Uplink NOMA for UAV-Aided Maritime Internet-of-Things

Maritime activities are vital for economic growth, being further accelerated by various emerging maritime Internet of Things (IoT) use cases, including smart ports, autonomous navigation, and ocean monitoring systems. However, broadband, low-delay, and reliable wireless connectivity to the ever-increasing number of vessels, buoys, platforms and sensors in maritime communication networks (MCNs) has not yet been achieved. Towards this end, the integration of unmanned aerial vehicles (UAVs) in MCNs provides an aerial dimension to current deployments, relying on shore-based base stations (BSs) with limited coverage and satellite links with high latency. In this work, a maritime IoT topology is examined where direct uplink communication with a shore BS cannot be established due to excessive pathloss. In this context, we employ multiple UAVs for end-to-end connectivity, simultaneously receiving data from the maritime IoT nodes, following the non-orthogonal multiple access (NOMA) paradigm. In contrast to other UAV-aided NOMA schemes in maritime settings, dynamic decoding ordering at the UAVs is used to improve the performance of successive interference cancellation (SIC), considering the rate requirements and the channel state information (CSI) of each maritime node towards the UAVs. Moreover, the UAVs are equipped with buffers to store data and provide increased degrees of freedom in opportunistic UAV selection. Simulations reveal that the proposed opportunistic UAV-aided NOMA improves the average sum-rate of NOMA-based maritime IoT communication, leveraging the dynamic decoding ordering and caching capabilities of the UAVs

Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach

The goal of the study presented in this paper is to evaluate the performance of a proposed Reinforcement Learning (RL) power allocation algorithm. The algorithm follows a demand-driven power adjustment approach aiming at maximizing the number of users inside a coverage area that experience the requested throughput to accommodate their needs. In this context, different Quality of Service (QoS) classes, corresponding to different throughput demands, have been taken into account in various simulation scenarios. Considering a realistic network configuration, the performance of the RL algorithm is tested under strict user demands. The results suggest that the proposed modeling of the RL parameters, namely the state space and the rewarding system, is promising when the network controller attempts to fulfill the user requests by regulating the power of base stations. Based on comparative simulations, even for strict demands requested by multiple users (2.5 – 5 Mbps), the proposed scheme achieves a performance rate of about 96%

A Stacking Ensemble Learning Model for Waste Prediction in Offset Printing

The production of quality printing products requires a highly complex and uncertain process, which leads to the unavoidable generation of printing defects. This common phenomenon has severe impacts on many levels for Offset Printing manufacturers, ranging from a direct economic loss to the environmental impact of wasted resources. Therefore, the accurate estimation of the amount of paper waste expected during each press run, will minimize the paper consumption while promoting environmentally sustainable principles. This work proposed a Machine Leaning (ML) framework for proactively predicting paper waste for each printing order. Based on a historical dataset extracted by an Offset Printing manufacturer, a two-level stacking ensemble learning model combining Support Vector Machine (SVM), Kernel Ridge Regression (KRR) and Extreme Gradient Boosting (XGBoost) as base learners, and Elastic Net as a meta-learner, was trained and evaluated using cross-validation. The evaluation outcomes demonstrated the ability of the proposed framework to accurately estimate the amount of waste expected to be generated for each printing run, by significantly outperforming the rest of the benchmarking models

Deciding on Optical Illusions: Reduced Alpha Power in Body Dysmorphic Disorder

Background: Body dysmorphic disorder (BDD) is a psychiatric disorder characterized by excessive preoccupation with imagined defects in appearance. Optical illusions induce illusory effects that distort the presented stimulus, thus leading to ambiguous percepts. Using electroencephalography (EEG), we investigated whether BDD is related to differentiated perception during illusory percepts. Methods: A total of 18 BDD patients and 18 controls were presented with 39 optical illusions together with a statement testing whether or not they perceived the illusion. After a delay period, they were prompted to answer whether the statement was right/wrong and their degree of confidence in their answer. We investigated differences of BDD patients on task performance and self-reported confidence and analyzed the brain oscillations during decision-making using nonparametric cluster statistics. Results: Behaviorally, the BDD group exhibited reduced confidence when responding incorrectly, potentially attributed to higher levels of doubt. Electrophysiologically, the BDD group showed significantly reduced alpha power at the fronto-central and parietal scalp areas, suggesting impaired allocation of attention. Interestingly, the lower the alpha power of the identified cluster, the higher the BDD severity, as assessed by BDD psychometrics. Conclusions: Results evidenced that alpha power during illusory processing might serve as a quantitative EEG biomarker of BDD, potentially associated with reduced inhibition of task-irrelevant areas

