High Uncertainty Research Articles

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early pCR prediction. In this study, we investigated the feasibility of the early prediction of pCR to neoadjuvant chemotherapy using multi-time point DCEMR images and clinical data acquired in the I-SPY2 trial. The prediction uncertainty was also quantified to allow physicians to make patient-specific decisions on treatment plans based on the level of associated uncertainty. The dataset used in our study included 624 patients with DCEMR images acquired at 3 time points before the completion of the chemotherapy: pretreatment (T0), after 3 cycles of treatment (T1), and after 12 cycles of treatment (T2). A convolutional long short-term memory (LSTM) network-based deep learning model, which integrated multi-time point deep image representations with clinical data, including tumor subtypes, was developed to predict pCR. The performance of the model was evaluated via the method of nested 5-fold cross validation. Moreover, we also quantified prediction uncertainty for each patient through test-time augmentation. To investigate the relationship between predictive performance and uncertainty, the area under the receiver operating characteristic curve (AUROC) was assessed on subgroups of patients stratified by the uncertainty score. By integrating clinical data and DCEMR images obtained at three-time points before treatment completion, the AUROC reached 0.833 with a sensitivity of 0.723 and specificity of 0.800. This performance was significantly superior (p<0.01) to models using only images (AUROC=0.706) or only clinical data (AUROC=0.746). After stratifying the patients into eight subgroups based on the uncertainty score, we found that group #1, with the lowest uncertainty, had a superior AUROC of 0.873. The AUROC decreased to 0.637 for group #8, which had the highest uncertainty. The results indicate that our convolutional LSTM network-based deep learning model can be used to predict pCR earlier before the completion of chemotherapy. By combining clinical data and multi-time point deep image representations, our model outperforms models built solely on clinical or image data. Estimating prediction uncertainty may enable physicians to prioritize or disregard predictions based on their associated uncertainties. This approach could potentially enhance the personalization of breast cancer therapy.

Model predictive control with self-learning capability for automated demand response in buildings

This paper presents an optimal management strategy, called Building Optimizer, based on a Model Predictive Control (MPC) approach with self-learning capabilities for buildings. The proposed MPC is a key enabler of cooperative demand response strategies at community level, ensuring the allocation of an optimal demand profile at each participating member of the community according to an optimal consumption reference defined by a complementary agent at community level. In this way, the proposed solution can contribute to provide demand response services exploiting the unlocked residential flexibility. The MPC calculates the optimal setpoints of the HVAC system’s terminal units, considering the expected usage of the buildings and the outdoor conditions, and exploiting the building’s thermal inertia. The models embedded in the MPC are grey-box models representing a thermal zone of the building. An adaptive MPC is proposed to mitigate the high uncertainty present in building management due to the time-variant characteristics of the building and the uncertain internal heat gains due to occupancy and other disturbances. For that, the reduced models incorporate self-learning capabilities implemented as Moving Horizon Estimators that perform a continuous calibration based on real-time measurements. This solution allows full automation for model calibration and management of the terminal units. This paper presents a case study to assess the thermal comfort assurance and power flexibility provision potential of the proposed solution. First, the Building Optimizer is compared to a regular benchmark consisting of an on–off controller, evaluating the performance of each controller for indoor temperature tracking. For the assessment of the flexibility provision, a baseline MPC with fixed model parameters obtained by an offline calibration is used for comparison to the Building Optimizer with self-learning capabilities. Different flexibility scenarios are simulated, considering different restrictions regarding thermal comfort and various flexibility request signals. Notably, the Building Optimizer outperforms the baseline MPC in all scenarios, particularly guaranteeing thermal comfort. Incorporating self-learning capabilities enhances controller performance by mitigating the effect of uncertainty, allowing the Building Optimizer to shift up to 13.08% of peak periods into valley periods, and tracking a flexibility request scenario of 10% with no discomfort.

Probabilistic Quantile Factor Analysis

This article extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of low, medium, and high economic policy uncertainty, as well as loose, median, and tight financial conditions. We show that the high uncertainty and tight financial conditions indexes have superior predictive ability for various measures of economic activity. In a high-dimensional exercise involving about 1000 daily financial series, we find that quantile factors also provide superior out-of-sample information compared to mean or median factors.

