Determining the role of statins in Parkinson's disease risk reduction and disease modification: A comprehensive meta-analysis of 4 million participants' data.

Background: Many observational studies have examined the association between statins and the incidence of Parkinson's disease (PD) in high-risk populations. On the other hand, clinical trials as well as other observational studies investigated the safety and efficacy of statins in slowing disease progression in PD patients. However, the evidence has been inconclusive in both questions.

Leveraging cross-view geo-localization with ensemble learning and temporal awareness.

The Global Navigation Satellite System (GNSS) is unreliable in some situations. To mend the poor GNSS signal, an autonomous vehicle can self-localize by matching a ground image against a database of geotagged aerial images. However, this approach has challenges because of the dramatic differences in the viewpoint between aerial and ground views, harsh weather and lighting conditions, and the lack of orientation information in training and deployment environments.

Impact of COVID-19 on youth's mental health in Egypt.

Background: Youth represent 21% of the Egyptian population; such proportion can create a leading demographic power for economic development and transition. However, with the current COVID-19 pandemic, everyone is exposed to more than usual stressors, adding a burden to their mental health and well-being.

Aim: This study aims to understand the pandemic's effect on youth's mental health in Egypt to strengthen the intervention areas needed to tackle such issues.

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning.

In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck's capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points.

Hybrid pointer networks for traveling salesman problems optimization.

In this work, we proposed a hybrid pointer network (HPN), an end-to-end deep reinforcement learning architecture is provided to tackle the travelling salesman problem (TSP). HPN builds upon graph pointer networks, an extension of pointer networks with an additional graph embedding layer. HPN combines the graph embedding layer with the transformer's encoder to produce multiple embeddings for the feature context.

Crash severity analysis of vulnerable road users using machine learning.

Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist.

A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems.

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers.

A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries.

Advancements in data collection and processing methods have produced large databases containing high quality vehicular data. Despite this, conventional vehicle-vehicle collisions remain difficult to identify due to their rarity. Therefore, there is a need to identify potential collisions given the introduction of these new data collection methods.

Correction: A framework for testing independence between lane change and cooperative intelligent transportation system.

[This corrects the article DOI: 10.1371/journal.pone.

Circulating glial fibrillary acidic protein and ubiquitin carboxy-terminal hydrolase-L1 as markers of neuronal damage in children with epileptic seizures.

Background: Epilepsy is a common neurological disease that has a negative impact on physical, social, and cognitive function. Seizure-induced neuronal injury is one of the suggested mechanisms of epilepsy complications. We aimed to evaluate the circulating level of glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1) as markers of neuronal damage in children with epilepsy and its relation to epilepsy characteristics.

Association of folic acid, vitamin B12, and intelligence scores in epileptic children.

Epilepsy is a serious childhood disease associated with cognitive impairment. Our aim was to investigate the possible association of serum folic acid, vitamin B12, and intelligence scores in epileptic children. A group of 30 children with established diagnosis of idiopathic epilepsy for at least one year as well as another group of 30 nonepileptic healthy children as the control group were recruited for analysis.

A framework for testing independence between lane change and cooperative intelligent transportation system.

Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events.

Experimental dataset for optimising the freight rail operations.

The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E.