Limited Window Research Articles

Climate change perception and mental health: the public health perspective

Abstract Climate change is increasingly recognized as a critical environmental stressor with significant psychological impacts. Despite consolidated evidence describing the impact of various aspects of climate change on numerous physical and mental health outcomes, a novel and burgeoning area of research is now assessing the association between climate change perception/awareness and health. In fact, the increasing awareness of climate change and its impacts on natural and human systems is not only a matter of environmental concern but also of significant psychological importance. Awareness of climate change involves understanding its causes, foreseeing its long-term global and local impacts, and recognizing the limited window available for effective mitigation and adaptation. This level of awareness, while necessary for proactive behavior, has been linked to various psychological responses, including chronic stress, anxiety, and feelings of helplessness and despair, collectively referred to as ‘eco-anxiety.’ The association between climate change awareness and mental health is complex and mediated by various individual and social factors. As the reality of climate change becomes more integrated into the public consciousness, it is imperative to address the accompanying psychological impacts. In this presentation, the main issues related to climate change perception/awareness and mental health will be discussed through the lens of public health.

Segmentation window of speech information processing in the human auditory cortex

Humans perceive continuous speech signals as discrete sequences. To clarify the temporal segmentation window of speech information processing in the human auditory cortex, the relationship between speech perception and cortical responses was investigated using auditory evoked magnetic fields (AEFs). AEFs were measured while participants heard synthetic Japanese words /atataka/. There were eight types of /atataka/ with different speech rates. The durations of the words ranged from 75 to 600 ms. The results revealed a clear correlation between the AEFs and syllables. Specifically, when the durations of the words were between 375 and 600 ms, the evoked responses exhibited four clear responses from the superior temporal area, M100, that corresponded not only to the onset of speech but also to each group of consonant/vowel syllable units. The number of evoked M100 responses was correlated to the duration of the stimulus as well as the number of perceived syllables. The approximate range of the temporal segmentation window limit of speech perception was considered to be between 75 and 94 ms. This finding may contribute to optimizing the temporal performance of high-speed synthesized speech generation systems.

Novel Silicate Platform as Weakly-Coordinating Anions for Diverse Organometallic and Electrochemical Applications.

Weakly-coordinating anions (WCAs) are employed in a wide range of applications, but limitations remain, including high reactivity, limited redox window, complicated synthesis, high cost, low solublity, and low structural tunabililty. Herein, we report a new class of WCA based onalkyl or aryl (R)substitutedsilicates bearingfluorinated pinacolate ligands,"[RSiF24]-".Anions bearing a variety of R groups were prepared, enabling facile tuning of sterics and solubility. A range of cations employed in chemical reactivity has been supported by these anions, including ether-free alkali cations, Ag+, Ph3C+, Fc+, [NiI(COD)2]+. [Pd(dppe)(NCMe)Me]+has been generated by salt metathesis or protonation of a metal-alkyl bond, showcasing the ability of theRSiF24-anions to support applications in coordination chemistry and catalysis. Electrochemical studies on the [Bu4N]+variant show an exceptionally wide stability window for theMeSiF24-anion of 7.5 V in MeCN. CV experiments demonstrate reversible Mg deposition and stripping.

Novel Silicate Platform as Weakly‐Coordinating Anions for Diverse Organometallic and Electrochemical Applications

Weakly‐coordinating anions (WCAs) are employed in a wide range of applications, but limitations remain, including high reactivity, limited redox window, complicated synthesis, high cost, low solublity, and low structural tunabililty. Herein, we report a new class of WCA based on alkyl or aryl (R) substituted silicates bearing fluorinated pinacolate ligands, "[RSiF24]‐". Anions bearing a variety of R groups were prepared, enabling facile tuning of sterics and solubility. A range of cations employed in chemical reactivity has been supported by these anions, including ether‐free alkali cations, Ag+, Ph3C+, Fc+, [NiI(COD)2]+. [Pd(dppe)(NCMe)Me]+ has been generated by salt metathesis or protonation of a metal‐alkyl bond, showcasing the ability of the RSiF24‐ anions to support applications in coordination chemistry and catalysis. Electrochemical studies on the [Bu4N]+ variant show an exceptionally wide stability window for the MeSiF24‐ anion of 7.5 V in MeCN. CV experiments demonstrate reversible Mg deposition and stripping.

Positioning comfort measures in antenatal counselling for periviable infants.