Media Literacy Interventions Improve Resilience to Misinformation: A Meta-Analytic Investigation of Overall Effect and Moderating Factors

The widespread dissemination of misinformation has become a global concern. A recommended solution is to improve people’s ability to discern true from false information through appropriate media literacy education programs. This meta-analysis quantitatively synthesized the results of 49 experimental studies ( N = 81,155) that examined the efficacy of media literacy interventions in mitigating misinformation. This study finds that media literacy interventions generally improve resilience to misinformation ( d = 0.60). Specifically, the interventions reduce belief in misinformation ( d = 0.27), improve misinformation discernment ( d = 0.76), and decrease misinformation sharing ( d = 1.04). Moreover, media literacy interventions have stronger effects (1) when multiple sessions rather than a single session are implemented, (2) in high (vs. low) uncertainty avoidance cultures, and (3) among college students than among adults recruited from online crowdsourcing platforms (e.g., Amazon Mechanical Turk). These findings enrich our understanding of inoculation theory and provide valuable guidance for the design of future media literacy intervention programs.

Uncertainty-based knowledge distillation for Bayesian deep neural network compression

Deep learning models have been widely employed across various fields. In real-world scenarios, especially safety-critical applications, quantifying uncertainty is as crucial as achieving high accuracy. To address this concern, Bayesian deep neural networks (BDNNs) emerged to estimate two different types of uncertainty: Aleatoric and Epistemic. Nevertheless, implementing a BDNN on resource-constrained devices poses challenges due to the substantial computational and storage costs imposed by approximation inference techniques. Thus, efficient compression methods should be utilized. We propose an uncertainty-based knowledge distillation method to compress BDNNs. Knowledge distillation is a model compression technique that involves transferring knowledge from a complex network, known as the teacher network, to a simpler one, referred to as the student network. Our method incorporates uncertainty into knowledge distillation to address situations where inappropriate teacher supervision undermines compression performance. We utilize the Epistemic uncertainty of teacher predictions to tailor supervision for each sample individually to take into account teacher's limited knowledge. Additionally, we adjust the temperature parameter of the distillation process for each sample based on the Aleatoric uncertainty of the teacher predictions, ensuring that the student receives appropriate supervision even in the presence of ambiguous data. As a result, the proposed method enables the Bayesian student network to be trained under both appropriate supervision of the Bayesian teacher network and ground truth labels. We evaluated our method on the CIFAR-10, CIFAR-100, and RAF-DB datasets, demonstrating notable improvements in accuracy over state-of-the-art knowledge distillation-based methods. Furthermore, the robustness of our approach was assessed through testing weakly trained teacher networks and the analysis of blurred and low-resolution data, which have high uncertainty. Experimental results show that the proposed method outperformed existing methods.

Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids

Soil information is critical for a wide range of land resource and environmental decisions. These decisions will be compromised when the soil information quality is unsatisfactory. Thus, users of soil information need to understand and consider the uncertainty of the available soil information and be able to judge whether it is fit for purpose. The uncertainty information provided with the SoilGrids2.0 product was examined in a case study. We hypothesised that the soil property predictions for the Netherlands (NL) might be less uncertain than those of New Zealand (NZ) because there were more relevant training data for NL than for NZ. The study objectives were to: 1)understand whether the provided uncertainty information is correct for both countries; 2)explore spatial patterns and relationships in the prediction error and uncertainty information using quantitative tools and new graphical analyses; 3)analyse whether these patterns and relations can be explained; and 4)explore how the uncertainty information and insights derived from graphical analyses might assist an end user to determine whether a map is suitable for their purpose. The study focused on soil texture.Independent datasets showed that the SoilGrids 2.0 uncertainty information was too optimistic for sand and too pessimistic for clay for both countries. The graphical analyses confirmed the initial assumption that NL predictions were more accurate than those for NZ, but they also indicated that some locations in NL have high uncertainty. The graphical analyses allowed only a limited identification of the four sources of uncertainty in digital soil maps, but were quite insightful in helping us to better understand the reliability of the information. A set of recommendations was developed for both producers and consumers of digital soil mapping (DSM) products. This includes the provision of a summary map of accuracy classes. We suggest that more research and educational effort is needed to ensure that digital soil maps are used appropriately.

