On recent computational results for a dynamic pickup and delivery problem

A few years ago, a dynamic pickup-and-delivery problem was introduced in the context of a competition Hao et al. ( ICAPS 2021 Competition). Since then, the problem has attracted the attention of many researchers. Recently, Zhou et al. (Memet Computing 16:1-8, 2014) proposed a solution approach to the problem, however, we found a flaw in their study that leads to a lot of misunderstanding. Our paper aims to clear up these misunderstandings. In this paper, we state and prove that contrary to their claim, Zhou et al. (Memet Computing 16:1-8, 2014) did not study and solve the original problem, but a relaxation of it. Accordingly, but without mentioning it, the authors modified the benchmark dataset and then applied state-of-the-art methods tailored to the original problem. Therefore, their published results are misleading as the authors did not clarify that the results are not for the original problem but for a relaxation

The Impact of Multitasking on Transport Mode Choice in Autonomous Vehicle Age

Travelers on board of transport modes conduct active and passive activities to mitigate the negative impact of traveling. Multitasking on board of conventional transport modes is studied by several researchers, but limited efforts are focusing on multitasking in case of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs). In this paper, the effects of multitasking on behavior of travelers on board of conventional cars (cars), AVs, and SAVs are analyzed. Furthermore, finding the impact of several factors on the travel time, the acceptability of SAVs, and conducting onboard activities are assessed. The study considers solely main trip purposes within urban areas. A stated preference (SP) survey is distributed in Budapest, Hungary, and 276 participants are collected. An SP includes discrete choice experiment (DCE) that is designed to mimic the realistic situation when AV is on the market. The DCE considers the attributes and attributes levels of the alternatives where rationality is maintained in the design. A transport mode choice model, which includes several variables influencing the choice of a transport mode, is developed. In addition, an SP includes Likert scale and sociodemographic sections. Likert scale and exploratory factor analysis (EPA) are used to understand the impact of some factors on the travel time, the acceptability of SAVs as transport modes, and conducting onboard activities. The multinomial logit (MNL) model is applied, where a transport mode choice model of cars, AVs, and SAVs is developed. The results of the developed model show that travelers are willing to choose AVs over cars, and SAVs over AVs. Moreover, travelers with high income are more willing to use AVs over SAVs and more likely to use cars than SAVs. Besides, people from the older age group prefer using SAVs more than other age groups. The results demonstrate the probability of selecting a transport mode with active activities on board is larger than the probability of choosing a transport mode with passive activities. Besides, the findings of EFA and Likert scale analyses demonstrate that the waiting time has the largest negative effect on the travel time, seat availability affects the conduction of onboard activities, and the internal design of SAVs influences the use of SAVs as transport modes. The results of current research can be beneficial to transport planners, transport operators, and vehicle manufacturers. Author

Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence

A delay in brain maturation is a hypothesized pathomechanism of attention-deficit/hyperactivity disorder (ADHD). Differences in emotion regulation are associated with phenotypic and prognostic heterogeneity in ADHD. The development of emotion regulation is driven, in part, by brain maturation. Whether the difference between an individual’s brain age predicted by machine-learning algorithms trained on neuroimaging data and that individual’s chronological age, i.e. brain-predicted age difference (brain-PAD) predicts differences in emotion regulation, and whether ADHD problems add to this prediction is unknown. Using data from the Adolescent Brain Cognitive Development Study, we examined, in 2711 children ( M age = 120.09 months, SD = 7.61; 54.15% female; 61.23% white), whether adjusting for action cancellation (inhibition), age, sex assigned at birth, psychotropic treatment, and pubertal status, brain-PAD in late childhood predicts self-reported emotion regulation in early adolescence (at 3-year follow-up), and whether parent-reported ADHD problems predict self-reported emotion regulation above and beyond brain-PAD. Greater brain-PAD predicted greater expressive suppression ( b = 0.172, SE = 0.051, p FDR = 0.004), whereas ADHD problems did not ( b = 0.041, SE = 0.022, p FDR = 0.124), model marginal R 2 = 0.020. This pattern of results was replicated across sensitivity tests. Neither brain-PAD, nor ADHD problems predicted cognitive reappraisal, p FDR s = 0.734. Clinically, consistent with earlier findings linking greater brain-PAD to psychopathology, we observed that greater brain-PAD in childhood—but not ADHD problems—predicted expressive suppression in early adolescence. Expressive suppression is implicated in the etiology, maintenance, and treatment of numerous psychopathologies, highlighting the relevance of brain-PAD in understanding developmental risk mechanisms. Conceptually, these findings further validate brain-PAD as a valuable tool for advancing developmental neuroscience

