En-route Sector Complexity Control Strategies in Air Traffic Management

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.

Socio-ecological dynamics and challenges to the governance of Neglected Tropical Disease control

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.