Research on dangerous flight weather prediction based on machine learning

Abstract With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

Innovative Soft Cryptosystem for Encrypting and Decrypting Messages with IDEA

This paper introduces an advanced cryptosystem for message encryption and decryption, integrating the International Data Encryption Algorithm (IDEA) with soft set and soft matrix principles. Soft sets, conceptualized by Molodtsov, are adept at managing uncertainty, making them ideal for cryptographic frameworks. Our approach utilizes inverse and characteristic products of soft sets and matrices, combined with the robust IDEA algorithm, to significantly enhance security. The IDEA algorithm operates on 64 -bit data blocks with a 128 -bit key, ensuring strong encryption. Additionally, we incorporate the symmetric to introduce complex permutations, further strengthening the cryptographic process by ensuring unique encryptions for identical plaintexts. This layered approach drastically improves resilience against unauthorized decryption. Practical applications demonstrate the system's efficacy, highlighting its superior security and reliability for data transmission in environments with high uncertainty. This innovative cryptosystem provides a formidable solution for contemporary encryption challenges.

Speed of sound for understanding metals in extreme environments

Knowing material behavior is crucial for successful design, especially given the growing number of next-generation energy, defense, and manufacturing systems operating in extreme environments. Specific applications for materials in extreme environments include fusion energy, semiconductor manufacturing, metal additive manufacturing, and aerospace. With increased applications, awareness of foundational science for materials in extreme environments is imperative. The speed of sound provides insights into phase boundaries, like shock-induced melting. Thermodynamic integration of the speed of sound enables the deduction of other desirable properties that are difficult to measure accurately, like density, heat capacity, and expansivity. Metrology advancements enable the speed of sound to be measured at extreme conditions up to 15 000 K and 600 GPa. This comprehensive review presents state-of-the-art sound speed metrology while contextualizing it through a historical lens. Detailed discussions on new standards and metrology best practices, including uncertainty reporting, are included. Data availability for condensed matter speed of sound is presented, highlighting significant gaps in the literature. A theoretical section covers empirically based theoretical models like equations of state and CALPHAD models, the growing practice of using molecular dynamics and density functional theory simulations to fill gaps in measured data, and the use of artificial intelligence and machine learning prediction tools. Concluding, we review how a lack of measurement methods leads to gaps in data availability, which leads to data-driven theoretical models having higher uncertainty, thus limiting confidence in optimizing designs via numerical simulation for critical emerging technologies in extreme environments.

Dispatch of Decentralized Energy Systems using Artificial Neural Networks: A Comparative Analysis with Emphasis on Training Methods

Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.

Methodological Challenges in Aligning EPDs with Whole Life Carbon Limits for Buildings: A B2B Approach

Abstract The environmental performance from the materials used in buildings is pivotal in reducing greenhouse gas (GHG) emissions from the building sector; buildings are in the top three of the world’s most significant contributors of GHG emissions and are responsible for one-fifth of the overall resource consumption. Alongside multiple countries enforcing legal GHG limits and requiring Life Cycle Assessment (LCA) for new buildings, the availability of product-level environmental data, known as Type III Environmental Product Declarations (EPDs) has increased exponentially. EPDs were originally used for Business-to-Business purposes but are now the main data source for building-level LCAs. However, this often comes with a large set of uncertainties, as EPDs are still evolving as a documentation approach, and not always readily applicable in the whole life cycle approach. There are a multitude of complex areas to engage into, this study focuses on how use-stage modules are documented in EPDs, and how varied approaches create further complexity and perils in relation to their use in LCA and regulations, in the sense of, potential leading to high uncertainties and wrongful interpretations. The study aims to address the methodological gaps associated with the use of EPDs as data inputs in legally binding LCA requirements particularly concerning modules B1-5, which constitute the embodied part of the use-stage. The findings reveal a significant margin of error if EPDs are not correctly implemented, underscoring the importance of the Business-to-Business documentation approach.

