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Dexmedetomidine- or Clonidine-Based Sedation Compared With Propofol in Critically Ill PatientsThe A2B Randomized Clinical Trial
No full textImportance: Whether α2-adrenergic receptor agonist–based sedation, compared with propofol-based sedation, reduces time to extubation in patients receiving mechanical ventilation in the intensive care unit (ICU) is uncertain.Objective: To evaluate whether dexmedetomidine- or clonidine-based sedation reduces duration of mechanical ventilation compared with propofol-based sedation (usual care).Design, Setting, and Participants: Pragmatic, open-label randomized clinical trial conducted at 41 ICUs in the UK including adults who were within 48 hours of starting mechanical ventilation, were receiving propofol plus an opioid for sedation and analgesia, and were expected to require mechanical ventilation for 48 hours or longer. The median time from intubation to randomization was 21.0 (IQR, 13.2-31.3) hours. Recruitment occurred from December 2018 to October 2023; the last follow-up occurred on December 10, 2023.Interventions: The bedside algorithms used targeted a Richmond Agitation-Sedation Scale score of −2 to 1 (unless clinicians requested deeper sedation). The algorithms supported uptitration in the dexmedetomidine- and clonidine-based sedation intervention groups and supported downtitration for propofol-based sedation followed by sedation primarily with the allocated sedation (dexmedetomidine or clonidine). If required, supplemental use of propofol was permitted.Main Outcomes and Measures: The primary outcome was time from randomization to successful extubation. The secondary outcomes included mortality, sedation quality, rates of delirium, and cardiovascular adverse events.Results: Among the 1404 patients in the analysis population (mean age, 59.2 [SD, 14.9] years; 901 [64%] were male; and the mean APACHE II score was 20.3 [SD, 8.2]), the subdistribution hazard ratio (HR) for time to successful extubation was 1.09 (95% CI, 0.96-1.25; P = .20) for dexmedetomidine (n = 457) vs propofol (n = 471) and was 1.05 (95% CI, 0.95-1.17; P = .34) for clonidine (n = 476) vs propofol (n = 471). The median time from randomization to successful extubation was 136 (95% CI, 117-150) hours for dexmedetomidine, 146 (95% CI, 124-168) hours for clonidine, and 162 (95% CI, 136-170) hours for propofol. In the predefined subgroup analyses, there were no interactions with age, sepsis status, median Sequential Organ Failure Assessment score, or median delirium risk score. Among the secondary outcomes, agitation occurred at a higher rate with dexmedetomidine vs propofol (risk ratio [RR], 1.54 [95% CI, 1.21-1.97]) and with clonidine vs propofol (RR, 1.55 [95% CI, 1.22-1.97]). Compared with propofol, the rates of severe bradycardia (heart rate <50/min) were higher with dexmedetomidine (RR, 1.62 [95% CI, 1.36-1.93]) and clonidine (RR, 1.58 [95% CI, 1.33-1.88]). Compared with propofol, mortality was similar over 180 days for dexmedetomidine (HR, 0.98 [95% CI, 0.77-1.24]) and clonidine (HR, 1.04 [95% CI, 0.82-1.31]).Conclusions and Relevance: In critically ill patients, neither dexmedetomidine nor clonidine was superior to propofol in reducing time to successful extubation.Trial Registration: ClinicalTrials.gov Identifier: NCT0365383
Enhancing human activity recognition with TB-ConvAtt: A multi-dimensional attention framework
No full textThe growing prevalence of wearable technology in healthcare highlights the essential need for accurate and efficient human activity recognition (HAR) using wearable sensor data. In clinical settings, HAR plays a pivotal role in patient monitoring, rehabilitation, and personalized healthcare management. This study introduces TB-ConvAtt, a lightweight and multi-dimensional framework that integrates Convolutional Neural Networks (CNNs) with specialized attention mechanisms to effectively balance the extraction of independent temporal, spatial, and spatio-temporal features from wearable multi-sensor data. TB-ConvAtt consists of three distinct branches: the Temporal Attention Dimension (TAD), the Spatial Attention Dimension (SAD), and the Spatio-temporal Attention Dimension (STAD). The performance of TB-ConvAtt is thoroughly evaluated on four public HAR datasets (UNIMIB-SHAR, OPPORTUNITY, PAMAP2, MHEALTH). Comparative studies and detailed ablation experiments demonstrate that TB-ConvAtt achieves state-of-the-art performance while maintaining a lightweight design, enabling efficient deployment in resource-constrained environments
Improving DOA estimation of GNSS interference through sparse non-uniform array reconfiguration