Caring for the extremely premature infant born in the grey zone of viability is the most difficult area of neonatal medicine. Little research has been done on antenatal communication between neonatologists and parents anticipating the birth of a periviable infant. This article analyses 25 antenatal consultations between neonatologists and parents in one Midwestern hospital in the United States of America. It explores how neonatologists position comfort care as one of two predominant care trajectories for extremely premature infants born into the grey zone of viability. We found comfort care featured minimally in and was often marginalised by neonatologists' language. The two dominant discourses contributing to this were acute medicine's life-saving capacity and a limited temporal window marked by gestational age where comfort measures were deemed appropriate. Antenatal consultations framed by shared decision-making could be approached as a form of care characterised by a relational openness and responsiveness to parents' views on care. This asks neonatologists to enter antenatal consultations for periviability without knowing ahead of time which care trajectory will necessarily call one's attention or the particular response one should take, thus highlighting the skills of reflexivity in addition to an attentiveness and openness towards those receiving care.

Advancements, Challenges, and Future Prospects in Clinical Hyperpolarized Magnetic Resonance Imaging: A Comprehensive Review

Hyperpolarized (HP) magnetic resonance imaging (MRI) is a groundbreaking imaging platform advancing from research to clinical practice, offering new possibilities for real-time, non-invasive metabolic imaging. This review explores the latest advancements, challenges, and future directions of HP MRI, emphasizing its transformative impact on both translational research and clinical applications. By employing techniques such as dissolution Dynamic Nuclear Polarization (dDNP), Parahydrogen-Induced Polarization (PHIP), Signal Amplification by Reversible Exchange (SABRE), and Spin-Exchange Optical Pumping (SEOP), HP MRI achieves enhanced nuclear spin polarization, enabling in vivo visualization of metabolic pathways with exceptional sensitivity. Current challenges, such as limited imaging windows, complex pre-scan protocols, and data processing difficulties, are addressed through innovative solutions like advanced pulse sequences, bolus tracking, and kinetic modeling. We highlight the evolution of HP MRI technology, focusing on its potential to revolutionize disease diagnosis and monitoring by revealing metabolic processes beyond the reach of conventional MRI and positron emission tomography (PET). Key advancements include the development of novel tracers like [2-13C]pyruvate and [1-13C]-alpha-ketoglutarate and improved data analysis techniques, broadening the scope of clinical metabolic imaging. Future prospects emphasize integrating artificial intelligence, standardizing imaging protocols, and developing new hyperpolarized agents to enhance reproducibility and expand clinical capabilities particularly in oncology, cardiology, and neurology. Ultimately, we envisioned HP MRI as a standardized modality for dynamic metabolic imaging in clinical practice.

Superconcentration Strategy Allows Sodium Metal Compatibility in Deep Eutectic Solvents for Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) are a more sustainable alternative to lithium-ion batteries (LIBs) considering the abundance, global distribution, and low cost of sodium. However, their economic impact remains small compared to LIBs, owing in part to the lag in materials development where significant improvements in energy density and safety remain to be realized. Deep eutectic solvents (DESs) show promise as alternatives to conventional electrolytes in SIBs because of their nonflammable nature. However, their practical application has thus far been hindered by their limited electrochemical stability window. In particular, DESs based on N-methylacetamide have thus far been reported not to be stable with sodium metal. In contrast, this work reports a superconcentration strategy where sodium-ion conducting DESs, based on the dissolution of NaFSI in N-methylacetamide, are simultaneously stable with sodium metal and Prussian blue as state-of-the-art positive electrode material. At 60 °C, the nonflammable DES outperforms a conventional liquid electrolyte in terms of rate performance and capacity retention. Therefore, these novel DES compositions pave the way for the use of DESs in practical applications with an improved safety and sustainability.

Development and user experience evaluation of an Experience Sampling-Based Dietary Assessment Method

