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Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations.

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Background: Diarrheal infections continue to be a major public health concern in Bangladesh, especially in urban areas where population density and environmental variables increase dissemination risks. Identifying the intricate connections between weather variables and diarrhea epidemics is critical for developing effective public health remedies.

Methods: We deploy the novel approach of Wavelet-Autoregressive Integrated Moving Average with Exogenous Variable (WARIMAX) and the traditional Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) technique to forecast the incidence of diarrhea by analyzing the influence of climate factors.

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In recent years, the prevalence of bullying among university students in Indonesia has continued to increase. The consequences of bullying have detrimental effects on the victims, highlighting the importance of finding ways to reduce bullying behavior. However, few studies have explored the factors that can mitigate this behavior among students.

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To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm's global optimization capability.

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Background: Fitness to maintain an ideal body shape is becoming increasingly popular among college students. However, it also leads to many mental health issues. This study explores the pathways through which negative body image affects depression in college students.

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