Get PDFInterference significantly impacts the performance of the Global Navigation Satellite Systems (GNSS), highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities. The Uniform Circular Array (UCA) enables concurrent estimation of the Direction of Arrival (DOA) in both azimuth and elevation. Given the paramount importance of stability and real-time performance in interference localization, this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation. The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA. To ensure DOA estimation accuracy, minimizing the Cramér-Rao Bound (CRB) is the objective, and the Spatial Correlation Coefficient (SCC) is incorporated as a constraint to mitigate side-lobe. The optimization model is a quadratic fractional model, which is solved by Semi-Definite Relaxation (SDR). When the array has perturbations, the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array. Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA, showing high robustness and reductions in hardware and computational costs associated with DOA estimation
Vulnerability to trafficking in persons in the context of the war in Ukraine. Findings from Moldova
No full textThis report presents the findings of a research study assessing the vulnerability of Ukrainian refugees in the Republic of Moldova to exploitation, abuse, and violence, including human trafficking. Commissioned in the context of the ongoing displacement crisis caused by the full-scale invasion of Ukraine in February 2022, the study responds to concerns regarding the protection needs of displaced populations
Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning
Get PDFIntroduction: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection. Methods: This study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. Traditional methods for training machine learning and deep learning models raises data privacy concerns and require significant computational resources. To address these issues, the study applies a split learning framework. In this framework, an ensemble learning technique has been utilized that combines the best performing machine and deep learning models. Results: Results demonstrate a commendable classification performance with certain ensemble methods, and a Transformer-Random Forest combination achieved 99% accuracy. In addition, to address data-sharing constraints, a split learning framework is implemented across three clients, yielding high accuracy (over 95%) while preserving privacy. The best client recorded 96.23% accuracy, underscoring the robustness of combining Transformers with Random Forest under resource-constrained conditions. Discussion: These findings demonstrate that distributed deep learning pipelines can deliver precise MDD detection from EEG data without compromising data security. Proposed framework keeps data on local nodes and only exchanges intermediate representations. This approach meets institutional privacy requirements while providing robust classification outcomes
Experience-based integral reinforcement learning consensus for unknown multi-agent systems
Get PDFThis paper investigates an optimal consensus control problem and proposes a policy iteration algorithm based on online integral reinforcement learning for nonlinear multi-agent systems with unknown dynamics. Introducing a critic-actor neural network into the traditional policy iteration avoids the identification of unknown dynamics. To address the issue of local optima in online learning, an experience-based weight-tuning law is introduced to ensure the persistence of excitation conditions during the training phase. The theoretical results show that the system is asymptotically stable, and the network weights converge. Finally, the effectiveness and correctness are verified by several simulation studies
Resource-efficient sliding mode control for unmanned aerial vehicles with multiple faults and hybrid cyber attacks
Get PDFThis paper investigates resource-efficient sliding mode control for a quadrotor unmanned aerial vehicle (UAV) subjected to multiple faults and hybrid cyber-attacks. We derive a comprehensive dynamic model that captures actuator-bias faults, partial failures, and false data injection attacks. Based on this, we design adaptive event-triggered terminal sliding mode controllers for both position and attitude control. To counter denial-of-service (DoS) attacks, we develop a forgetting factor compensation scheme that mitigates the effects of DoS attacks occurring in both feedback and forward channels. Lyapunov analysis guarantees closed-loop stability of the proposed schemes, precludes Zeno phenomena, and improves robustness to the combined false-data-injection (FDI) attacks and DoS attacks. Several simulations demonstrate that the proposed method maintains fast and accurate trajectory tracking while significantly reducing the impact of DoS and FDI attacks
Obesity: A call to action