BackgroundMost technology-based dietary assessment methods use the same methodology as traditional dietary assessment methods resulting in similar limitations and biases. Experience Sampling Methodology (ESM) is a real-life real-time data-capturing method that is explored as an alternative methodology for dietary assessment to improve feasibility and data accuracy. ObjectiveThis research aimed to develop and evaluate an experience sampling-based dietary assessment method (ESDAM) measuring habitual dietary intake. MethodsStarting from a food frequency questionnaire, experience sampling principles were implemented resulting in a pilot ESDAM. Second, the pilot ESDAM was evaluated for feasibility and convergent validity compared to a 3-day food record. Mean intake with Standard Deviations was compared between the pilot ESDAM, Food Record (FR), and FFQ and Spearman's Correlation Coefficients were calculated. Third, following a literature review and expert opinion, the questions and design of the pilot ESDAM were further adapted to ESM and implemented in an experience sampling survey application. The resulting prototype ESDAM underwent 2 rounds of user experience (UX) evaluation in which 10 persons tested ESDAM for one week followed by a structured evaluation interview. ResultsThe pilot ESDAM, FR, and FFQ were completed by 28 participants and the evaluation questionnaire by 78 participants. Mean energy intake by the FFQ, pilot ESDAM, and FR was 1272.2 ± 308.9 kcal/day, 1592.3 ± 358.9 kcal/day, and 1664.6 ± 257.8 kcal respectively. The evaluation revealed the limited time window (7-11 PM) to respond was inconvenient, good acceptability, and ease-of-use of the pilot ESDAM. The UX evaluation study revealed overall good acceptability, ease of use, and low burden of the different prototypes of ESDAM. ConclusionESM could advance the field beyond traditional methodologies and improve feasibility. ESDAM is unique in assessing dietary intake quantitatively through ESM. Additional assessment of validity might shed light on the data accuracy of ESDAM.

Individual-Level Exposure to Residential Redlining in 1940 and Mortality Risk.

Historic redlining, the practice by the Home Owners' Loan Corporation (HOLC) of systematically denying credit to borrowers in neighborhoods that were inhabited by primarily African American individuals, has been associated with poor community outcomes, but the association with individual risk of death is not clear. To examine if exposure to residential redlining practices by HOLC in 1940 is associated with increased risk of death later in life. The study linked individuals who resided within HOLC-graded neighborhoods (defined as Census Enumeration Districts) in 1940 with administrative death records data. The study estimated hazard ratios as well as age-specific life expectancy gaps (at age 55, 65, and 75 years) for HOLC grading exposure. This was done using methods that adapted standard parametric survival analysis to data with limited mortality coverage windows and incomplete observations of survivors. The analysis sample consisted of 961 719 individual-level observations across 13 912 enumeration districts within 30 of the largest US cities (based on 1940 population counts) across 23 states. Data were analyzed between December 1, 2023, and September 4, 2024. The exposure was HOLC grade based on historic HOLC maps, with A representing "best" or creditworthy areas; B, "still desirable"; C, "definitely declining"; and D, "hazardous" areas not worthy of credit (ie, redlined), and the main outcome was age at death from the Social Security Numident file. The 961 719-person individual sample had a mean (SD) age of 19.26 (9.26) years in 1940 and a mean (SD) age at death of 76.83 (9.22) years. In a model adjusted for sex (52.48% female; 47.52% male), race and ethnicity (7.36% African American; 92.64% White), and latent place effects, a 1-unit lower HOLC grade was associated with an 8% (hazard ratio, 1.08 [95% CI, 1.07-1.09]) increased risk of death. At age 65 years, these hazard differentials translated into an estimated life expectancy gap of -0.49 (95% CI, -0.56 to -0.43) years for each 1-unit decrease of the HOLC grade. This study found that individuals who resided within redlined neighborhoods in 1940 had lower life expectancy later in life than individuals who resided within other HOLC-graded areas.

Intravenous Thrombolysis with Urokinase for Acute Ischemic Stroke.

Intravenous thrombolysis is one of the most effective therapies for the treatment of acute ischemic stroke (AIS), with urokinase offering a cost-effective alternative to newer agents like alteplase and tenecteplase, especially in resource-limited settings. This review provides a comprehensive overview of the application of intravenous thrombolysis with urokinase for AIS in the clinical practice of stroke management, including the efficacy, safety, and cost-effectiveness of urokinase compared to other thrombolytic agents. Urokinase, a first-generation thrombolytic drug, is a non-specific plasminogen activator that offers a cost-effective alternative. It has been used in clinical practice for over two decades to improve neurological outcomes in patients with AIS if administered within 6 h of ictus. Numerous studies have indicated that urokinase remains a viable option for patients who cannot access alteplase or tenecteplase because of economic constraints, time window limitations, availability, or other reasons. In low- and middle-income countries, urokinase is a cost-effective alternative thrombolytic drug. High-level evidence-based medical research is therefore urgently needed to confirm that urokinase is not inferior to new-generation thrombolytic drugs, and to assess whether it may even be superior in some patient populations.

