries were the three most common seatbelt-induced injuries. The spleen was the most commonly injured site in both the moderate and severe injury groups. The liver was the second most commonly injured site in the moderate injury group, but no liver injuries occurred in the severe injury group (p=0.001). The multivariable logistic regression analysis revealed that force to the lower abdomen positively influenced the occurrence of severe injuries and that airbag deployment negatively influenced the occurrence of severe injuries.

Conclusions: Correct seatbelt use and airbag deployment can prevent severe seatbelt-induced abdominal injuries. Forensic pathologists should still suspect moderate abdominal injuries even in correct restrained vehicle passengers.

Fluorescence sensors have emerged as invaluable tools in medical diagnostics and forensic science, owing to their rapidity, sensitivity, and versatility. This article explores the recent advancements in fluorescence sensor technology, focusing on their applications in medical and forensic examination. We delve into the principles behind fluorescence sensing, highlight the key features of fast and accurate fluorescence sensors, and discuss their potential impact on improving healthcare and forensic investigations.

Traffic congestion is a perennial challenge in urban environments, with traffic signal control systems playing a crucial role in managing flow and reducing congestion. Traditional traffic signal control systems often lack adaptability and responsiveness to dynamic traffic conditions. In recent years, advancements in Artificial Intelligence (AI) have paved the way for innovative approaches to traffic signal control. One such approach is the integration of value decomposition techniques with communication-multiple agent approval systems. This article explores the synergy between these methodologies and their potential to revolutionize traffic signal control, leading to more efficient and adaptive traffic management solutions.