Search Results (2,934)

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient’s health status over time. Methods: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient’s time-fixed covariates (patient and cancer data at diagnosis) but also a patient’s time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I–III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I–III BC, and each stage’s ER/PR+ or ER/PR− status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model’s utility. Main Outcomes and Measures: The primary outcomes of the modeling were the measures of the model’s prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model’s discrimination and calibration. Results: The proposed extended patients’ covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models’ prediction error and the discrimination and calibration of a model’s estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models’ predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient’s individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. Conclusions: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients’ survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making. Full article

In the extreme polar marine environment, safety risks pose a significant threat to drilling vessels. By conducting a safety risk assessment, potential hazards can be predicted and identified, thereby significantly reducing the frequency of accidents and promoting the sustained stability of economic activities. This paper investigates a Bayesian-network-based risk assessment model for polar drilling operations. Grey relational analysis was employed to identify the main risk factors. The model is trained using 525 valid incident sample data and is combined with expert knowledge. The accuracy rate is above 88%. Additionally, corresponding decision-making recommendations are provided through sensitivity analysis. The three most sensitive elements to fire nodes are human error, other causes, and equipment damage, with sensitivity coefficients of 0.046, 0.042, and 0.022, respectively. In terms of deck/handrail collision nodes, the highly sensitive elements are related to lifting (totally more than 0.1). For the events that have already transpired, the probabilities of most related nodes are 0.73 and 0.74, both of which are above 0.5, thereby validating the accuracy of forward and backward reasoning. Risk assessments based on Bayesian networks can offer pertinent decision-making recommendations and preventive measures. Full article

Management of reputational risk is crucial for financial institutions to establish a solid foundation for strategic decisions, gain customer trust, and enhance resilience against environmental adversities, as they largely operate on digital platforms. Since this becomes even more significant as online transactions and digital interactions amplify the visibility and potential impact of reputational issues in the context of electronic commerce, it is essential to thoroughly investigate environmental factors to achieve a comprehensive understanding of reputational risk. However, measuring and evaluating their influence on reputational risk is challenging due to their inherent connection to human perception. This study aims to explore the factors influencing reputational risk of financial organizations to mitigate potential reputational losses by addressing uncertainties associated with concepts such as vagueness. The employed methodology integrates the Decision-Making Trial and Evaluation Laboratory and Fuzzy Cognitive Map techniques using linguistic fuzzy terms. This approach focuses on both the direct effects of factors on reputational risk and the indirect effects arising from interdependencies between factors. Linguistic fuzzy variables enable us to consider the hesitation of the experts and the vagueness of human judgment. To validate the results, factors are also weighted using the fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method. The most influential factors identified by both methods are market value, revenue, risk culture, shareholder value, firm performance, reputation awareness, and return on equity. Additionally, factors affecting other factors include firm performance, revenue, and growth opportunities. Full article

This study explores the trust dynamics in financial forecasting by comparing how individuals perceive the credibility of AI and human experts during significant structural market changes. We specifically examine the impact of two types of structural breaks on trust: Additive Outliers, which represent a single yet significant anomaly, and Level Shifts, which indicate a sustained change in data patterns. Grounded in theoretical frameworks such as attribution theory, algorithm aversion, and the Technology Acceptance Model (TAM), this research investigates psychological responses to AI and human advice under uncertainty. This experiment involved 157 participants, recruited via Amazon Mechanical Turk (MTurk), who were asked to forecast stock prices under different structural break scenarios. Participants were randomly assigned to either the AI or human expert treatment group, and the experiment was conducted online. Through this controlled experiment, we find that, while initial trust levels in AI and human experts are comparable, the credibility of advice is more severely compromised following a structural break in the Level Shift condition, compared to the Additive Outlier condition. Moreover, the decline in trust is more pronounced for human experts than for AI. These findings highlight the psychological factors influencing decision making under uncertainty and offer insights into the behavioral responses to AI and human expert systems during structural market changes. Full article

Under the impact of global climate change and human activities, the occurrence of compound disasters such as cascading landslides and flash floods caused by heavy rainfall is increasing. In response to these compound disaster events, it is important to simultaneously transport emergency resources from multiple emergency rescue points to the disaster sites to promptly control the cascading development of disasters and reduce the areas affected by the disasters and associated adverse impacts. This study proposes a dynamic game model for emergency resources dispatch to comprehensively consider the evolution of the compound disaster states and the timely dispatch of emergency resources from the rescue points to the disaster site. The dynamic game model is exemplarily applied to the emergency resource dispatch for a rainstorm-induced compound disaster that occurs in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Starting with the analysis of the characteristics of emergency resource management and the attributes of a cascading of heavy rainstorms, landslides, and flash floods, the game model simulates the dynamic game process between the “disaster state” and the “emergency resource manager” in the rescue operations. A two-stage dynamic game model can support decision-making with the objectives of minimal time cost and sufficient resource dispatch for the disaster sites. Game results show that the united emergency resource dispatch in the three GBA metropolitan areas can efficiently respond to compound disasters that occur within the GBA metropolitan area. The dynamic game model could be extended for compound disaster emergency responses with more complicated compound effects and resource constraints. Full article