Recurrent neural network-aided processing of incomplete free induction decays in 1H-MRS of the brain

In the case of limited sampling windows or truncation of free induction decays (FIDs) for artifact removal in proton magnetic resonance spectroscopy (1H‐MRS) and spectroscopic imaging (1H‐MRSI), metabolite quantification needs to be performed on incomplete FIDs. Given that FIDs are naturally time-domain sequential data, we investigated the potential of recurrent neural network (RNN)-types of neural networks (NNs) in the processing of incomplete human brain FIDs with or without FID restoration prior to quantitative analysis at 3.0T.First, we employed an RNN encoder-decoder and developed it to restore incomplete FIDs (rRNN) with different amounts of sampled data. The quantification of metabolites from the rRNN-restored FIDs was achieved by using LCModel. Second, we modified the RNN encoder-decoder and developed it to convert incomplete brain FIDs into incomplete metabolite-only FIDs without restoration, followed by linear regression using a metabolite basis set for quantitative analysis (cRNN). In consideration of the practical benefit of the FID restoration with respect to pure zero-filling, development and analysis of the NNs were focused particularly on the incomplete FIDs with only the first 64 data points retained. All NNs were trained on simulated data and tested mainly on in vivo data acquired from healthy volunteers (n = 27).Strong correlations were obtained between the NN-derived and ground truth metabolite content (LCModel-derived content on fully sampled FIDs) for myo‐inositol, total choline, and total creatine (normalized to total N-acetylaspartate) on the in vivo data using both rRNN (R = 0.83–0.94; p ≤ 0.05) and cRNN (R = 0.86–0.91; p ≤ 0.05).RNN-types of NNs have potential in the quantification of the major brain metabolites from the FIDs with substantially reduced sampled data points. For the metabolites with low to medium SNR, the performance of the NNs needs to be further improved, for which development of more elaborate and advanced simulation techniques would be of help, but remains challenging.

Utilizing diffusion tensor imaging as an image biomarker in exploring the therapeutic efficacy of forniceal deep brain stimulation in a mice model of Alzheimer’s disease

Objective.With prolonged life expectancy, the incidence of memory deficits, especially in Alzheimer's disease (AD), has increased. Although multiple treatments have been evaluated, no promising treatment has been found to date. Deep brain stimulation (DBS) of the fornix area was explored as a possible treatment because the fornix is intimately connected to memory-related areas that are vulnerable in AD; however, a proper imaging biomarker for assessing the therapeutic efficiency of forniceal DBS in AD has not been established.Approach.This study assessed the efficacy and safety of DBS by estimating the optimal intersection volume between the volume of tissue activated and the fornix. Utilizing a gold-electroplating process, the microelectrode's surface area on the neural probe was increased, enhancing charge transfer performance within potential water window limits. Bilateral fornix implantation was conducted in triple-transgenic AD mice (3 × Tg-AD) and wild-type mice (strain: B6129SF1/J), with forniceal DBS administered exclusively to 3 × Tg-AD mice in the DBS-on group. Behavioral tasks, diffusion tensor imaging (DTI), and immunohistochemistry (IHC) were performed in all mice to assess the therapeutic efficacy of forniceal DBS.Main results.The results illustrated that memory deficits and increased anxiety-like behavior in 3 × Tg-AD mice were rescued by forniceal DBS. Furthermore, forniceal DBS positively altered DTI indices, such as increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD), together with reducing microglial cell and astrocyte counts, suggesting a potential causal relationship between revised FA/MD and reduced cell counts in the anterior cingulate cortex, hippocampus, fornix, amygdala, and entorhinal cortex of 3 × Tg-AD mice following forniceal DBS.Significance.The efficacy of forniceal DBS in AD can be indicated by alterations in DTI-based biomarkers reflecting the decreased activation of glial cells, suggesting reduced neural inflammation as evidenced by improvements in memory and anxiety-like behavior.

Special Considerations in the Orthopaedic Trauma Patient: Curbing Negative Social Behaviors

Optimizing the orthopaedic trauma patient for surgical intervention presents unique challenges. Due to the acute nature of musculoskeletal injuries and time constraints for treatment, there is a limited window for overall patient comorbidity optimization prior to surgical fracture repair. Certain social behaviors including tobacco use, excessive alcohol consumption, and inappropriate opioid medication usage can have a negative impact on wound and fracture healing. Curbing such negative social behaviors can have a positive outcome on patients who are affected by musculoskeletal trauma. Addressing these challenging issues, via available resources, can have a significant impact on recovery after sustaining orthopaedic trauma. A tactical approach focused on relationship building with patients and their families as well as highlighting strategies to curb negative social behaviors is presented involving a patient with an infected tibial nonunion.