Modeling human activity comprehension at human scale: prediction, segmentation, and categorization.

Humans form sequences of event models-representations of the current situation-to predict how activity will unfold. Multiple mechanisms have been proposed for how the cognitive system determines when to segment the stream of behavior and switch from one active event model to another. Here, we constructed a computational model that learns knowledge about event classes (event schemas), by combining recurrent neural networks for short-term dynamics with Bayesian inference over event classes for event-to-event transitions. This architecture represents event schemas and uses them to construct a series of event models. This architecture was trained on one pass through 18 h of naturalistic human activities. Another 3.5 h of activities were used to test each variant for agreement with human segmentation and categorization. The architecture was able to learn to predict human activity, and it developed segmentation and categorization approaching human-like performance. We then compared two variants of this architecture designed to better emulate human event segmentation: one transitioned when the active event model produced high uncertainty in its prediction and the other transitioned when the active event model produced a large prediction error. The two variants learned to segment and categorize events, and the prediction uncertainty variant provided a somewhat closer match to human segmentation and categorization-despite being given no feedback about segmentation or categorization. These results suggest that event model transitioning based on prediction uncertainty or prediction error can reproduce two important features of human event comprehension.

Assessing the Influence of Zero‐Flow Threshold Choice for Characterising Intermittent Stream Hydrology

ABSTRACTZero‐flow recordings in gauged streamflow data are critically important for intermittent stream research. Acknowledging the high uncertainty in zero‐flow recordings, many studies pick a small number as zero‐flow threshold, below which the flow is considered to be zero. The choice of zero‐flow threshold is often arbitrary or unjustified, which leads us to wonder: would selecting a slightly different threshold change analysis result significantly? Here, we used a simple sensitivity analysis to assess how the choice of zero‐flow threshold impacts the calculated values of relevant metrics to intermittent stream research. Results show that these metrics tended to be more sensitive to lower zero‐flow thresholds, suggesting that even choosing a slightly different threshold could lead to meaningfully different results from the management perspective. This study highlights the need for reasonable justification of the choice of zero‐flow threshold and concludes with potential ways to reduce uncertainty in zero‐flow measurement.

Fuel-Efficient and Fault-Tolerant CubeSat Orbit Correction via Machine Learning-Based Adaptive Control

The increasing deployment of CubeSats in space missions necessitates the development of efficient and reliable orbital maneuvering techniques, particularly given the constraints on fuel capacity and computational resources. This paper presents a novel two-level control architecture designed to enhance the accuracy and robustness of CubeSat orbital maneuvers. The proposed method integrates a J2-optimized sequence at the high level to leverage natural perturbative effects for fuel-efficient orbit corrections, with a gated recurrent unit (GRU)-based low-level controller that dynamically adjusts the maneuver sequence in real-time to account for unmodeled dynamics and external disturbances. A Kalman filter is employed to estimate the pointing accuracy, which represents the uncertainties in the thrust direction, enabling the GRU to compensate for these uncertainties and ensure precise maneuver execution. This integrated approach significantly enhances both the positional accuracy and fuel efficiency of CubeSat maneuvers. Unlike traditional methods, which either rely on extensive pre-mission planning or computationally expensive control algorithms, our architecture efficiently balances fuel consumption with real-time adaptability, making it well-suited for the resource constraints of CubeSat platforms. The effectiveness of the proposed approach is evaluated through a series of simulations, including an orbit correction scenario and a Monte Carlo analysis. The results demonstrate that the integrated J2-GRU system significantly improves positional accuracy and reduces fuel consumption compared to traditional methods. Even under conditions of high uncertainty, the GRU-based control layer effectively compensates for errors in thrust direction, maintaining a low miss distance throughout the maneuvering period. Additionally, the GRU’s simpler architecture provides computational advantages over more complex models such as long short-term memory (LSTM) networks, making it more suitable for onboard CubeSat implementations.