The objective of this research is to evaluate four distinct models for multi-population mortality projection in order to ascertain the most effective approach for forecasting the impact of the COVID-19 pandemic on mortality. Utilizing data from the Human Mortality Database for five countries—Finland, Germany, Italy, the Netherlands, and the United States—the study identifies the generalized additive model (GAM) within the age–period–cohort (APC) analytical framework as the most promising for precise mortality forecasts. Consequently, this model serves as the basis for projecting the impact of the COVID-19 pandemic on future mortality rates. By examining various pandemic scenarios, ranging from mild to severe, the study concludes that projections assuming a diminishing impact of the pandemic over time are most consistent, especially for middle-aged and elderly populations. Projections derived from the superior GAM-APC model offer guidance for strategic planning and decision-making within sectors facing the challenges posed by extreme historical mortality events and uncertain future mortality trajectories. Full article

Background: Neurosurgery demands exceptional precision due to the brain’s complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon’s ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Methods: Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Results: Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. Conclusions: This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS’s clinical utility and cost-effectiveness. Full article

Recognizing the critical role of oil and gas resources as strategic assets and acknowledging the increasing emphasis on green development and social responsibility driven by international energy agreements and the “dual carbon” strategy, this study addresses the urgent need for a robust employee evaluation framework within China’s petroleum sector. While existing systems often prioritize competence-based indicators, they frequently overlook the crucial aspect of employee Moral Quotient (MQ). This research focuses on developing and validating a holistic, scientifically grounded, multi-dimensional, and dynamic MQ evaluation index system tailored specifically for Chinese petroleum enterprises. The study employed a mixed-methods approach, integrating a comprehensive literature review, semi-structured interviews with industry experts, and robust quantitative techniques, including the Analytic Hierarchy Process (AHP) and the entropy weight method. A two-round Delphi study, involving 18 experts from six provinces across China, ensured broad representation and facilitated the construction of the evaluation index system and the determination of indicator weights. The Delphi process achieved a high degree of expert consensus, evidenced by a 100% questionnaire response rate in both rounds, expert authority coefficients of 0.851 and 0.879, respectively, and Kendall’s coefficient of concordance of 0.243 and 0.247 (p < 0.01), respectively, demonstrating strong reliability and scientific validity. The resulting MQ evaluation index system comprises eight first-level indicators and thirty-three s-level indicators, encompassing key dimensions of employee morality relevant to the petroleum industry. This comprehensive system provides a robust and objective tool for employee selection, training, performance evaluation, and career development within Chinese petroleum enterprises, supporting informed decision-making in human resource management and fostering a culture of ethical conduct and sustainable development. Furthermore, the developed framework offers valuable insights and serves as a potential model for petroleum enterprises and other resource-intensive industries globally seeking to integrate MQ assessment into their human capital management strategies. Full article

Western Monarch butterflies (Danaus plexippus) migrate from inland breeding ranges to coastal overwintering grounds in California. Given that migratory individuals may make multi-scale habitat selection decisions, we considered a multi-scale species distribution model (SDM) using range-wide climatic and local landscape-level predictors of migratory and overwintering habitat and community-science presence data. The range-wide model output was included as a predictor in the local-scale model, generating multi-scale habitat suitability. The top range-wide predictor was the minimum temperature in December, contributing 83.7% to the model, and was positively associated with presence. At the local scale, the strongest predictors of presence were the range-wide output and percent coverage of low and medium levels of development, contributing > 95%, with 61–63% from the range-wide output, with local-scale suitability coinciding with the California coastal zones. Development’s positive association with overwintering monarch presence was counterintuitive. It is likely that our local-scale model is overfit to these development zones, but it is unclear whether this overfitting resulted from modeler choices, monarchs overwintering close to human development, biased detection near human development, or a combination of these factors. Therefore, alternative approaches to collecting local-scale attribute data are suggested while recognizing the primacy of climate in restricting overwinter sites. Full article

In the construction of water conveyance tunnels with the shield method, accidents have occurred from time to time, such as collapses and explosions, and it is of practical significance to explore the cause mechanism of the accident. However, previous research has not considered the effects of dependence between risks on the risk spread. In response, we propose a method based on the Natural Killing Model (the N-K Model) and complex network theory to analyze the cause of shield construction accidents in water conveyance tunnels. By deeply exploring the transmission mechanism and action intensity between system risks, this method can scientifically clarify the accident cause mechanism and provide support for engineering construction safety management. The method constructs a risk index system. Secondly, we introduce the N-K model to reveal the risk coupling mechanism. Then, based on complex network theory, we construct the incident causation model and revise the node’s centrality with the coupling value. Finally, the network topology parameters are calculated to quantitatively describe the causal characteristics of accidents, revealing the risk evolution process and critical causes. The research results indicate that the key causes of accidents are failure to construct according to regulations, inadequate emergency measures, poor ability of judgment and decision-making, and insufficient understanding of abnormal situations. The front end of critical links is subject to human or management risks and should be carefully controlled during construction. Full article