Chip-scale optical phased array for broadband two-dimensional beam steering at visible wavelengths

An optical phased array (OPA) on a chip, as a novel non-mechanical beam steering technique, has attracted much attention in various applications. However, due to the challenges in fabrication and the limited transparency window of the material for visible wavelengths, current OPAs are mainly focuses on the infrared wavelengths. Despite the progress, the visible light OPAs remain the challenges to not only simultaneously work in blue, green and red light, but also implement beam steering in two dimensions for the single-wavelength. In this paper, an 8-channel broadband OPA is demonstrated, which enables two-dimensional beam steering in visible light. Along the longitudinal direction, the beam steering angle of 0.7° at 585 nm is achieved by tuning phase shifts. Along the transverse direction, the demonstrated OPA implements beam steering angle of 4.49° at 585 nm by tuning polarization degrees and 25.97° at 488–610 nm by tuning wavelengths. Therefore, the demonstrated OPA holds significant promise for commercial applications such as visual projection and free-space optical communications.

Global Insights on Prehospital Stroke Care: A Comprehensive Review of Challenges and Solutions in Low- and Middle-Income Countries.

Stroke is a leading cause of disability and mortality worldwide, and it disproportionately affects low- and middle-income countries (LMICs), which account for 88% of stroke fatalities. Prehospital stroke care delays are a crucial obstacle to successful treatment in these settings, especially given the limited therapeutic window for thrombolytic treatments, which may greatly improve recovery chances when initiated early after stroke onset. These delays are caused by a lack of public understanding of stroke symptoms, sociodemographic and cultural variables, and insufficient healthcare infrastructure. This review discusses these issues in detail, emphasizing the disparities in stroke awareness and reaction times between locations and socioeconomic classes. Innovative options for reducing these delays include the deployment of mobile stroke units and community-based educational campaigns. This review also discusses how technology improvements and personalized educational initiatives might improve stroke awareness and response in LMICs. The primary goal is to give a thorough assessment of the challenges and potential remedies that might serve as the foundation for policy reforms and healthcare improvements in LMICs, eventually improving stroke care and lowering disease-related mortality and disability.

The anxious and resilient European Union: experiencing FOMO in the increasingly geopolitical world order

ABSTRACT In this paper, we draw on insights from the literature on social psychology and marketing in order to account for the emotional mechanism through which resilience became a key aspect of the EU’s strategy in world politics. We argue that faced with the deep uncertainty created by multiple and overlapping crises, the EU’s response was shaped by its experience of the ‘fear of missing out’ (FOMO). FOMO represents the intersubjective process through which actors compare their own experience with those of their peers, and experience anxiety due to the realisation that they are not benefitting from similar opportunities or managing risks and challenges as effectively as their peers in the context of limited windows of opportunity. The article presents a model for understanding FOMO as a cycle that occurs under conditions of uncertainty and can be used to study other settings.

Near Infrared Aggregation Induced Electrogenerated Chemiluminescence Studies of Xanthene Dye

Electrogenerated chemiluminescence (ECL) serves as a versatile electrochemical-spectroscopic tool for rapid sample analysis, offering high sensitivity, good selectivity, and a low detection limit. During electrochemical redox reactions of luminophores and often ECL co-reactants, excited state luminophore species are formed through high energetic electron transfer between oxidized and reduced luminophore radicals or between the radical cation or anion and co-reactant intermediates. This process emits photons, generating ECL. Near infrared (NIR) luminophores represent an emerging class of materials in ECL studies. However, greater vibronic coupling between the ground and excited state in NIR luminophores with a lower band gap promotes nonradiative decay. The aggregation-induced emission (AIE) phenomenon overcomes these challenges, demonstrating enhanced radiative emission. We conducted a study on the photophysical, electrochemical, and ECL behavior of XanthCR-880 dye, which comprises a thienylpiperidine donor and a xanthene core as an acceptor. The central xanthene core provides extended conjugated length and increased charge transfer, resulting in NIR absorption at 880 nm and fluorescence emission at 960 nm, with a stroke shift of 80 nm. Cyclic voltammetry performed at a Pt electrode in MeCN with a scan rate of 50 mVs-1 revealed two oxidation waves and two reduction waves at 0.35 V, 0.70 V, -0.70 V, and -1.55 V vs Ag/Ag+, respectively. Due to the limited potential window of the Pt electrode in MeCN (not exceeding -2.5 V, a boron-doped diamond (BDD) electrode was employed at more negative potentials, uncovering an additional reduction wave at -2.62 V vs Ag/Ag+. The cathodic and anodic ECL behavior of XanthCR-880 dye was investigated using benzoyl peroxide and 2-(dibutylamino)ethanol as cathodic and anodic co-reactants, respectively. For cathodic ECL, XanthCR-880 dye produced two ECL waves at the BDD electrode, with peak potentials of -2.06 V and -2.36 V, respectively. In anodic ECL, relatively weak emission was observed at 1.05 V. ECL spectra were generated using the cathodic ECL pathway and interference filters as wavelength selectors, revealing a maximum emission at ~975 nm. The proposed ECL mechanism, as well as AIE studies, will also be discussed.