The relationship between corporate culture of SMEs and crisis response ability under the cross-cultural background

This research explores the critical influence of corporate culture on small and medium-sized enterprises’ (SMEs) crisis response abilities under varied cross-cultural environments. Amid the disruptive backdrop of the COVID-19 pandemic, SMEs globally have faced unprecedented challenges. This study addresses a gap in the existing literature by conducting a cross-cultural analysis of SMEs in China, Thailand, and Germany to understand how corporate culture affects crisis management. Utilizing a competitive cultural value model, the research categorizes corporate culture into four dimensions: group culture, development culture, hierarchy culture, and rational culture. These cultural dimensions are investigated in relation to their impact on crisis response abilities. Additionally, national cultural dimensions such as individualism and uncertainty avoidance are examined as moderating variables. The findings reveal that group and development cultures positively influence crisis response abilities, enhancing organizational resilience and adaptability. Conversely, hierarchy culture negatively affects crisis management, hindering flexible response strategies. Rational culture supports structured crisis response through goal-oriented practices. National culture significantly moderates these relationships, with individualism and high uncertainty avoidance impacting the effectiveness of organizational cultural dimensions in crisis scenarios. This study offers theoretical advancements by integrating cultural dimensions with crisis response strategies and provides practical implications for SMEs striving to enhance their resilience and adaptability in a globalized business environment.

Quantile connectedness between social network sentiment and sustainability index volatility: Evidence from the Moroccan financial market

The goal of this paper is to investigate the connectedness between investor sentiment and volatility on the environmental, social, and governance index (ESG) in Morocco. Therefore, on the basis of an investor sentiment index constructed from the X platform, and using quantile and frequency connectedness approaches, the findings reveal a significant connectedness between sentiment and ESG volatility, particularly during turbulent events. Although sentiment acts most of the time as a net receiver of shocks, notably during the COVID-19 pandemic and geopolitical crises, it sometimes becomes a net sender of shocks. Furthermore, the spillovers between sentiment and ESG volatility are determined mainly by the long-term component, especially during extreme events, implying the persistence of shock transmission due to high uncertainty. The results also illustrate the impact of market conditions on the spillovers between sentiment and ESG volatility. The conclusions of this study provide useful guidance for pro-ESG investors, policymakers, and companies.

Innovative Approaches to Assessing the Competitiveness of Business Structures Using the Method of Analysis of Hierarchies in Multi-Criteria Ranking

The primary objective of this paper is the practical testing of a multi-criteria rating methodology for assessing the competitiveness of business structures. This aims to evaluate critical functional areas of their operations and justify specific managerial decisions to ensure sustainable competitiveness. Maintaining the competitiveness of business structures becomes a primary task in the context of high uncertainty and competition, as well as the presence of crises and politico-economic problems. To achieve the set objective, the Analytic Hierarchy Process (AHP), developed by T. Saaty, was utilized. This practical method allows for multi-criteria comparisons using expert evaluations and calculations of eigenvectors and values, providing systematization and quantitative assessment of criteria and alternatives' priorities. AHP includes procedures for synthesizing multiple judgments and obtaining priorities, allowing for consideration of the 'human factor' in decision-making preparation. The study results demonstrate that the multi-criteria approach to rating the competitiveness of business structures effectively accounts for all areas of their activities and forms an adequate comprehensive measure of competitiveness. Within the study's framework, three business structures with different performance trends were analyzed. The comparative importance of criteria and sub-criteria for competitiveness assessment was determined using the Analytic Hierarchy Process, considering the specifics of the business structures and their business activities. The proposed methodology for multi-criteria rating of business structures' competitiveness allows for a comprehensive assessment of their activities and justification of specific managerial decisions to ensure sustainable competitiveness. The use of the Analytic Hierarchy Process helps to consider the influence of various factors and conditions, contributing to a more objective assessment and strategic decision-making. The application of the proposed methodology is universal and can be effectively used in various sectors of business structures' activities, showcasing its adaptability.

