Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the opera...Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.展开更多
LargeLanguageModels(LLMs)are increasingly appliedinthe fieldof code translation.However,existing evaluation methodologies suffer from two major limitations:(1)the high overlap between test data and pretraining corpora...LargeLanguageModels(LLMs)are increasingly appliedinthe fieldof code translation.However,existing evaluation methodologies suffer from two major limitations:(1)the high overlap between test data and pretraining corpora,which introduces significant bias in performance evaluation;and(2)mainstream metrics focus primarily on surface-level accuracy,failing to uncover the underlying factors that constrain model capabilities.To address these issues,this paper presents TCode(Translation-Oriented Code Evaluation benchmark)—a complexity-controllable,contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework.The dataset is carefully designed to control complexity along multiple dimensions—including syntactic nesting and expression intricacy—enabling both broad coverage and fine-grained differentiation of sample difficulty.This design supports precise evaluation of model capabilities across a wide spectrum of translation challenges.The proposed evaluation framework introduces a correlation-driven analysis mechanism based on static program features,enabling predictive modeling of translation success from two perspectives:Code Form Complexity(e.g.,code length and character density)and Semantic Modeling Complexity(e.g.,syntactic depth,control-flow nesting,and type system complexity).Empirical evaluations across representative LLMs—including Qwen2.5-72B and Llama3.3-70B—demonstrate that even state-of-the-art models achieve over 80% compilation success on simple samples,but their accuracy drops sharply below 40% on complex cases.Further correlation analysis indicates that Semantic Modeling Complexity alone is correlated with up to 60% of the variance in translation success,with static program features exhibiting nonlinear threshold effects that highlight clear capability boundaries.This study departs fromthe traditional accuracy-centric evaluation paradigm and,for the first time,systematically characterizes the capabilities of large languagemodels in translation tasks through the lens of programstatic features.The findings provide actionable insights for model refinement and training strategy development.展开更多
The current global attempts to control the so-called“Neglected Tropical Diseases(NTDs)”have the potential to significantly reduce the morbidity suffered by some of the world’s poorest communities.However,the govern...The current global attempts to control the so-called“Neglected Tropical Diseases(NTDs)”have the potential to significantly reduce the morbidity suffered by some of the world’s poorest communities.However,the governance of these control programmes is driven by a managerial rationality that assumes predictability of proposed interventions,and which thus primarily seeks to improve the cost-effectiveness of implementation by measuring performance in terms of pre-determined outputs.Here,we argue that this approach has reinforced the narrow normal-science model for controlling parasitic diseases,and in doing so fails to address the complex dynamics,uncertainty and socio-ecological context-specificity that invariably underlie parasite transmission.We suggest that a new governance approach is required that draws on a combination of non-equilibrium thinking about the operation of complex,adaptive,systems from the natural sciences and constructivist social science perspectives that view the accumulation of scientific knowledge as contingent on historical interests and norms,if more effective control approaches sufficiently sensitive to local disease contexts are to be devised,applied and managed.At the core of this approach is an emphasis on the need for a process that assists with the inclusion of diverse perspectives,social learning and deliberation,and a reflexive approach to addressing system complexity and incertitude,while balancing this flexibility with stability-focused structures.We derive and discuss a possible governance framework and outline an organizational structure that could be used to effectively deal with the complexity of accomplishing global NTD control.We also point to examples of complexity-based management structures that have been used in parasite control previously,which could serve as practical templates for developing similar governance structures to better manage global NTD control.Our results hold important wider implications for global health policy aiming to effectively control and eradicate parasitic diseases across the world.展开更多
基金supported by the National Natural Science Foundation of China (Nos.U1833103, 71801215, U1933103)the Fundamental Research Funds for the Central Universities (No.3122019129)。
文摘Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.
文摘LargeLanguageModels(LLMs)are increasingly appliedinthe fieldof code translation.However,existing evaluation methodologies suffer from two major limitations:(1)the high overlap between test data and pretraining corpora,which introduces significant bias in performance evaluation;and(2)mainstream metrics focus primarily on surface-level accuracy,failing to uncover the underlying factors that constrain model capabilities.To address these issues,this paper presents TCode(Translation-Oriented Code Evaluation benchmark)—a complexity-controllable,contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework.The dataset is carefully designed to control complexity along multiple dimensions—including syntactic nesting and expression intricacy—enabling both broad coverage and fine-grained differentiation of sample difficulty.This design supports precise evaluation of model capabilities across a wide spectrum of translation challenges.The proposed evaluation framework introduces a correlation-driven analysis mechanism based on static program features,enabling predictive modeling of translation success from two perspectives:Code Form Complexity(e.g.,code length and character density)and Semantic Modeling Complexity(e.g.,syntactic depth,control-flow nesting,and type system complexity).Empirical evaluations across representative LLMs—including Qwen2.5-72B and Llama3.3-70B—demonstrate that even state-of-the-art models achieve over 80% compilation success on simple samples,but their accuracy drops sharply below 40% on complex cases.Further correlation analysis indicates that Semantic Modeling Complexity alone is correlated with up to 60% of the variance in translation success,with static program features exhibiting nonlinear threshold effects that highlight clear capability boundaries.This study departs fromthe traditional accuracy-centric evaluation paradigm and,for the first time,systematically characterizes the capabilities of large languagemodels in translation tasks through the lens of programstatic features.The findings provide actionable insights for model refinement and training strategy development.
基金E.M.acknowledges the financial support of the Eck Institute for Global HealthUniversity of Notre Dame,while S.M.thanks the British Academy for supporting the undertaking of this work。
文摘The current global attempts to control the so-called“Neglected Tropical Diseases(NTDs)”have the potential to significantly reduce the morbidity suffered by some of the world’s poorest communities.However,the governance of these control programmes is driven by a managerial rationality that assumes predictability of proposed interventions,and which thus primarily seeks to improve the cost-effectiveness of implementation by measuring performance in terms of pre-determined outputs.Here,we argue that this approach has reinforced the narrow normal-science model for controlling parasitic diseases,and in doing so fails to address the complex dynamics,uncertainty and socio-ecological context-specificity that invariably underlie parasite transmission.We suggest that a new governance approach is required that draws on a combination of non-equilibrium thinking about the operation of complex,adaptive,systems from the natural sciences and constructivist social science perspectives that view the accumulation of scientific knowledge as contingent on historical interests and norms,if more effective control approaches sufficiently sensitive to local disease contexts are to be devised,applied and managed.At the core of this approach is an emphasis on the need for a process that assists with the inclusion of diverse perspectives,social learning and deliberation,and a reflexive approach to addressing system complexity and incertitude,while balancing this flexibility with stability-focused structures.We derive and discuss a possible governance framework and outline an organizational structure that could be used to effectively deal with the complexity of accomplishing global NTD control.We also point to examples of complexity-based management structures that have been used in parasite control previously,which could serve as practical templates for developing similar governance structures to better manage global NTD control.Our results hold important wider implications for global health policy aiming to effectively control and eradicate parasitic diseases across the world.