Extreme Cycling Longevity in Additive Free T-Nb2O5 over a Quarter Million Lithiation Cycles

Rapid energy storage via intercalation requires quick ionic diffusion and often results in pseudocapacitive behavior. The long-term cycling stability of such energy storage materials remains understudied despite the relevance to their lifetime cost and market feasibility. Orthorhombic niobium oxide (T-Nb2O5)is a rapid ion intercalation material with a theoretical lithiation capacity of 201.7 mAh g-1 (Li2Nb2O5) and good cycling stability due to the minimal unit cell strain during (de)intercalation. Prior reports of T-Nb2O5 cycling between 1.3-3.1 V vs. Li/Li+ noted a 50% loss in capacity after 10k cycles, stemming from a convolved effect of amorphization, dissolution, and/or delamination. Here, cyclic voltammetry is used to identify the role of the voltage window, state of charge (SOC), and potentiostatic holds on the cycling stability of mesoporous T-Nb2O5 thin films. Films cycled at high-rate between 1.2-3.0 V vs. Li/Li+ without voltage holds (kinetically limited SOC of Li1.1Nb2O5) exhibited extreme cycling stability with 90.8% ± 2.1% capacity retention after a quarter million cycles without detectable morphological/crystallographic changes.1 In contrast, inclusion of 60 s voltage holds (SOC of Li2.18Nb2O5) led to rapid capacity loss with only a 61.6% retention after 10k cycles and corresponding x-ray diffraction evidence of amorphization. Cycling with other limited voltage windows identified that most crystallographic degradation occurred at higher extents of lithiation (<1.4 V vs Li/Li+). These results reveal remarkable stability, up to 25x higher than what has been previously observed, over limited conditions and suggest that T-Nb2O5 amorphization is associated with high extents of lithiation. [1] Wechsler, S. C.; Gregg, A.; Stefik, M. Highly Reversible Lithiation of Additive Free T-Nb2O5 for a Quarter of a Million Cycles. Adv. Funct. Mater. 2023, ASAP. 10.1002/adfm.202312839.

Combining Bayesian Networks and MCDA methods to maximise information gain during reconnaissance in emergency situations

In the immediacy of an event that disrupts the operation of an infrastructure, the time between its occurrence and the arrival of qualified personnel for emergency response can be valuable. For example, it can be used for gathering information about the status of the infrastructure by using automated reconnaissance devices. In an operation which precedes the intervention of human first responders, such devices can gather information about the situation, providing knowledge about the locations of stressors (e.g. fire), about the inaccessibility of parts of the infrastructure or about the presence of hazardous materials. In this study, we show how a Bayesian Networks can be used for knowledge representation and how it can be combined with methods from the realm of Multi-Criteria Decision Analysis (MCDA) for situation reconnaissance and route-optimisation in emergency situations, where different criteria (current belief about the location of zones of special interest, such as emergency exits, distance to the next point of interest, etc.) can be considered. As an example, we consider the case of an outbreak of a fire in a building. A pedantic check of all rooms by an automated reconnaissance device would take too long and thus delay intervention. Due to the limited time in which the building can be explored, the route is optimised to gather the greatest possible amount of information in the available time window. Results show how it is possible to maximise the information collected in a limited time window. This is done by discovering the location of fire and any hazardous materials through causal inferences automatically calculated by the Bayesian network. Route optimisation is facilitated by sequential MCDA using a parameter selection that meets the priorities of the specific application example.

Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using NTDB Data.

Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention. This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention. The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications. Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning's potential to improve emergency responses and decision-making in trauma care.