Forest cover is more important than its integrity or landscape configuration in determining habitat use by mammals in a human-modified landscape in Colombia

Human activities shape the structure of landscapes in different ways and hence modify animal communities depending on the type and intensity of these activities. The Montes de Maria subregion of Colombia has experienced a heavy transformation of most areas despite covering one of the last remnants of dry forest, a critically endangered ecosystem. However, the effects of this transformation have been little explored. Here, we used a multispecies occupancy model (MSOM) to understand the relative influence of three components of land-use change – deforestation (remaining forest amount), degradation of forest integrity (forest quality) and fragmentation (landscape configuration) on mammalian habitat use across a mosaic of tropical dry forest in Colombia. Our data suggest that the percentage of forest cover was substantially important for herbivores, and consistently showed a moderate effect on the entire community and some individual species. High variability in species-specific responses to the examined variables hindered broad taxonomic generalizations, nevertheless, we detected a moderate positive effect of forest cover in both diet specialists and generalists species, as well as in species with small home ranges. Although omnivores responses, tended to use less complex landscapes (mosaics of land uses), there was high uncertainty in this response. The lack of substantial effects on most species, and the absence of threatened species across this anthropogenic landscape, suggests that the current community is composed of species tolerant to habitat modifications, but not only diet generalist species. This is most likely the result of a long filtering process caused by land use transformation and hunting which could have caused non-sensitive species to distribute relatively homogenously across this landscape. Our results suggest that conservation strategies in the study area should focus on conserving and expanding as much forest as possible rather than only improving the quality of already existing forest patches.

Characterizing chemical abundance ratios in extremely metal-poor star-forming galaxies in DESI EDR

We present a search for galaxies in the local Universe with extremely low oxygen abundance, that is, more than 25 times lower than solar, which corresponds to 12 + log(O/H) &lt; 7.3. To determine the oxygen abundance, we apply the direct Te method for objects where the [O III]λ4363 line is detected. We identified 21 extremely metal-poor galaxies in the early data release of the Dark Energy Spectroscopic Instrument (DESI EDR), for some of which we also derived N/O, Ne/O, Ar/O, and S/O ratios. We find that many DESI galaxies with extremely low oxygen abundance exhibit a higher N/O ratio in comparison to the reference low-metallicity sample collected from the literature. We suggest that the elevation in N/O ratio may be explained by a contamination with metal-rich gas caused by gas inflow or a merger event. Moreover, contrary to some recent studies, we find that Ar/O and S/O ratios are enhanced as well, while the Ne/O ratio does not show such elevation. One of the galaxies, J0713+5608, has a remarkably low oxygen abundance of 6.978 ± 0.095 dex. This measurement aligns with the lowest known oxygen abundances in galaxies to date. Given the relatively high uncertainty, this galaxy may have the lowest oxygen abundance ever found. Additionally, J0713+5608 exhibited an enhanced N/O ratio compared to the typical N/O ratio observed in metal-poor galaxies within the local Universe.

Current status and influencing factors of fear disease progression in Chinese primary brain tumor patients: A mixed methods study

ObjectiveIn this study, we investigated the fear of disease progression in Chinese PBT patients and examined the correlation between sociodemographic, clinical, and psychological variables of patients with the fear of progression (FoP). Additionally, the study also evaluated the subjective experience of FoP in patients with primary brain tumors (PBT). MethodsA mixed-methods study was conducted between March 2022 and December 2023, consisting of two phases: a quantitative approach in phase I, and a qualitative approach in phase II. In phase I, 305 patients with PBT filled in several questionnaires. An analysis was performed to identify potential predictors associated with FoP. In phase II, semi-structured interviews were conducted with 16 participants whose FoP scores were ≥ 34 in phase I to obtain information on their personal experiences with FoP. ResultsThe results of the quantitative study showed that 192 (63 %) patients experienced high levels of FoP. The mean score of fear of progression was (34.02±6.78). Young age, high disease uncertainty, low social support, high negative coping and low positive coping are important factors affecting FoP in PBT patients. Qualitative research focused on three themes: triggers, coping styles, and the help needed. ConclusionEnhanced screening and assessment of FoP is essential to identify dysfunctionin PBT. Meanwhile, the implications of these predictors for enhanced healthcare professional education and patient self-management may help healthcare providers implement relevant interventions promptly and help patients reduce their FoP. However, due to limitations such as sample, reporting bias, and specific mechanisms between predictors and FOPs that have not yet been explored in depth, further exploration is needed in the future.