Evaluating the Modulation of the Acoustic Startle Reflex in Children and Adolescents via Vertical EOG and EEG: Sex, Age, and Behavioral Effects

Acoustic startle reflex (ASR) constitutes a reliable, cross-species indicator of sensorimotor and inhibitory mechanisms, showing distinct signature in cognitive aging, sex, and psychopathological characterization. ASR can be modulated by the prepulse inhibition (PPI) paradigm, which comprises the suppression of reactivity to a startling stimulus (pulse) following a weak prepulse (30- to 500-ms time difference), being widely linked to inhibitory capabilities of the sensorimotor system. If the prepulse–pulse tones are more clearly separated (500–2,000 ms), ASR amplitude is enhanced, termed as prepulse facilitation (PPF), reflecting sustained or selective attention. Our study aimed to investigate early-life sensorimotor sex/age differences using Electroencephalographic recordings to measure muscular and neural ASR in a healthy young population. Sixty-three children and adolescents aged 6.2–16.7 years (31 females) took part in the experiment. Neural ASR was assessed by two different analyses, namely, event-related potentials (ERPs) and first-derivative potentials (FDPs). As expected, PPF showed enhanced responses compared with PPI, as indicated by eyeblink, ERP and FDP measures, confirming the gating effect hypothesis. Sex-related differences were reflected in FDPs, with females showing higher ASR than males, suggesting increased levels of poststartle excitability. Intragroup age effects were evaluated via multipredictor regression models, noticing positive correlation between age versus eyeblink and ERP responses. Attention-related ERPs (N100 and P200) showed distinct patterns in PPI versus PPF, potentially indicative for alternative attentional allocation and block-out of sensory overload. Screening measures of participants’ neurodevelopmental (assessed by Wechsler Intelligence Scale for Children) and behavioral (assessed by Child Behavior Checklist) markers were also associated with increased N100/P200 responses, presumably indexing synergy between perceptual consistency, personality profiling, and inhibitory performance. Conclusively, modulation of ASR by PPI and PPF is associated with biological sex and internal/external personality traits in childhood and adolescence, potentially useful to guide symptomatology and prevention of psychopathology

Direct oral anticoagulant-vs vitamin k antagonist-related nontraumatic intracerebral hemorrhage

Objective: To compare the neuroimaging profile and clinical outcomes among patients with intracerebral hemorrhage (ICH) related to use of vitamin K antagonists (VKAs) or direct oral anticoagulants (DOACs) for nonvalvular atrial fibrillation (NVAF). Methods: We evaluated consecutive patients with NVAF with nontraumatic, anticoagulant-related ICH admitted at 13 tertiary stroke care centers over a 12-month period. We also performed a systematic review and meta-analysis of eligible observational studies reporting baseline characteristics and outcomes among patients with VKA- or DOAC-related ICH. Results: We prospectively evaluated 161 patients with anticoagulation-related ICH (mean age 75.6 ± 9.8 years, 57.8% men, median admission NIH Stroke Scale [NIHSSadm] score 13 points, interquartile range 6–21). DOAC-related (n = 47) and VKA-related (n = 114) ICH did not differ in demographics, vascular risk factors, HAS-BLED and CHA2DS2-VASc scores, and antiplatelet pretreatment except for a higher prevalence of chronic kidney disease in VKA-related ICH. Patients with DOAC-related ICH had lower median NIHSSadm scores (8 [3–14] vs 15 [7–25] points, p = 0.003), median baseline hematoma volume (12.8 [4–40] vs 24.3 [11–58.8] cm3, p = 0.007), and median ICH score (1 [0–2] vs 2 [1–3] points, p = 0.049). Severe ICH (>2 points) was less prevalent in DOAC-related ICH (17.0% vs 36.8%, p = 0.013). In multivariable analyses, DOAC-related ICH was independently associated with lower baseline hematoma volume (p = 0.006), lower NIHSSadm scores (p = 0.022), and lower likelihood of severe ICH (odds ratio [OR] 0.34, 95% confidence interval [CI] 0.13–0.87, p = 0.025). In meta-analysis of eligible studies, DOAC-related ICH was associated with lower baseline hematoma volumes on admission CT (standardized mean difference = −0.57, 95% CI −1.02 to −0.12, p = 0.010) and lower in-hospital mortality rates (OR = 0.44, 95% CI 0.21–0.91, p = 0.030). Conclusions: DOAC-related ICH is associated with smaller baseline hematoma volume and lesser neurologic deficit at hospital admission compared to VKA-related ICH