Linear Parameter Varying and Reinforcement Learning Approaches for Trajectory Tracking Controller of Autonomous Vehicles

This research focuses on controlling the motion trajectory of autonomous vehicles by using a combination of two high-performance control methods: Linear Parameter Varying (LPV) and Reinforcement Learning (RL). First, a single-track motion model is researched and developed with coordinate systems to determine the car's motion trajectory through signals from GPS. Then, the LPV control method is used to design a controller to control the car's motion trajectory. Reinforcement learning method with detailed training procedures is used to combine with the advantages of LPV controller. Finally, the simulation results are evaluated in the time domain through the use of specialized CarSim software, which clearly demonstrates the superiority of the research method

MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring

Automated tasks, mainly in the biomedical field, help to develop new technics to provide faster solutions for monitoring patients’ health status. For instance, they help to measure different types of human bio-signal, perform fast data analysis, and enable overall patient status monitoring. Eulerian Video Magnification (EVM) can reveal small-scale and hidden changes in real life such as color and motion changes that are used to detect actual pulse. However, due to patient movement during the measurement, the EVM process will result in the wrong estimation of the pulse. In this research, we provide a working prototype for effective artefact elimination using a face movement compensated EVM (MC-EVM) which aims to track the human face as the main Region Of Interest (ROI) and then use EVM to estimate the pulse. Our primary contribution lays on the development and training of two face detection models using TensorFlow Lite: the Single-Shot MultiBox Detector (SSD) and the EfficientDet-Lite0 models that are used based on the computational capabilities of the device in use. By employing one of these models, we can crop the face accurately from the video, which is then processed using EVM to estimate the pulse. MC-EVM showed very promising results and ensured robust pulse measurement by effectively mitigating the impact of patient movement. The results were compared and validated against ground-truth data that were made available online and against pre-existing solutions from the state-of-the-art

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy

Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise in early identification. This study characterizes Positive Schizotypy Factor (PSF) and Chronic Schizophrenia (CS) groups using actigraphic data from two databases. At Hauke Land University Hospital, data from patients with chronic schizophrenia were collected; separately, at the University of Szeged, healthy university students were recruited and screened for PSF tendencies toward schizotypy. Several types of features are extracted from both datasets. Machine learning algorithms using different feature sets achieved nearly 90-95% for the CS group and 70-85% accuracy for the PSF. By applying model-explaining tools to the well-performing models, we could conclude the movement patterns and characteristics of the groups. Our study indicates that in the PSF liability phase of schizophrenia, actigraphic features related to sleep are most significant, but as the disease progresses, both sleep and daytime activity patterns are crucial. These variations might be influenced by medication effects in the CF group, reflecting the broader challenges in schizophrenia research, where the drug-free study of patients remains difficult. Further studies should explore these features in the prodromal and clinical High-Risk groups to refine our understanding of the development of the disorder. © The Author(s) 2025

Asymptotic Stability of Delayed Complex Balanced Reaction Networks with Non-Mass Action Kinetics

We consider delayed chemical reaction networks with non-mass action monotone kinetics and show that complex balancing implies that within each positive stoichiometric compatibility class there is a unique positive equilibrium that is locally asymptotically stable relative to its class. The main tools of the proofs are respectively a version of the well-known classical logarithmic Lyapunov function applied to kinetic systems and its generalization to the delayed case as a Lyapunov–Krasovskii functional. Finally, we demonstrate our results through illustrative examples

A pénzmosás és a korrupció érzékelése

Aim: The purpose of this study is to explore and analyse the connections between corruption and money laundering. Sharing the results can contribute to effective action against the investigated phenomena.Methodology: The authors used Transparency International's corruption perception index and the anti-money laundering component of the Organized Crime index from public Internet databases. They were examined using statistical methods (descriptive statistics, ANOVA, correlation and linear regression) in a sample of 175 countries of the world.Findings: Increasing the effectiveness of the fight against money laundering generally reduces the perceived level of corruption. There is a well-explained, significant positive correlation between the two indicators. The perceived level of corruption is also influenced by other factors of organized crime with different effects from continent to continent.Value: The value of the research is given by the up-to-date nature of the data. The most important of its limitations is that the organized crime index has only been available since 2021. Another possible research direction is the examination of country groups according to national income, as well as the performance of time series analyses in parallel with the increase in the number of reports