Get PDFObesity has emerged as one of the most pressing public health challenges of the 21st century, impacting millions worldwide and contributing to serious health complications such as type 2 diabetes and cardiovascular diseases, as well as a diminished quality of life. This editorial explores the multifaceted nature of obesity, emphasizing the interplay between genetic predisposition, environmental constraints and behavioral drivers. Key contributors, such as the rising consumption of ultra-processed foods, increasingly sedentary lifestyles and psychosocial stressors, are explored in detail, along with their combined impact on the escalating global obesity rates. The editorial highlights the far-reaching consequences of obesity, including its economic burden, societal implications and the ripple effects on healthcare systems. Priority areas for action are proposed, including public health policies, education and the creation of environments that support active lifestyles. The importance of clinical interventions, such as early screening, personalized treatment strategies and the inclusion of dietitians within multidisciplinary care teams, is emphasized as vital for enhancing patient outcomes and managing obesity effectively. This editorial calls for a comprehensive, systemic response to address the global obesity epidemic, advocating for evidence-based interventions that are tailored to individual needs while addressing societal and environmental determinants. By fostering collaboration across sectors and prioritizing prevention and treatment, meaningful progress can be made in combating this escalating crisis
Carbon markets and firms’ perceived climate regulatory risk
Get PDFThis study examines how involvement in emissions trading schemes (ETS) affects firm climate regulatory risks (FCRR) across 36 countries from 2003 to 2021. We find a positive link between ETS membership and FCRR. Furthermore, we investigate how governance structures and firm-specific factors influence this relationship. Our analysis indicates that factors such as financial constraints, CEO network size, CEO tenure, the number of independent directors, and board size can lessen the impact of ETS membership on FCRR. Conversely, highercorporate political risk, membership in carbon-intensive industries, and a greater number of co-opted board members intensify this effect. Early participation in the scheme appears to reduce the firms’ climate regulatory risk, while subsequent withdrawal increases it. Notably, the influence of ETS on FCRR is mainly observed amongfirms operating in developed economies. Legislative shocks, such as the EU Climate and Energy Package, diminish the positive effect of the ETS on FCRR. Overall, our findings highlight the sensitivity of firm-level climate regulatory risk to strategic decisions regarding ETS participation and exit
Ecohydrological Indicators and Environmental Flow Assessment (EFA) in the Inlet and Outlet Reaches of the Kenyir Lake Basin, Malaysia
Get PDFThe balance of environmental flow in basin-maintained ecosystems is crucial for sustaining biodiversity and the environment. Maintaining optimal environmental flow in rivers ensures the sustainability of natural ecosystems. An Environmental Flow Assessment (EFA) was conducted in the Terengganu River (outlet) and Petuang River (inlet) to assess whether river flow is sufficient to support ecological and biodiversity needs. The study aimed to develop a hydrological-hydrodynamic model to determine Environmental Flow Values (E-Flow) and to use ecohydrological indicators for restoration and rehabilitation in the Kenyir Lake basin. Sampling was carried out during both the dry and normal seasons. Data were collected on hydrology (water level and river discharge), hydrodynamics (using XPSWMM software), and ecology (fish sampling and Length-Weight Relationship (LWR)). Three sampling stations were selected on each river, with the fish species Toman (Channa micropeltes), Sebarau (Hampala macrolepidota), and Belida (Chitala lopis) chosen as bioindicators. These species were selected based on their size (width, length, and weight), which indicates their tolerance to Environmental Flow Values. A 7Q20 low-flow analysis revealed that in the Terengganu River, the optimum discharge was 42.78 m³/s, with a depth of 3.94 m and a water velocity of 0.54 m/s, supporting the needs of larger fish species. Meanwhile, the Petuang River's optimum discharge was 0.08 m³/s, with a depth of 0.4 m and a water velocity of 0.04 m/s, which could only accommodate small fish species. These low-flow values, with an error margin of less than 20%, were used as inputs in the low-flow analysis. The study highlights the importance of E-Flow in maintaining river health. This holistic assessment, based on Integrated Water Resources Management (IWRM), supports sustainable ecosystem management using green physical structures to optimize environmental flow
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