Today, historic villages represent a widespread and relevant reality of the Italian administrative structure. To preserve their value for future generations, smart city applications can contribute to implement effective monitoring and decision-making processes devoted to safeguarding their fragile ecosystem. Starting from a situational awareness model, this study proposes a method for designing human-in-the-loop cyber-physical systems that allow the design of monitoring and decision-making applications for historic villages. Both the model and the design method can be used as a reference for the realization of human-in-the-loop cyber-physical systems that consist of human beings, smart objects, edge devices, and cloud components in edge-cloud architectures. The output of the research, consisting of the graphical models for the definition of monitoring architectures and the method for the design of human-in-the-loop cyber-physical systems, was validated in the context of the village of Sant’Agata dei Goti through the implementation of a human-in-the-loop cyber-physical system for monitoring sites aiming at their management, conservation, protection, and fruition. Full article

This article explores the specific role of courage in the context of the Anthropocene’s moment; it first examines Aristotle’s conception of virtues, focusing on courage, before comparing it to Confucian thought and analyzing the historical dialogue between Western and Chinese traditions on ethics through the works of François Noël (1651–1729). Aristotle views moral cultivation as a social process wherein habits shape inner dispositions; in his view, courage is linked to other virtues, such as temperance and justice. For Aristotle, courage implies the appropriate balance between extremes and must be directed toward a worthy end, such as promoting positive change within a community. This Aristotelian perspective was later incorporated into a biblical framework by Aquinas and Suarez, emphasizing dichotomies between body and soul, as well as between humans and other living beings. These dichotomies must be challenged in the face of the Anthropocene’s emergencies. The second part of this contribution proceeds to a detour examining Confucian ethics, which rests on a different anthropology, emphasizing continuities rather than discontinuities. Like Aristotelian thought, Confucian thought also underscores moral education within a community; it prioritizes humanity, embodied through empathy and loyalty. In the Analects, courage is balanced by a sense of rituals and righteousness. Mencius further distinguishes several types of courage, stressing self-cultivation and the ruler’s responsibility to make empathetic, appropriate decisions for the community’s sake. From this perspective, courage is understood as the continuous perseverance in self-cultivation, coupled with a firm intention oriented toward the good of the community. Zhu Xi’s comments on Zilu’s courage in the Analects extend this Confucian tradition. Finally, this article highlights how a dialogue between Aristotelian and Confucian ethics began four centuries ago, particularly through Noël’s Philosophia Sinica, which combined these traditions. This inter-religious approach to ethics, enriched by figures such as Aquinas, Suarez, Zhu Xi, and neo-Confucian thinkers, requires re-evaluation because the understanding of personal ethics and nature has evolved. The modern naturalistic approach, with its emphasis on dichotomies, has contributed to a mechanistic view of nature, fostering its exploitation, and a devaluation of the body. This contrast highlights the urgent need for renewed dialogue between Western and Chinese ethical traditions to address contemporary challenges, with François Noël serving as a historical witness of these exchanges. Full article

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes. Full article

World’s Best Practice management of locusts and grasshoppers requires accurate forecasting that helps determine where and when surveys are preferentially conducted so that infestations can be found quickly as part of ensuring early intervention treatments. Using survey data downloaded directly into a Geographic Information System (GIS), as well as rainfall and other factors important in the population dynamics of the species concerned, models within the GIS provide forecasts of future developments. The GIS provides forecasts of likely events and is used by locust and grasshopper experts to make decisions; that is, the forecasting is part of a Decision Support System for improved locust and grasshopper management. Surveys are generally conducted by ground vehicles, but for locusts, surveys by aircraft can be an important way to rapidly find bands. In Australia, dense bands can often be seen from an aircraft flying overhead at a height of 300 m, and similar detection of bands of the desert locust by aircraft has been conducted in Somalia. Swarms can be detected by ground vehicles, but because swarms move, surveying by aircraft is also an important way of locating swarms for treatment. When locust infestations are found, they are rapidly treated as part of early intervention preventive management. However, it is generally recognized that it is extremely difficult for landholders alone to protect crops against locusts and grasshoppers, so government intervention is often necessary. These organizations use a variety of treatment techniques to reduce the amount of chemical pesticide applied either by strip spraying or treating very dense infestations, such as roosting swarms, or using biopesticides. These techniques, as used in a number of countries, have proven to be very effective in managing locust populations while reducing the risk to the natural environment and human health. Full article

Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages. Full article